By MATTHEW HOLT

Okay, I can’t do it any longer. As much as I tried to resist, it is time to write about ambient scribing. But I’m going to do it in a slightly odd way

If you have met me, you know that I have a strange English-American accent, and I speak in a garbled manner. Yet I’m using the inbuilt voice recognition that Google supplies to write this story now.

Side note: I dictated this whole thing on my phone while watching my kids water polo game, which has a fair amount of background noise. And I think you’ll be modestly amused about how terrible the original transcript was. But then I put that entire mess of a text  into ChatGPT and told it to fix the mistakes. it did an incredible job and the output required surprisingly little editing.

Now, it’s not perfect, but it’s a lot better than it used to be, and that is due to a couple of things. One is the vast improvement in acoustic recording, and the second is the combination of Natural Language Processing and artificial intelligence.

Which brings us to ambient listening now. It’s very common in all the applications we use in business, like Zoom and others like transcript creation from videos on Youtube. Of course, we have had something similar in the medical business for many years, particularly in terms of radiology and voice recognition. It has only been in the last few years that transcribing the toughest job of all–the clinical encounter–has gotten easier.

The problem is that doctors and other professionals are forced to write up the notes and history of all that has happened with their patients. The introduction of electronic medical records made this a major pain point. Doctors used to take notes mostly in shorthand, leaving the abstraction of these notes for coding and billing purposes to be done by some poor sap in the basement of the hospital.

Alternatively in the past, doctors used to dictate and then send tapes or voice files off to parts unknown, but then would have to get those notes back and put them into the record. Since the 2010s, when most American health care moved towards using  electronic records, most clinicians have had to type their notes. And this was a big problem for many of them. It has led to a lot of grumpy doctors not only typing in the exam room and ignoring their patients, but also having to type up their notes later in the day. And of course, that’s a major contributor to burnout.

To some extent, the issue of having to type has been mitigated by medical scribes–actual human beings wandering around behind doctors pushing a laptop on wheels and typing up everything that was said by doctors and their patients. And there have been other experiments. Augmedix started off using Google Glass, allowing scribes in remote locations like Bangladesh to listen and type directly into the EMR.

But the real breakthrough has been in the last few years. Companies like Suki, Abridge, and the late Robin started to promise doctors that they could capture the ambient conversation and turn it into proper SOAP notes. The biggest splash was made by the biggest dictation company, Nuance, which in the middle of this transformation got bought by one of the tech titans, Microsoft. Six years ago, they had a demonstration at HIMSS showing that ambient scribing technology was viable. I attended it, and I’m pretty sure that it was faked. Five years ago, I also used Abridge’s tool to try to capture a conversation I had with my doctor — at that time, they were offering a consumer-facing tool – and it was pretty dreadful.

Fast forward to today, and there are a bunch of companies with what seem to be really very good products.

Nuance’s DAX is in relatively wide use. Abridge has refocused itself on clinicians and has excellent reviews, (you can see my interview and demo with CEO Shiv Rao here) and Nabla has just published a really compelling review from its first big rollout with Kaiser Permanente, Northern California in the NEJM no less. (FD I am an advisor to Nabla although not involved in its KP work). And others like DeepScribe, Ambience, Augmedix and even newcomers Innovaccer and Sudoh.ai seem to be good options.

If you take a look at the results of the NEJM published study that was done in Northern California using Nabla’s tool, you’ll see that clinicians have adopted that very quickly, with high marks for both its accuracy, and the ability to deliver a SOAP note and patient summary very quickly. And it has returned a lot of time to the clinician’s day. (Worth noting that independent practice Carbon Health has built its own inhouse ambient scribe and used it on 500K visits so far)

The big gorilla on the EMR side, Epic, has integrated to some extent with Nuance and Abridge, but many of the other companies are both working to integrate with Epic and are inside other EMR competitors – for instance Nextgen is private-labeling Nabla. At the moment, for basically everyone integration really just means getting the note summary into the notes section of the EMR.

But there is definitely more to come. For many years, NLP companies like Apixio, Talix, Health Equity and more (all seemingly bought by Edifecs) have been working on EMR notes to aid coders in billing, and it’s an easy leap to assume that will happen more and more with ambient scribing. And of course, the same thing is going to be true for clinical decision support and pretty soon integration with orders and workflow. In other words, when a doctor says to a patient, “We are going to start you on this new drug,” not only will it appear in the SOAP note, but the prescription or the lab order will just be magically done.

But is it reasonable to suppose that we are just paving the cowpath here? Ambient scribing is just making the physician office visit data more accessible. It’s not making it go away, which is what we should be trying to do. But I can’t blame the ambient scribing companies for that. And as I have (at length!) pointed out, we are still stuck in a fee-for-transaction system in which the health services operators in this country make money by doing stuff, writing it up, and charging for it. That is not going away anytime soon.

But given that’s where we are, I think we can still see how the ambient scribing battle will play out. 

Nuance’s DAX has the advantage of a huge client base, but frankly, Nuance has not been an innovative company. One former employee told me that they have never invented anything. And indeed, the DAX system was massively enhanced by the tech Nuance acquired when purchasing a company called Saykara in 2021, some years after that unconvincing demo back at HIMSS 2018.

So innovation matters, but the other issue is the cost of ambient scribing, which in some cases is nearing the cost of a real scribe. Nuance’s DAX, Suki, and even new entries like Sunoh seem to be around the $400 to $600 a month per physician level. Sunoh is offered by eClinicalworks and has some co-ownership with that EMR vendor. What’s amazing is that at the price quoted at HIMSS of $1.25 per encounter the ambient scribing tool would cost a busy family practice doc seeing 25 patients a day as much as the EMR subscription, around $600 a month.

Abridge has been quoted at roughly $250 a month, and Nabla seems to be considerably less expensive, around $120. But realistically, the whole market will have to compress to about that level because the switching costs are going to be very trivial. Right now, with most of them requiring a paste and copy into the EMR, it’s almost zero.

Which then leads to some more technical issues. How good will these systems become? (Noting that they are already very good, according to reviews on the Elion site). And what will happen to the way they store data. Most of them are currently moving the data back to their cloud for processing. But this may not be acceptable for health systems that like to keep data within their firewalls. For what it’s worth, Nabla, being from the EU and very conscious of GDPR, has been pushing the fact that its process stays on the physician’s local machine – although I’m not sure how much difference that makes in the market.

The other technical issue is the reliance on the large LLMs like OpenAI, Google, etc., compared to companies that are using their own LLM. Again, this may just remain a technical issue that no one cares much about. On the other hand, accuracy and lack of anonymization will continue to be a big issue if more generic LLMs are used. Now the fascination with the initial ChatGPT type LLM is wearing off, there’s going to be a lot more concern about how AI is using health care as a whole–particularly its tendency to “hallucinate” or get stuff wrong. That will obviously impact ambient scribing, even if mistakes may not be as serious as perhaps patient diagnosis or treatment suggestions.

So it’s too early to know exactly how this plays out, but it’s not much too early. In some ways, it’s very refreshing to see the speed at which this new technology is being adopted. As it is, the number of American doctors using ambient scribing is probably below 10%. But it’s highly likely that number goes up to 70%+ in very short order.

The problem that it is fixing for doctors is one that has been around for thousands of years and also one that has been particularly acute for the last twenty years or so. It’s almost like we’re in a period where the doctor suffering with having to  type up their notes in Epic–written up so eloquently by Bob Wachter in his book, “The Digital Doctor,”– is going to be a historical artifact that lasted for fifteen years or so. Maybe it’s going to be talked about nostalgically, like those of us who reminisce about having to get online with dial-up modems.

I’m pretty sure that the winners will be apparent in a couple of years, and that somebody, possibly Microsoft, or possibly the investors in big rounds at 2021 style valuations for Abridge or Ambience, may be regretting what happened in a couple of years. Alternatively, one of them may be a monopoly winner that soon starts printing money.

I suspect, though, that ambient scribing will essentially become a close-to-free product for all different types of business and that clinical care will not be much of an exception. That suggests that a company like Anthropic or OpenAI with close connections to the tech titans, Amazon and Microsoft, will end up becoming more of a feature for the tech giants. My guess is that they will be delivering that product for free probably also into much of clinical care, including ambient scribing. Of course, Epic may decide that it wants to do the same thing, which may leave its partners including Microsoft in the lurch.

It’s reasonable to expect that all aspects of life, including education, general business, consumer activity, and more, will find note-taking, summaries, and decision support a natural part of the next round of computing. For instance, anyone who has had a conversation with their contractor when renovating a house would probably love to have the notes, to-dos and agreements automatically recorded. It’ll be a whole new way of “keeping people honest”. Same thing for health care, I suspect.

But to be fair, we are not there yet. My dictation tool took this whole thing while watching a water polo game on Sunday. And I think you’ll be modestly amused about how terrible the original transcript was. But then I put that entire mess of a text  into ChatGPT and told it to fix the mistakes. it did an incredible job and the output required surprisingly little editing.

AI is getting very smart at working on incomplete information, and health care (as well as clinicians and patients) will benefit.

Matthew Holt is the publisher of The Health Care Blog and one upon a time ran the Health 2.0 Conference

By MIKE MAGEE

For most loyalist Americans at the turn of the 19th century, Justice John Marshall Harlan’s decision in Jacobson v. Massachusetts (1905). was a “slam dunk.” In it, he elected to force a reluctant Methodist minister in Massachusetts to undergo Smallpox vaccination during a regional epidemic or pay a fine.

Justice Harlan wrote at the time: “Real liberty for all could not exist under the operation of a principle which recognizes the right of each individual person to use his own, whether in respect of his person or his property, regardless of the injury that may be done to others.”

What could possibly go wrong here? Of course, citizens had not fully considered the “unintended consequences,” let alone the presence of President Wilson and others focused on “strengthening the American stock.”

This involved a two-prong attack on “the enemy without” and “the enemy within.”

The The Immigration Act of 1924, signed by President Calvin Coolidge, was the culmination of an attack on “the enemy without.” Quotas for immigration were set according to the 1890 Census which had the effect of advantaging the selective influx of Anglo-Saxons over Eastern Europeans and Italians. Asians (except Japanese and Filipinos) were banned.

As for “the enemy within,” rooters for the cause of weeding out “undesirable human traits” from the American populace had the firm support of premier academics from almost every elite university across the nation. This came in the form of new departments focused on advancing the “Eugenics Movement,” an excessively discriminatory, quasi-academic approach based on the work of Francis Galton, cousin of Charles Darwin.

Isolationists and Segregationists picked up the thread and ran with it focused on vulnerable members of the community labeled as paupers, mentally disabled, dwarfs, promiscuous or criminal.

In a strategy eerily reminiscent of that employed by Mississippi Pro-Life advocates in Dobbs v. Jackson Women’s Health Organization in 2021, Dr. Albert Priddy, activist director of the Virginia State Colony for Epileptics and Feebleminded, teamed up with radical Virginia state senator Aubrey Strode to hand pick and literally make a “federal case” out of a young institutionalized teen resident named Carrie Buck.

Their goal was to force the nation’s highest courts to sanction state sponsored mandated sterilization.

In a strange twist of fate, the Dobbs name was central to this case as well.

That is because Carrie Buck was under the care of foster parents, John and Alice Dobbs, after Carrie’s mother, Emma, was declared mentally incompetent. At the age of 17, Carrie, after having been removed from school after the 6th grade to work as a domestic for the Dobbs, was raped by their nephew and gave birth to a daughter, Vivian. This lead to her mandated institutionalization, and subsequent official labeling as an “imbecile.”

In his majority decision supporting Dr. Priddy, Buck v. Bell,  Supreme Court Chief Justice Oliver Wendall Holmes leaned heavily on precedent. Reflecting his extreme bias, he wrote: “The principle that supports compulsory vaccination is broad enough to cover the cutting of Fallopian tubes (Jacobson v. Massachusetts 197 US 11). Three generation of imbeciles are enough.”

Carrie Buck lived to age 76, had no mental illness, and read the Charlottesville, VA newspaper every day, cover to cover. There is no evidence that her mother Emma was mentally incompetent. Her daughter Vivian was an honor student, who died in the custody of the John and Alice Dobbs at the age of 8.

The deeply embedded roots of the prejudicial idea that inferiority is a biological construct was used to justify indentured servitude and enslaved Africans traces back to our very beginnings as a nation. Our third president, Thomas Jefferson, was not shy in declaring that his enslaved Africans were biologically distinguishable from land-holding whites. Channeling Eugenic activists a century later, the President noted his enslaved Africans suitability for brutal labor was based on their greater physiologic tolerance for plantation-level heat exposure, and lesser (required) kidney output.

Helen Burstin MD, CEO of the Council of Medical Specialty Societies, drew a direct line from those early days to the present day practice of medicine anchored in opaque decision support computerized algorithms. “It is mind-blowing in some ways how deeply embedded in history some of this misinformation is,” she said. She was talking about risk-prediction tools that are commercial and proprietary, and utilized for opague oversight of “roughly 200 million U.S. citizens per year.” Originally designed for health insurance prior approval systems and managed care decisions, they now provide underpinning for new AI super-charged personalized medicine decision support systems.

Documented misinformed and racially constructed clinical guidelines have been uncovered and rewritten over the past few years. They include obstetrical guidelines that disadvantaged black mothers seeking vaginal birth over Caesarian Section, and limitations on treatment of black children with fever and acute urinary tract infection, as just two examples. Other studies uncovered reinforcement of myths that “black people have higher pain thresholds,” greater strength, and resistance to disease – all in support of their original usefulness as slave laborers.

Can’t we just make a fresh start on clinical guidelines? Sadly, it is not that easy. As James Baldwin famously wrote, “People are trapped in history and history is trapped in them.” The explosion of technologic advance in health care has the potential to trap the bad with the good, as vast databases are fed into hungry machines indiscriminately.

Computing power, genomic databases, EMR’s, natural language processing, machine based learning, generative AI, and massive multimodal downloads bury our historic biases and errors under multi-layered camouflage. Modern day Dobb-ists have now targeted vulnerable women and children using carefully constructed legal cases and running them all the way up to the Supreme Court. This strategy was joined with a second (MAGA Republican take-over’s of state legislatures) to ban abortion, explore contraceptive restrictions, and eliminate fertility therapy. It is one more simple step to require encodement of these restrictions on medical freedom and autonomy into binding clinical protocols.

In an age where local bureaucrats are determined to “play doctor”, and modern day jurists are determined to provide cover for a third wave of protocol encoded Dobb-ists, “the enemy without” runs the risk of becoming “the enemy within.”

Mike Magee MD is a Medical Historian and regular contributor to THCB. He is the author of CODE BLUE: Inside America’s Medical Industrial Complex (Grove/2020).

Over the past year, the general popularization of AI orArtificial Intelligence has captured the world’s imagination. Of course, academicians often emphasize historical context. But entrepreneurs tend to agree with Thomas Jefferson who said, “I like dreams of the future better than the history of the past.”

This particular dream however is all about language, its standing and significance in human society. Throughout history, language has been a species accelerant, a secret power that has allowed us to dominate and rise quickly (for better or worse) to the position of “masters of the universe.”

Well before ChatGPT became a household phrase, there was LDT or the laryngeal descent theory. It professed that humans unique capacity for speech was the result of a voice box, or larynx, that is lower in the throat than other primates. This permitted the “throat shape, and motor control” to produce vowels that are the cornerstone of human speech. Speech – and therefore language arrival – was pegged to anatomical evolutionary changes dated at between 200,000 and 300,000 years ago.

That theory, as it turns out, had very little scientific evidence. And in 2019, a landmark study set about pushing the date of primate vocalization back to at least 3 to 5 million years ago. As scientists summarized it in three points: “First, even among primates, laryngeal descent is not uniquely human. Second, laryngeal descent is not required to produce contrasting formant patterns in vocalizations. Third, living nonhuman primates produce vocalizations with contrasting formant patterns.”

Language and speech in the academic world are complex fields that go beyond paleoanthropology and primatology. If you want to study speech science, you better have a working knowledge of “phonetics, anatomy, acoustics and human development” say the  experts. You could add to this “syntax, lexicon, gesture, phonological representations, syllabic organization, speech perception, and neuromuscular control.”

Professor Paul Pettitt, who makes a living at the University of Oxford interpreting ancient rock paintings in Africa and beyond, sees the birth of civilization in multimodal language terms. He says, “There is now a great deal of support for the notion that symbolic creativity was part of our cognitive repertoire as we began dispersing from Africa.  Google chair, Sundar Pichai, maintains a similarly expansive view when it comes to language. In his December 6, 2023, introduction of their ground breaking LLM (large language model), Gemini (a competitor of ChatGPT), he described the new product as “our largest and most capable AI model with natural image, audio and video understanding and mathematical reasoning.”

Digital Cognitive Strategist, Mark Minevich, echoed Google’s view that the torch of human language had now gone well beyond text alone and had been passed to machines. His review: “Gemini combines data types like never before to unlock new possibilities in machine learning… Its multimodal nature builds on, yet goes far beyond, predecessors like GPT-3.5 and GPT-4 in its ability to understand our complex world dynamically.”

GPT what???

O.K. Let’s take a step back, and give us all a chance to catch-up.

What we call AI or “artificial intelligence” is a 70-year old concept that used to be called “deep learning.” This was the brain construct of University of Chicago research scientists Warren McCullough and Walter Pitts, who developed the concept of “neural nets” in 1944, modeling the theoretical machine learner after human brains, consistent of multiple overlapping transit fibers, joined at synaptic nodes which, with adequate stimulus could allow gathered information to pass on to the next fiber down the line.

On the strength of that concept, the two moved to MIT in 1952 and launched the Cognitive Science Department uniting computer scientists and neuroscientists. In the meantime, Frank Rosenblatt, a Cornell psychologist, invented the “first trainable neural network” in 1957 termed by him futuristically, the “Perceptron” which included a data input layer, a sandwich layer that could adjust information packets with “weights” and “firing thresholds”, and a third output layer to allow data that met the threshold criteria to pass down the line.

Back at MIT, the Cognitive Science Department was in the process of being hijacked in 1969 by mathematicians Marvin Minsky and Seymour Papert, and became the MIT Artificial Intelligence Laboratory. They summarily trashed Rosenblatt’s Perceptron machine believing it to be underpowered and inefficient in delivering the most basic computations. By 1980, the department was ready to deliver a “never mind,” as computing power grew and algorithms for encoding thresholds and weights at neural nodes became efficient and practical.

The computing leap, experts now agree, came “courtesy of the computer-game industry” whose “graphics processing unit” (GPU), which housed thousands of processing cores on a single chip, was effectively the neural net that McCullough and Pitts had envisioned. By 1977, Atari had developed game cartridges and microprocessor-based hardware, with a successful television interface.

With the launch of the Internet, and the commercial explosion of desk top computing, language – that is the fuel for human interactions worldwide – grew exponentially in importance. More specifically, the greatest demand was for language that could link humans to machines in a natural way.

With the explosive growth of text data, the focus initially was on Natural Language Processing (NLP), “an interdisciplinary subfield of computer science and linguistics primarily concerned with giving computers the ability to support and manipulate human language.” Training software initially used annotated or referenced texts to address or answer specific questions or tasks precisely. The usefulness and accuracy to address inquiries outside of their pre-determined training was limited and inefficiency undermined their usage.

But computing power had now advanced far beyond what Warren McCullough and Walter Pitts could have possibly imagined in 1944, while the concept of “neural nets” couldn’t be more relevant. IBM describes the modern day version this way:

“Neural networks …are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another… Artificial neural networks are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer…Once an input layer is determined, weights are assigned. These weights help determine the importance of any given variable, with larger ones contributing more significantly to the output compared to other inputs. All inputs are then multiplied by their respective weights and then summed. Afterward, the output is passed through an activation function, which determines the output. If that output exceeds a given threshold, it “fires” (or activates) the node, passing data to the next layer in the network… it’s worth noting that the “deep” in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm. A neural network that only has two or three layers is just a basic neural network.”

The bottom line is that the automated system responds to an internal logic. The computers “next choice” is determined by how well it fits in with the prior choices. And it doesn’t matter where the words or “coins” come from. Feed it data, and it will “train” itself; and by following the rules or algorithms imbedded in the middle decision layers or screens, it will “transform” the acquired knowledge, into “generated” language that both human and machine understand.

In 2016, a group of tech entrepreneurs including Elon Musk and Reed Hastings, believing AI could go astray if restricted or weaponized, formed a non-profit called OpenAI. Two years later they released a deep learning product called Chat GPT.  This solution was born out of the marriage of Natural Language Processing and Deep Learning Neural Links with a stated goal of “enabling humans to interact with machines in a more natural way.”

The GPT stood for “Generative Pre-trained Transformer.” Built into the software was the ability to “consider the context of the entire sentence when generating the next word” – a tactic known as “auto-regressive.” As a “self-supervised learning model,” GPT is able to learn by itself from ingesting or inputting huge amounts of anonymous text; transform it by passing it through a variety of intermediary weighed screens that jury the content; and allow passage (and survival) of data that is validated. The resultant output? High output language that mimics human text.

Leadership in Microsoft was impressed, and in 2019 ponied up $1 billion to jointly participate in development of the product and serve as their exclusive Cloud provider.

The first ChatGPT-1 by OpenAI was first introduced by GPT-1 in 2018, but not formally released publicly until November 30, 2022.

It was trained on an enormous BooksCorpus dataset. Its’ design included an input and output layer, with 12 successive transformer layers sandwiched in between. It was so effective in Natural Language Processing that minimal fine tuning was required on the back end.

OpenAI released version two, called GPT-2, next, which was 10 times the size of its predecessor with 1.5 billion parameters, and the capacity to translate and summarize. GPT-3 followed. It had now grown to 175 billion parameters, 100 times the size of GPT-2, and was trained by ingesting a corpus of 500 billion content sources (including those of my own book – CODE BLUE). It could now generate long passages on verbal demand, do basic math, write code, and do (what the inventors describe as) “clever tasks.” An intermediate GPT 3.5 absorbed Wikipedia entries, social media posts and news releases.

On March 14, 2023, GPT-4 went big language, now with multimodal outputs including text, speech, images, and physical interactions with the environment. This represents an exponential convergence of multiple technologies including databases, AI, Cloud Computing, 5G networks, personal Edge Computing, and more.

 The New York Times headline announced it as “Exciting and Scary.” Their technology columnist wrote, “What we see emerging are machines that know how to reason, are adept at all human languages, and are able to perceive and interact with the physical environment.” He was not alone in his concerns. The Atlantic, at about the same time, ran an editorial titled, “AI is about to make social media (much) more toxic.

Leonid Zhukov, Ph.D, director of the Boston Consulting Group’s (BCG) Global AI, believes offerings like ChatGPT-4 and Genesis have the potential to become the brains of autonomous agents—which don’t just sense but also act on their environment—in the next 3 to 5 years. This could pave the way for fully automated workflows.”

Were he alive, Leonardo da Vinci, would likely be unconcerned. Five hundred years ago, he wrote nonchalantly, “It had long come to my attention that people of accomplishment rarely sat back and let things happen to them. They went out and happened to things.”

Mike Magee MD is a Medical Historian and regular contributor to THCB. He is the author of CODE BLUE: Inside America’s Medical Industrial Complex (Grove/2020).

By JONATHON FEIT

Journalists like being salty.  Like many venture investors, we who are no longer “green” have finely tuned BS meters that like to rip off the sheen of a press release to reach the truthiness underneath. We ask, is this thing real? If I write about XYZ, will I be embarrassed next year to learn that it was the next Theranos?

Yet journalists must also be optimistic—a delicate balance: not so jaded that one becomes boooring, not so optimistic that one gets giddy at each flash of potential; and still enamored of the belief that every so often, something great will remake the present paradigm.

This delicately balanced worldview is equally endemic to entrepreneurs that stick around: Intel founder Andy Grove’s famously said “only the paranoid survive,” a view that is inherently nefarious since it points out that failure is always lurking nearby. Nevertheless, to venture is to look past the risk, as in, “Someone has to reach that tall summit someday—it may as well be our team!” Pragmatic entrepreneurs seek to do something else, too: deliver value for one’s clients / customers / partners / users in excess of what they pay—which makes they willing to pay in excess of what the thing or service costs to produce. We call that metric “profit,” and over the past several years, too many young companies, far afield of technology and healthcare, forgot about it.

Once upon a time, not too many years ago, during the very first year that my company (Beyond Lucid Technologies) turned a profit, I presented to a room of investors in San Francisco, and received a stunning reply when told that people were willing to pay us for our work.  “But don’t you want to grow?” the investor asked. 

Flabbergasted, I replied that we felt it was more important to deliver enough value that people were willing to pay enough that we could operate in the black, whereas the typical “growth at all costs” model is essentially about subsidizing enough adoption using outside capital that winning a market becomes a game of chicken with one’s competitors: the one who can lose the most for longest wins…and when the other guy is dead and desiccated, having used up all its venture money driving prices and margins to zero, the winner gets to raise prices. Like a victorious seal, lion, or bison, the winner controls the beach, the savannah, the prairie.

According to Business Insider, Matthew Wansley, a professor at Yeshiva University’s Cardozo School of Law said, “Progressive economists had long understood that tech companies, backed by gobs of venture capital, were effectively subsidizing the price of their products until users couldn’t live without them. Think Amazon: Offer stuff cheaper than anyone else, even though you lose money for years, until you scale to unimaginable proportions. Then, once you’ve crushed the competition and become the only game in town, you can raise prices and make your money back. It’s called predatory pricing, and it’s supposed to be illegal.”

Happily, cynical ways of doing business don’t work forever or in all contexts. Once interest rates rise, every contender has a handicap—but it is the biggest, strongest, most willing to go to the mat who find themselves vulnerable in a new and unhappy way. Profitable companies have both hands free to fight, and their weapons of choice are real metrics to show value and efficiency. By contrast, firms whose growth was fueled by “free” money are fighting with their hands chained to cement that is getting heavier. Using the language of the Great Recession, the teaser rate on their mortgage just skyrocketed, and those payments…yeesh.

But profit is more than just a financial metric—it is also a powerful and pragmatic signal. The renewed, overdue focus on profit’s second, more esoteric importance was on full-peacock display during the first day of the Digital Health Innovation Summit (DHIS) West earlier this week, where the main takeaway from seemingly every presenter was: Can you prove your value, and convince me that I cannot go another day without you?

Hospital and health insurance executives—whose names I do not need to recite here; you can find the agenda online—speaking frankly and alongside firms whose services they have hired, addressed questions about how to break through the noise of too many emails, too polished emails, too little focus on building real relationships. Then they acknowledged that they are slammed-busy and lack the time to build them while also traveling to conferences to talk about relationship-building…which means finding another way through the noise. That is the entrepreneur’s mission, and trick. One executive basically said, “Don’t call us, we’ll call you” if we want what you have to offer (Remember people, this is San Diego, not Hollywood!).

Another confessed that so many young companies are coached about the “right” way to phrase an opening salvo that the pitches begin running together, filled with plenty of heart and dripping with mission but still lacking individuality. In other words, a bit of roughness-around-the-edges may not be a bad thing when some organizational leaders highlighted their interest in building collaboratively.  Because I would be remiss not to, I asked how Mobile Medical services can engage with hospitals to expand their role and showcase all the good they can do beyond transport—for example, Community Paramedicine. The advice was to sit down with the agency’s emergency department contact and straightforwardly say, “We’d like to help out more.” No fluff. No pussyfooting. Tactic #1: have a discussion. The worse anyone can say is “No.” Here’s something telling: I had a chance to explain some of the good that Community Paramedicine programs already do, and some of the interoperability wins that Mobile Medical services have already notched. Some of these executives did not even know about them—which just goes to highlight the noise. Both ventures and those who use them to do great things need to sing more about success….but, it seems, not necessarily more loudly.  Rather, in a more targeted fashion that all the willing, listening ears can hear.

Which goes back to profit: More than raising another round of funding, or winning an award, or stacking a slide deck with logos, being able to say “people are willing to pay for this work—presumably more than once—more than it costs to make, and you should consider it to, and here is why” is curious to those who may not have yet been aware that such a solution exists.

One hospital executive here described their employer’s new ethos: “We don’t need to do everything ourselves.” But with the willingness to look beyond the walls of the institution is a Monkey’s Paw kind of change: careful what you wish for. The price for such willingness is a focus on accountability—those rising interest rates put on pressure everywhere, which means investments have to perform. Now they cost money in excess of people’s time (which they are getting paid for anyway). As every minute becomes more expensive, the last thing these executives asked for is more waste.

I arrived at the DHIS West prepared to meet old friends and hear old tropes.  Perhaps I would even have been able to confirm that—as CEO of a company that is unusual by Bay Area standards, working in the world of Mobile Medicine that too few understand (“The sirens sound and your people show up…right?”)—there would be nothing to see because all the oxygen would have been spent talking about a hot new topic without fundamentals (or in the case of A.I. with declining fundamentals). Of course A.I. would be a bingo buzzword (“Take a shot!”) but I also expected boldface speakers reciting platitudes.

Boy was I wrong! Color me impressed! By dinner, my salty journalistic crust had washed away clean.  Instead, I confessed to my tablemates—an entrepreneur, an insurance professional, and Michelle Snyder, a lovely, ever-curious person who I first met a decade ago (wow!)—that DHIS West almost immediately inspired me to look back at the arc of our profession, and in so doing, to recognize how much change has really happened—even though, like so many fleeting loves in life, on a daily basis we are too close to see it. As Michelle said, it’s not moving fast enough—but it never will be for someone who is committed to improving the status quo. I suspect that for her, the deadline to achieve impact at scale in American and global healthcare will always be yesterday.

I later described to Ilana Brand, a business development executive in the area of digital health for the law firm Cooley, my own mental wellness and mission-motivation trick, which I have done for years and recommend to anyone who has been venturing for as long as I have: look back on those old slide decks from time to time to see how much has changed—and what remains the same. The through-line orientation to address problems in the market should ideally be consistent until they are solved—but a company cannot be stubborn either, lest an asteroid come. It must adaptive to changing realities while keeping its soul. Ideally, in hindsight, one sees ups, downs, fumbles and tackles, but always progressing toward the goal (and sometimes a Hail Mary pass is just what the digital doctor ordered).  I am writing this just days before Super Bowl LVIII (Go Niners!), so perhaps football offers an ideal entrepreneurial analogy after all.

What’s magical is to look back on the arc of change with a sense of wonder and gratitude for how far we have come when seen at a distance (as opposed to while in the trenches of innovation). It’s like watching the horizon bend in the distance while flying toward the sunset: we all know that the Earth is round, and if we get high enough, we can see so for ourselves. Yet that knowledge still pales against “Oh my gosh, look in the distance! The colors…the curve of our planet…how amazing to think we’re up so high.  No strings!”

Finally: we spoke, of course, of artificial intelligence—but not of generative A.I. per se. A dichotomy is forming: some think A.I. will be relegated, for the foreseeable future, to administration, where it will automate the paperwork that everyone hates and so it becomes both expensive and neglected. This approach has the added benefit of delaying the introduction of perceived “replacement” technologies into clinical settings (with pushback anticipated just like it was in Hollywood and elsewhere). The delay may serve to our collective benefit because A.I. has not yet come close to solving its hallucination problem.

Others (including me) believe we may be selling ourselves short—and I was further inspired by investor Ryan McCrackan, CFA, who described an optimistic future: as soon as something extraordinary proves itself, the instinctual corporate risk aversion, which often blocks great things from happening, will be proven to have overblown. Attention will quickly shift to all that could be possible. Then we’re off to the races, together, seeking and supporting meaningful improvements to under-attended sectors (“White spaces”) of health, safety, and life in general. Until then, we’ll embrace the most excellent irony that emerged post-pandemic, in conjunction with the Dawn of Artificial Intelligence: In both medicine and business, “relationships still matter.”

Jonathan Feit is the CEO of Beyond Lucid Technologies

By MIKE MAGEE

“What exactly does it mean to augment clinical judgement…?”

That’s the question that Stanford Law professor, Michelle Mello, asked in the second paragraph of a May, 2023 article in JAMA exploring the medical legal boundaries of large language model (LLM) generative AI.

This cogent question triggered unease among the nation’s academic and clinical medical leaders who live in constant fear of being financially (and more important, psychically) assaulted for harming patients who have entrusted themselves to their care.

That prescient article came out just one month before news leaked about a revolutionary new generative AI offering from Google called Genesis. And that lit a fire.

Mark Minevich, a “highly regarded and trusted Digital Cognitive Strategist,” writing in a December issue of  Forbes, was knee deep in the issue writing, “Hailed as a potential game-changer across industries, Gemini combines data types like never before to unlock new possibilities in machine learning… Its multimodal nature builds on, yet goes far beyond, predecessors like GPT-3.5 and GPT-4 in its ability to understand our complex world dynamically.”

Health professionals have been negotiating this space (information exchange with their patients) for roughly a half century now. Health consumerism emerged as a force in the late seventies. Within a decade, the patient-physician relationship was rapidly evolving, not just in the United States, but across most democratic societies.

That previous “doctor says – patient does” relationship moved rapidly toward a mutual partnership fueled by health information empowerment. The best patient was now an educated patient. Paternalism must give way to partnership. Teams over individuals, and mutual decision making. Emancipation led to empowerment, which meant information engagement.

In the early days of information exchange, patients literally would appear with clippings from magazines and newspapers (and occasionally the National Inquirer) and present them to their doctors with the open ended question, “What do you think of this?”

But by 2006, when I presented a mega trend analysis to the AMA President’s Forum, the transformative power of the Internet, a globally distributed information system with extraordinary reach and penetration armed now with the capacity to encourage and facilitate personalized research, was fully evident.

Coincident with these new emerging technologies, long hospital length of stays (and with them in-house specialty consults with chart summary reports) were now infrequently-used methods of medical staff continuous education. Instead, “reputable clinical practice guidelines represented evidence-based practice” and these were incorporated into a vast array of “physician-assist” products making smart phones indispensable to the day-to-day provision of care.

At the same time, a several decade struggle to define policy around patient privacy and fund the development of medical records ensued, eventually spawning bureaucratic HIPPA regulations in its wake.

The emergence of generative AI, and new products like Genesis, whose endpoints are remarkably unclear and disputed even among the specialized coding engineers who are unleashing the force, have created a reality where (at best) health professionals are struggling just to keep up with their most motivated (and often mostly complexly ill) patients. Needless to say, the Covid based health crisis and human isolation it provoked, have only made matters worse.

Like clinical practice guidelines, ChatGPT is already finding its “day in court.”  Lawyers for both the prosecution and defense will ask, “whether a reasonable physician would have followed (or departed from the guideline in the circumstances, and about the reliability of the guideline” – whether it exists on paper or smart phone, and whether generated by ChatGPT or Genesis.

Large language models (LLMs), like humans, do make mistakes. These factually incorrect offerings have charmingly been labeled “hallucinations.” But in reality, for health professionals they can feel like an “LSD trip gone bad.” This is because the information is derived from a range of opaque sources, currently non-transparent, with high variability in accuracy.

This is quite different from a physician directed standard Google search where the professional is opening only trusted sources. Instead, Genesis might be equally weighing a NEJM source with the modern day version of the National Inquirer. Generative AI outputs also have been shown to vary depending on day and syntax of the language inquiry.

Supporters of these new technologic applications admit that these tools are currently problematic but expect machine-driven improvement in generative AI to be rapid. They also have the ability to be tailored for individual patients in decision-support and diagnostic settings, and offer real time treatment advice. Finally, they self-updated information in real time, eliminating the troubling lags that accompanied original treatment guidelines.

One thing that is certain is that the field is attracting outsized funding. Experts like Mello predict that specialized applications will flourish. As she writes, “The problem of nontransparent and indiscriminate information sourcing is tractable, and market innovations are already emerging as companies develop LLM products specifically for clinical settings. These models focus on narrower tasks than systems like ChatGPT, making validation easier to perform. Specialized systems can vet LLM outputs against source articles for hallucination, train on electronic health records, or integrate traditional elements of clinical decision support software.”

One serious question remains. In the six-country study I conducted in 2002 (which has yet to be repeated), patients and physicians agreed that the patient-physician relationship was three things – compassion, understanding, and partnership. LLM generative AI products would clearly appear to have a role in informing the last two components. What their impact will be on compassion, which has generally been associated with face to face and flesh to flesh contact, remains to be seen.

Mike Magee MD is a Medical Historian and regular contributor to THCB. He is the author of CODE BLUE: Inside America’s Medical Industrial Complex (Grove/2020).

by KIM BELLARD

Well: 2024. I’m excited about the Paris Olympics, but otherwise I’d be just as happy to sleep through all the nonsense that the November elections will bring. In any event, I might as well start out talking about one of the hottest topics of 2023 that will get even more so in 2024: AI.

In particular, I want to look at what is being billed as the “AI PC.” 

Most of us have come to know about ChatGPT. Google has Bard (plus DeepMind’s Gemini), Microsoft is building AI into Bing and its other products, Meta released an open source AI, and Apple is building its AI framework. There is a plethora of others. You probably have used “AI assistants” like Alexa or Siri.

What most of the large language model (LLM) versions of AI have in common is that they are cloud-based. What AI PCs offer to do is to take AI down to your own hardware, not dissimilar to how PCs took mainframe computing down to your desktop.  

As The Wall Street Journal tech gurus write in their 2024 predictions in their 2024 predictions:

In 2024, every major manufacturer is aiming to give you access to AI on your devices, quickly and easily, even when they’re not connected to the internet, which current technology requires. Welcome to the age of the AI PC. (And, yes, the AI Mac.)

What’s coming is what engineers call “on-device AI.” Like our smartphones, our laptops will gain the ability to do the specialized computing required to perform AI-boosted tasks without connecting to the cloud. They will be able to understand our speech, search and summarize information, even generate images and text, all without the slow and costly round trip to a tech company’s server.

The chip companies are ready. Intel just announced their new AI PC chip. It believes that its new Intel® Core Ultra processor will change PCs forever: “Now, AI is for everyone.” If you’re used to thinking about CPU and GPU, now you’ll have to think about “NPU” – neural processing units.

Intel promises: “With AI-acceleration built into every Intel® Core Ultra processor, you now have access to a variety of experiences – enhanced collaboration, productivity, and creativity – right at your desktop.” It further claims it is working with over 100 developers and expects those developers to offer over 300 “AI-accelerated features” in 2024.

Rival AMD has also released its own AI chips. “We continue to deliver high performance and power-efficient NPUs with Ryzen AI technology to reimagine the PC,” said Jack Huynh, SVP and GM of AMD computing and graphics business. “The increased AI capabilities of the 8040 series will now handle larger models to enable the next phase of AI user experiences.”

And, of course, AI chip powerhouse Nvidia isn’t sitting idly in the AI PC race.  It says that already: “For GeForce RTX users, AI is now running on your PC. It’s personal, enhancing every keystroke, every frame and every moment.”

Nvidia sees four advantages to AI PCs:

• Availability: Whether a gamer or a researcher, everyone needs tools — from games to sophisticated AI models used by wildlife researchers in the field — that can function even when offline.
• Speed: Some applications need instantaneous results. Cloud latency doesn’t always cut it.
• Data size: Uploading and downloading large datasets from the cloud can be inefficient and cumbersome.
• Privacy: Whether you’re a Fortune 500 company or just editing family photos and videos, we all have data we want to keep close to home.

The PC manufacturers are getting ready. DigitalTrends’ Fionna Agomuoh spoke to multiple Lenovo executives, who are all-in on AI PCs. “Put simply,” she writes. “Lenovo sees the “AI PC” as a PC where AI is integrated at every level of the system, including both software and hardware.”  Lenovo Executive Vice President of Intelligent Devices Group, Luca Rossi, cited an example with gaming: “We apply certain AI techniques to improve the gaming experience. By making the machine understand what kind of usage model you’re going to do and then a machine fine tunes, the speed, the temperature, etc.”

AMD’s Jason Banta believes “the AI PC will be the next technological revolution since the graphical interface,” which is a pretty startling statement. He elaborated:

Prior to this, you kind of just typed commands. It wasn’t quite as intuitive. You saw the graphical interface with the mouse, and it really changed the way you interacted the productivity. How you got things done, how it felt. I think AI PC is going to be that powerful if not more powerful.

Mr. Banta also believes that having AI built into the PC will make AI cheaper, more secure, and more private.

HP’s CEO Enrique Lores told CNBC in November that AI capabilities will spur PC sales: “we think this is going to double the growth of the PC category starting next year.” Technology research form Canalys predicts 60% of PCs shipped in 2027 will be AI-capable. IDC analysts are similarly bullish, saying: “The integration of AI capabilities into PCs is expected to serve as a catalyst for upgrades, hitting shelves in 2024.”

Windows Central reports that Microsoft plans to release Surface Pro 10 as its first AI PC. Surface Laptop 6 may also feature AI capabilities, although what exactly those capabilities are for either device remain unclear.

And, yes, when we say “AI PC,” we’ll also be seeing AI Mac. “Apple may not wax eloquent about AI but it knows very well that the use cases for this technology are booming and that the development work will require unprecedented computing power,” Dipanjan Chatterjee, an analyst at Forrester, told CNN. “That’s a huge emerging opportunity, and Apple wants a piece of that pie.”

The people who aren’t quite ready are us.

Moral of the story: in the not-too-distant future, saying “AI PC” will be redundant. AI capabilities will be built-in, assumed – and not just in your PC but also your phone, your watch, your car, all of your devices. Some of those capabilities will be local, some may be boosted by nearby networked devices, others will rely on the cloud.

I’ll be interested in how any learning that a local AI gains is passed along to other versions, and vice-versa. E.g., my health devices will know things about how my health is impacted by various treatments, and some of those should be pooled with other patient data for broader meaning.

Just like 2023, AI is going to continue to surprise and impress us in 2024.

Kim is a former emarketing exec at a major Blues plan, editor of the late & lamented Tincture.io, and now regular THCB contributor

For my parents, March, 1965 was a banner month. First, that was the month that NASA launched the Gemini program, unleashing “transformative capabilities and cutting-edge technologies that paved the way for not only Apollo, but the achievements of the space shuttle, building the International Space Station and setting the stage for human exploration of Mars.” It also was the last month that either of them took a puff of their favored cigarette brand – L&M’s.

They are long gone, but the words “Gemini” and the L’s and the M’s have taken on new meaning and relevance now six decades later.

The name Gemini reemerged with great fanfare on December 6, 2023, when Google chair, Sundar Pichai, introduced “Gemini: our largest and most capable AI model.” Embedded in the announcement were the L’s and the M’s as we see here: “From natural image, audio and video understanding to mathematical reasoning, Gemini’s performance exceeds current state-of-the-art results on 30 of the 32 widely-used academic benchmarks used in large language model (LLM) research and development.

Google’s announcement also offered a head to head comparison with GPT-4 (Generative Pretrained Transformer-4.) It is the product of a non-profit initiative, and was released on March 14, 2023. Microsoft’s helpful AI search engine, Bing, helpfully informs that, “OpenAI is a research organization that aims to create artificial general intelligence (AGI) that can benefit all of humanity…They have created models such as Generative Pretrained Transformers (GPT) which can understand and generate text or code, and DALL-E, which can generate and edit images given a text description.”

While “Bing” goes all the way back to a Steve Ballmer announcement on May 28, 2009, it was 14 years into the future, on February 7, 2023, that the company announced a major overhaul that, 1 month later, would allow Microsoft to broadcast that Bing (by leveraging an agreement with OpenAI) now had more than 100 million users.

Which brings us back to the other LLM (large language model) – GPT-4, which the Gemini announcement explores in a head-to-head comparison with its’ new offering. Google embraces text, image, video, and audio comparisons, and declares Gemini superior to GPT-4.

Mark Minevich, a “highly regarded and trusted Digital Cognitive Strategist,” writing this month in Forbes, seems to agree with this, writing, “Google rocked the technology world with the unveiling of Gemini – an artificial intelligence system representing their most significant leap in AI capabilities. Hailed as a potential game-changer across industries, Gemini combines data types like never before to unlock new possibilities in machine learning… Its multimodal nature builds on yet goes far beyond predecessors like GPT-3.5 and GPT-4 in its ability to understand our complex world dynamically.”

Expect to hear the word “multimodality” repeatedly in 2024 and with emphasis.

But academics will be quick to remind that the origins can be traced all the way back to 1952 scholarly debates about “discourse analysis”, at a time when my Mom and Dad were still puffing on their L&M’s. Language and communication experts at the time recognized “a major shift from analyzing language, or mono-mode, to dealing with multi-mode meaning making practices such as: music, body language, facial expressions, images, architecture, and a great variety of communicative modes.”

Minevich believes that “With Gemini’s launch, society has arrived at an inflection point with AI advancement.” Powerhouse consulting group, BCG (Boston Consulting Group), definitely agrees. They’ve upgraded their L&M’s, with a new acronym, LMM, standing for “large multimodal model.” Leonid Zhukov, Ph.D, director of the BCG Global AI Institute, believes “LMMs have the potential to become the brains of autonomous agents—which don’t just sense but also act on their environment—in the next 3 to 5 years. This could pave the way for fully automated workflows.”

BCG predicts an explosion of activity among its corporate clients focused on labor productivity, personalized customer experiences, and accelerated (especially) scientific R&D. But they also see high volume consumer engagement generating content, new ideas, efficiency gains, and tailored personal experiences.

This seems to be BCG talk for “You ain’t seen nothing yet.” In 2024, they say all eyes are on “autonomous agents.” As they describe what’s coming next: “Autonomous agents are, in effect, dynamic systems that can both sense and act on their environment. In other words, with stand-alone LLMs, you have access to a powerful brain; autonomous agents add arms and legs.”

This kind of talk is making a whole bunch of people nervous. Most have already heard Elon Musk’s famous 2023 quote, “Mark my words, AI is far more dangerous than nukes. I am really quite close to the cutting edge in AI, and it scares the hell out of me.”  BCG acknowledges as much, saying, “Using AI, which generates as much hope as it does horror, therefore poses a conundrum for business… Maintaining human control is central to responsible AI; the risks of AI failures are greatest when timely human intervention isn’t possible. It also demands tempering business performance with safety, security, and fairness… scientists usually focus on the technical challenge of building goodness and fairness into AI, which, logically, is impossible to accomplish unless all humans are good and fair.”

Expect in 2024 to see once again the worn out phrase “Three Pillars” . This time it will be attached to LMM AI, and it will advocate for three forms of “license” in operate:

1. Legal license – “regulatory permits and statutory obligations.”
2. Economic license – ROI to shareholders and executives.
3. Social license – a social contract delivering transparency, equity and justice to society.

BCG suggests that trust will be the core challenge, and that technology is tricky. We’ve been there before. The 1964 Surgeon General’s report knocked the socks off of tobacco company execs who thought high-tech filters would shield them from liability. But the government report burst that bubble by stating “Cigarette smoking is a health hazard of sufficient importance in the United States to warrant appropriate remedial action.”  Then came the Gemini 6A’s 1st attempt to launch on December 12,1965.  It was cancelled when its’ fuel igniter failed.

Generative AI driven LMM’s will “likely be transformative,” but clearly will also have its ups and downs as well.  As BCG cautions, “Trust is critical for social acceptance, especially in cases where AI can act independent of human supervision and have an impact on human lives.”

Mike Magee MD is a Medical Historian and regular contributor to THCB. He is the author of CODE BLUE: Inside America’s Medical Industrial Complex.

By MIKE MAGEE

His biography states, “He speaks to philosophical questions about the fears and possibilities of new technology and how we can be empowered to shape our future. His work to bridge cultures spans artificial intelligence, cognition, language, music, creativity, ethics, society, and policy.”

He embraces the title “cross-disciplinary,” and yet his PhD thesis at UC Berkeley in 1980 “was one of the first to spur the paradigm shift toward machine learning based natural language processing technologies.” Credited with inventing and building “the world’s first global-scale online language translator that spawned Google Translate, Yahoo Translate, and Microsoft Bing Translator,” he is clearly a “connector” in a world currently consumed by “dividers.” In 2019, Google named De Kai as “one of eight inaugural members of its AI Ethics Council.”

The all encompassing challenge of our day, as he sees it, is relating to each other. As he says, “The biggest fear is fear itself – the way AI amplifies human fear exponentially…turning us upon ourselves through AI powered social media driving misinformation, divisiveness, polarization, hatred and paranoia.” The value system he embraces “stems from a liberal arts perspective emphasizing creativity in both technical and humanistic dimensions.”

Dr. De Kai is feeling especially urgent these days, which is a bit out of character.

As a 7 year old child of Chinese immigrants in St. Louis, he spoke little English, saying what needed to be said on the family’s piano. Summers were spent back and forth between Hong Kong and the states. Others noticed he’d sneak in some blues to the classical pieces, causing his grandfather to remark the synthesis pieces sounded “Chinese” to him. This led the budding linguist/musicologist to later reflect that “That got me thinking. I realized that the way we understand music is really dependent on the cultural frame of reference we adopt.”

Music and technology married during his PhD work at UC Berkeley, and eventually grounded four decades of research in “natural language processing and computational creativity.” He has earned the right to chill, but is anything but at ease these days, and the cause of his anguish is existential artificial intelligence.

As he said recently, “We are on the verge of breaking all our social, cultural and governmental norms…Our social norms were not designed to handle this level of stress.”

De Kai has morphed into an AI Ethicist. He is on a personal quest and anxious to bare his soul. The questions that keep him up at night all consider whether he is parenting his “AI children” properly. “Am I setting a good example? Am I a good role model? Do I speak respectfully to AI and teach them to respect diversity, or do I show them that it’s okay to insult people online?”

His focus is solidly on the here and now, because he doesn’t believe time is on our side. “We have more AIs today that are part of our society. These are functioning, integral, active, imitative, learning, influential members of society more than most — probably more influential than 90 percent of human society — in shaping culture…. Even though these are really weak AI’s, the culture that we are jointly shaping with our artificial members of society is the one under which every successive stronger generation of AI’s will be learning and spreading their culture. We are already in that cycle and we don’t realize it because we don’t look at machines from a sociological standpoint… This is unprecedented, given the ways we have created to develop and relate, both good and bad, will be exponentially increased by AI. In this way, the impact it will have on society and culture will be unimaginable.”

Raising “mindful AI’s” in the age of Trump is no small feat. It demands that AI children be “mindful of their ethical responsibilities.” Pulling this off in the developed world with an increasingly fractured educational system that pits science/technology against humanities will be a remarkable challenge. As De Kai puts it, “It is the single worst possible time in history to have an education system that cripples people to be unable to think deeply across these boundaries, about what humanity is in the face of technology.

To accomplish “A.I. alignment with the goals of humanity,” may require Americans to examine their own health and wellness in a manner that could be profoundly uncomfortable. Population welfare, philosophical treatises, and political compromise are not exactly our cultural strong suits.

How will we do with these competing priorities, wonders De Kai in a recent New York Times Op-Ed:  “Short-term instant gratification? Long-term happiness? Avoidance of extinction? Individual liberties? Collective good? Bounds on inequality? Equal opportunity? Degree of governance? Free speech? Safety from harmful speech? Allowable degree of manipulation? Tolerance of diversity? Permissible recklessness? Rights versus responsibilities?”

“Culture matters. A.I.s are now an everyday part of our society”,says De Kai. Changing culture, as health professionals know, is a tall order. It is about compassion, understanding and partnerships. It is about healing, providing health, and keeping individuals, families and communities whole. And – most importantly – it is about managing population-wide fear, worry and anxiety.

What De Kai is setting out to do is to change our historic culture (one built on self-interest, hyper-competitiveness, and distrust of good government). This is a tall order – something that parents, pastors, politicians and physicians equally recognize. Things evolve, and difficult things take time.

Mike Magee MD is a Medical Historian and regular contributor to THCB. He is the author of CODE BLUE: Inside America’s Medical-Industrial Complex.

By MIKE MAGEE

One of the top ten headlines of all time created by the satirical geniuses at The Onion was published 25 years ago this December. It read, “God Answers Prayers Of Paralyzed Little Boy. ‘No,’ Says God.”

The first paragraph of that column introduced us to Timmy Yu, an optimistic 7-year old, who despite the failures of the health system had held on to his “precious dream.” As the article explained, “From the bottom of his heart, he has hoped against hope that God would someday hear his prayer to walk again. Though many thought Timmy’s heavenly plea would never be answered, his dream finally came true Monday, when the Lord personally responded to the wheelchair-bound boy’s prayer with a resounding no.”

But with a faith that rivals the chieftains of today’s American health care system who continue to insist this is “the best health care in the world,” this Timmy remained undeterred. As The Onion recorded the imagined conversation, “‘I knew that if I just prayed hard enough, God would hear me,’ said the joyful Timmy,., as he sat in the wheelchair to which he will be confined for the rest of his life. ‘And now my prayer has been answered. I haven’t been this happy since before the accident, when I could walk and play with the other children like a normal boy.’”

According to the article, the child did mildly protest the decision, but God held the line, suggesting other alternatives. “God strongly suggested that Timmy consider praying to one of the other intercessionary agents of Divine power, like Jesus, Mary or maybe even a top saint,” Timmy’s personal physician, Dr. William Luttrell, said. ‘The Lord stressed to Timmy that it was a long shot, but He said he might have better luck with one of them.’”

It didn’t take a wild leap of faith to be thrust back into the present this week. Transported by a headline to Rochester, Minnesota, the banner read, “Mayo Clinic to spend $5 billion on tech-heavy redesign of its Minnesota campus.” The “reinvention” is intended to “to present a 21st-century vision of clinical care” robust enough to fill 2.4 million square feet of space.

The Mayo Clinic’s faith in this vision is apparently as strong as little “Timmy’s”, and their “God” goes by the initials AI.

Only six months earlier, they announced a 10-year collaboration with Google to create an “AI factory” described as “an assembly line of AI solutions that are developed at scale and incorporated into clinical workflows.” They added that they are “looking beyond foundational development.”

Cris Ross, CIO of Mayo Clinic, imagines crowded hallways. He says, “I think it’s really wonderful that Google will have access and be able to walk the halls with some of our clinicians, to meet with them and discuss what we can do together in the medical context.” No small dreamer, Ross sees bright skies ahead – “an assembly line of AI breakthroughs… being able to do the kinds of things that people are doing in little bits all over the planet, to be able to do the same kinds of things but at scale and repeatedly.”

Luckily, the “AI god” will provide management infrastructure in the form of the new Mayo Clinic Platform, a group of digital and long-distance health care initiatives under the direction of physician executive missionary, John Halamka MD. As a fully registered Medical Industrial Complex (MIC) professional, he has touched all the bases – graduate of hallowed Stanford University, member NAM, wrote econometrics software for Milton Friedman, medical informatics at MIT and Harvard, birthed the software startup Ibis Research Labs, CIO at Beth Israel Deaconess, and influencer on multiple government panels.

The venture he directs will not rely on spirit alone. Venture capital dollars have helped launch two joint ventures – one intended to collect deindentified clinical data from patients far and wide, and the second “to commercialize algorithms for the early detection of disease.”

The “AI god” is wise enough not to reinvent the wheel. His/Her plan comes directly from the MIC playbook, originally designed in 1950 by Arthur Sackler. Create an integrated career ladder for academic medical scientists that will seamlessly carry them from Medicine to Industry to Government and back again, reward all parties with exclusive patents and hidden incentives, and trust that the little “Timmy’s” of the world will find some way to survive.

Of course, the “AI god”, to reach this level of power so quickly, has had to make certain sacrifices, notably replacing one of the two bedrock commandments that have served to guide human behavior for several thousand years:

The first – “Love the Lord your God with all your heart and with all your soul and with all your mind” – can stand, as we transfer loyalty to an over-arching artificial intelligence.

But we must toss the second, “Love your neighbor as yourself.”  And embrace instead, “Love technology as yourself”, and all the riches will follow.

And for the patients? Have faith that when science and technology finally “defeats disease,” your health will follow.

Mike Magee MD is a Medical Historian and regular contributor to THCB. He is the author of CODE BLUE: Inside America’s Medical-Industrial Complex.

By KIM BELLARD

You had to know I’d write about the new Humane AI Pin, right?

After all, I’d been pleading for the next big thing to take the place of the smartphone, as recently as last month and as long ago as six years, so when a start-up like Humane suggests it is going to do just that, it has my attention.  Even more intriguing, it is billed as an AI device, redefining “how we interact with AI.”  It’s like catnip for me.

For anyone who has missed the hype – and there has been a lot of hype, for several months now – Humane is a Silicon Valley start-up founded by two former Apple employees, Imran Chaudhri and Bethany Bongiorno (who are married).  They left Apple in 2016, had the idea for the AI Pin by 2018, and are ready to launch the actual device early next year.  It is intended to be worn as a pin on the lapel, starts at $699, and requires a monthly $24 subscription (which includes wireless connectivity).  Orders start November 16.

Partners include OpenAI, Microsoft, T-Mobile, Tidal, and Qualcomm.

Mr. Chaudhri told The New York Times that artificial intelligence  “can create an experience that allows the computer to essentially take a back seat.” He also told TechCrunch that the AI Pin represented “a new way of thinking, a new sense of opportunity,” and that it would “productize AI” (hmm, what are all those other people in AI doing?).  

Humane’s press release elaborates:

Ai Pin redefines how we interact with AI. Speak to it naturally, use the intuitive touchpad, hold up objects, use gestures, or interact via the pioneering Laser Ink Display projected onto your palm. The unique, screenless user interface is designed to blend into the background, while bringing the power of AI to you in multi-modal and seamless ways.

Basically, you wear a pin that is connected with an AI, which – upon request – will listen and respond to your requests. It can respond verbally, or it can project a laser display into the palm of your hand, which you can control with a variety of gestures that I am probably too old to learn but which younger people will no doubt pick up quickly.  It can take photos or videos, which the laser display apparently does not, at this point, do a great job projecting. 

Here’s Humane’s introductory video:

Some cool features worth noting:

• It can summarize your messages/emails;
• It can make phone calls or send messages;
• It can search the web for you to answer questions/find information;
• It can act as a translator;
• It has trust features that include not always listening and a “Trust Light” that indicates when it is.

It does not rely on apps; rather, it uses “AI Experiences” – on device and in the cloud — to accomplish whatever goals smartphone apps try to accomplish.  The press release brags: “Instead, it quickly understands what you need, connecting you to the right AI experience or service instantly.”  

Ken Kocienda, Humane’s head of product engineering, contrasted the AI Pin with smartphone’s addiction bias, telling Erin Griffin of The New Times: “It’s more of a pull than pushing content at you in the way iPhones do.”

Health and nutrition is said to be an early focus, although currently it is mostly calorie counting.

Ms. Griffin summarizes the AI Pin thusly: “It was, like any new technology, equal parts magic and awkward.”  Inverse’s Ian Carlos Campbell was also impressed: “Added together, the Ai Pin is exciting in the way all big swings are, the difference being it seems like Humane could back up its claims.”  

Mark Wilson of Fast Company, on the other hand, was more reserved, noting: “In practice, the AI Pin reminded me of an Echo Dot on your chest,” and wondering: “Where was all the magical stuff?…The stuff where, because the AI Pin is so overtly planted on our person, the rest of its demands could disappear?”   

Mr. Chaudhri defended using a pin instead of another version of smartglasses, telling Mr. Wilson:

Contextual compute has always been assumed as something you have to wear on your face.  There’s just a lot of issues with that…If you look at the power of context, and that’s the impediment to achieving contextual compute, there has to be another way. So we started looking at what is the piece that allows us to be far more personal? We came up with the fact that all of us wear clothing, so how can we adorn a device that gives us context on our clothing?

Or, as Mr. Chaudhri said earlier this year: “The future is not on your face.”

Color Mr. Wilson unconvinced:

Humane’s issue in a nutshell isn’t that a wearable assistant is inherently a flawed idea, it’s that Chaudhri’s product doesn’t yet solve the problem he has diagnosed and set out to mitigate: that removing a screen will solve our dependence on technology… it appears Humane hasn’t unlocked the potential of AI of today, let alone tomorrow, nor has it fundamentally solved any significant problems we have with technology.

To be honest, it isn’t everything I’d hoped it’d be either. The AI is impressive but, at this point, still limited. The laser display is cool but not really ready for prime time. The pin is sleek, as would be expected from Apple alums, but I don’t want to even be aware of a device; I want it embedded in my clothes, maybe worn as a “smart tattoo.”  

But these are, really, quibbles. The AI will get exponentially more useful. The device will get much smaller. The display will get much better. As others have pointed out, the iPod was a revolution but was limited, and led to the iPhone, which itself was initially fairly limited.  Similarly, the AI Pin should get much, much powerful, and have even more awesome successors.

In the press release, Ms. Bongiorno and Mr. Chaudhri say:

AI Pin is the embodiment of our vision to integrate AI into the fabric of daily life, enhancing our capabilities without overshadowing our humanity. We are proud to finally unveil what we and the team at Humane have been working on for the past four years. For us, Ai Pin is just the beginning.

The introductory video closes with Mr. Chaudhri promising: “It is our aim at Humane to build for the world not as it is today, but as it could be tomorrow.”  We should all be designing for that.

Kim is a former emarketing exec at a major Blues plan, editor of the late & lamented Tincture.io, and now regular THCB contributor