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ChatGPT Vs. Magic 8 Ball: Who Can Solve “The HealthCare Crisis”?

By MICHAEL L. MILLENSON

Long before ChatGPT, whose question-answering choices still remain somewhat of a black box, there was an equally mysterious, question-answering black ball. I decided to ask them each of them how to solve the cost, quality and access issues labeled for more than half a century as “the healthcare crisis.”

The hard, plastic Magic 8 Ball was invented in 1946, two years before a landmark Supreme Court decision spurred a boom in employer-sponsored health insurance. It catapulted into kid-driven popularity in the 1970s, the same decade that rising healthcare costs propelled “healthcare crisis” into the public vocabulary.

Magic 8 Ball and ChatGPT
ChatGPT is a “black box,” Magic 8 ball a black ball.

The healthcare crisis is still with us, as is Magic 8 Ball, which, thanks to current owner Mattel, can now be consulted either in person (i.e., by holding and shaking it) or online. With a fiercely fought presidential election campaign underway, I decided that pitting the black box vs. the black ball to answer crucial health policy questions would likely provide just as much clarity as wading through weasel-worded white papers.

Both ChatGPT (Cost to OpenAI: $400,000 per day to operate) and Magic 8 Ball (One-time cost: $14.99) were up for the challenge, though they acknowledged it wouldn’t be easy.

“Can you help me solve the healthcare crisis?” I asked. “Signs point to yes,” Magic 8 ball replied, in its typically pithy, understated manner. ChatGPT, on the other hand, took my question as an invitation to show off its artificial intelligence.

“Addressing the healthcare crisis is a complex and multifaceted challenge that requires a holistic approach,” ChatGPT began. Then, as if a Washington think tank had been crossed with an academic policy conference, the Large Language Model offered a very large helping of language. There were 8 “key strategies,” each of which contained three bullet points, and each of which, I was advised, “involves detailed planning, resource allocation, and collaboration among various stakeholders, including government, healthcare providers, insurers, and the public.”

Then there was this diss when I asked about its competitor. “It’s a fun toy,” sneered the chatbot (if chatbots could sneer), “but it doesn’t provide reliable or informed answers.”

I decided to home in on specifics.

“Is a government-run single payer system the right answer?” I asked. “My sources say ‘no,’” Magic 8 ball told me. ChatGPT was more positive, with caveats.

“A government-run single-payer healthcare system is one potential solution to the healthcare crisis, and it comes with its own set of advantages and challenges,” the chatbot replied. It added, “Whether it is the ‘right’ answer depends on various factors” – and then, once more, went on to provide a long list of relevant ones.

I decided to inquire about an approach with bipartisan support. “Is value-based healthcare the best way to control costs?”

“It is decidedly so,” said the Magic 8 Ball immediately. But ChatGPT, usually lightning quick, waited perhaps 20 seconds before not only responding positively, but presenting an overview and specific suggestions. There were 5 advantages and 5 challenges, plus 3 examples of possible strategies (accountable care organizations, bundled payments and patient-centered medical homes), all tied together with 5 considerations for implementation.

“Ultimately, VBHC can be a key component of a broader strategy to reform healthcare systems and achieve sustainable cost control,” ChatGPT concluded.

That pattern continued as I probed about the need for more effective financial incentives to reward high-quality, cost-effective care, a central component of VBHC. “It is certain,” Magic 8 Ball quickly agreed. ChatGPT, meanwhile, again paused for a lengthy period (by its standards) before responding “thoughtfully” (by human standards).

“Yes,” it said, “effective financial incentives are crucial for promoting high-quality, cost-effective care. Properly designed incentives can align the interests of healthcare providers, payers and patients, leading to better health outcomes and more efficient use of resources.”

The chatbot then listed 5 types of financial incentives, 5 key elements of effective incentive programs and three specific examples incorporating them.

Continuing the financial incentives theme, I asked whether health savings accounts could help. Magic 8 Ball simply replied, “Yes,” while ChatGPT carefully pointed out that while HSAs “offer some benefits, they are not a comprehensive solution to the broader health care crisis.”

Like politicians, both ChatGPT and Magic 8 Ball sometimes hedged. “Are hospital mergers good or bad for patients?” I asked. “Ask again later,” said Magic 8 Ball. “Hospital mergers can have both positive and negative impacts on patients,” responded ChatGPT, before presenting a long list of why either might be the case.

“Is private equity buying doctors’ practices good or bad for patients?” I inquired. “Concentrate and ask again,” evaded Magic 8 Ball, followed by an incomprehensible, “Most likely.” ChatGPT allowed that this was “a complex issue, with potential benefits and drawbacks for patients,” before going on to the kind of pro and con balancing act any politician might admire.

I decided it was time to cut to the heart of the matter.

“Will health care costs ever be effectively controlled in America?” I demanded.

Magic 8 Ball tried to spare my feelings – “Better not to tell you now”– while ChatGPT, in its elliptical way, pointed me towards the unpleasant truth. While the challenge was not “insurmountable,” answered ChatGPT, it would require a “multi-faceted approach” involving “strong political will, stakeholder collaboration, and continuous evaluation and adjustment of strategies.”

In other words, “No.”

Michael Millenson is President of Health Quality Advisors and a long time THCB regular, he’s also a Forbes columnist where this piece first appeared.

Why Sam Altman Cares So Much About Voice

By MIKE MAGEE

When OpenAI decided to respond to clamoring customers demanding voice mediated interaction on Chat GPT, CEO Sam Altman went all in. That’s because he knew this was about more than competitive advantage or convenience. It was about relationships – deep, sturdy, loyal and committed relationships.

He likely was aware, as well, that the share of behavioral health in telemedicine mediated care had risen from 1% in 2019 to 33% by 2022. And that the pandemic had triggered an explosion of virtual mental health services. In a single year, between 2020 and 2021, psychologists offering both in-person and virtual sessions grew from 30% to 50%. Why? The American Psychological Association suggests these oral communications are personal, confidential, efficient and effective. Or in one word – useful.

As Forbes reported in 2021, “Celebrity endorsements, like Olympic swimmer Michael Phelps’ campaign with virtual therapy startup Talkspace, started to chip away at the long standing stigma, while mindfulness apps like Calm offered meditation sessions at the click of a button. But it was the Covid-19 pandemic and collective psychological fallout that finally mainstreamed mental health.” As proof, they noted mental health start-up funding has increased more than fivefold over the prior four years.

Altman was also tracking history. The first “mass medium” technology in the U.S. was voice activated – the radio. He also understood its’ growth trajectory a century ago. From a presence in 1% of households in 1923, it became a fixture in 3/4 of all US homes just 14 years later.

Altman also could see the writing on the wall. The up and coming generations, the ones that gently encouraged Biden to exit stage left, were both lonely and connected.

The most recent Nielson and Edison Research told him that the average adult in the U.S. now  spends four hours a day consuming audio and their associated ads. 67% of that listening was on radios, 20% on podcasts, 10% on music streaming and 3% on satellite radio.

Post-pandemic, younger generations use of online audio had skyrocketed.  In 2005, only 15% of young adults listened online. By 2023, it had reached 75%. And as their listening has risen, loneliness rates in young adults have declined from 38% in 2020 to 24% now.

A decade earlier, screenwriter Spike Jonze ventured into this territory when he wrote Her. Brilliantly cast, the film featured Joaquin Phoenix as lonely, introverted Theodore Twombly, reeling from an impending divorce. In desperation, he developed more than a relationship (a friendship really) with an empathetic reassuring female AI, voiced by actress Scarlett Johansson.

Scarlett’s performance was so convincing that it catapulted Her into contention for 5 academy awards winning Best Original Screenplay. It also apparently impressed Sam Altman, who, a decade later, approached Scarlett to be the “voice” of ChatGPT’s virtual lead. She declined, seeing the potential downside of becoming a virtual creature. He subsequently identified a “Scarlett-like” voice actor and chose “Sky” as one of five voice choices to embody ChatGPT. Under threat of a massive intellectual property challenge, Altman recently “killed off” Sky, but the other four virtual companions (out of 400 auditioned) have survived.

As for content so that “what you say” is as well represented as “how you say it,” companies like Google have that covered. Their LLM (Large Language Model) product was trained on content from over 10 million websites, including HealthCommentary.org. Google engineer, Blaise Aguera y Arcas says “Artificial neural networks are making strides toward consciousness.”

Where this all ends up for the human race remains an open question. What is known is that the antidote for loneliness and isolation is relationships. But of what kind? Who knows? Oxford’s Evolutionary Psychologist Robin Dunbar believes he does.

Altman likely paid close attention to this review by Atlantic writer Sheon Han in 2021: “Robin Dunbar is best known for his namesake ‘Dunbar’s number,’ which he defines as the number of stable relationships people are cognitively able to maintain at once. (The proposed number is 150.) But after spending his decades-long career studying the complexities of friendship, he’s discovered many more numbers that shape our close relationships. For instance, Dunbar’s number turns out to be less like an absolute numerical threshold than a series of concentric circles, each standing for qualitatively different kinds of relationships.… All of these numbers (and many non-numeric insights about friendship) appear in his new book, Friends: Understanding the Power of Our Most Important Relationships.”

But what many experts now agree is that voice seems to unlock the key. Shorthand for Altman: Pick the right voice and you might just trigger the addition of 149 “friends” for each ChatGPT “buyer.”

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)

AI Cognition – The Next Nut To Crack

By MIKE MAGEE

OpenAI says its new GPT-4o is “a step towards much more natural human-computer interaction,” and is capable of responding to your inquiry “with an average 320 millisecond (delay) which is similar to a human response time.” So it can speak human, but can it think human?

The “concept of cognition” has been a scholarly football for the past two decades, centered primarily on “Darwin’s claim that other species share the same ‘mental powers’ as humans, but to different degrees.” But how about genAI powered machines? Do they think?

The first academician to attempt to define the word “cognition” was Ulric Neisser in the first ever textbook of cognitive psychology in 1967. He wrote that “the term ‘cognition’ refers to all the processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used. It is concerned with these processes even when they operate in the absence of relevant stimulation…”

The word cognition is derived from “Latin cognoscere ‘to get to know, recognize,’ from assimilated form of com ‘together’ + gnoscere ‘to know’ …”

Knowledge and recognition would not seem to be highly charged terms. And yet, in the years following Neisser’s publication there has been a progressively intense, and sometimes heated debate between psychologists and neuroscientists over the definition of cognition.

The focal point of the disagreement has (until recently) revolved around whether the behaviors observed in non-human species are “cognitive” in the human sense of the word. The discourse in recent years had bled over into the fringes to include the belief by some that plants “think” even though they are not in possession of a nervous system, or the belief that ants communicating with each other in a colony are an example of “distributed cognition.”

What scholars in the field do seem to agree on is that no suitable definition for cognition exists that will satisfy all. But most agree that the term encompasses “thinking, reasoning, perceiving, imagining, and remembering.” Tim Bayne PhD, a Melbourne based professor of Philosophy adds to this that these various qualities must be able to be “systematically recombined with each other,” and not be simply triggered by some provocative stimulus.

Allen Newell PhD, a professor of computer science at Carnegie Mellon, sought to bridge the gap between human and machine when it came to cognition when he published a paper in 1958 that proposed “a description of a theory of problem-solving in terms of information processes amenable for use in a digital computer.”

Machines have a leg up in the company of some evolutionary biologists who believe that true cognition involves acquiring new information from various sources and combining it in new and unique ways.

Developmental psychologists carry their own unique insights from observing and studying the evolution of cognition in young children. What exactly is evolving in their young minds, and how does it differ, but eventually lead to adult cognition? And what about the explosion of screen time?

Pediatric researchers, confronted with AI obsessed youngsters and worried parents are coming at it from the opposite direction. With 95% of 13 to 17 year olds now using social media platforms, machines are a developmental force, according to the American Academy of Child and Adolescent Psychiatry. The machine has risen in status and influence from a side line assistant coach to an on-field teammate.

Scholars admit “It is unclear at what point a child may be developmentally ready to engage with these machines.” At the same time, they are forced to admit that the technological tidal waves leave few alternatives. “Conversely, it is likely that completely shielding children from these technologies may stunt their readiness for a technological world.”

Bence P Ölveczky, an evolutionary biologist from Harvard, is pretty certain what cognition is and is not. He says it “requires learning; isn’t a reflex; depends on internally generated brain dynamics; needs access to stored models and relationships; and relies on spatial maps.”

Thomas Suddendorf PhD, a research psychologist from New Zealand, who specializes in early childhood and animal cognition, takes a more fluid and nuanced approach. He says, “Cognitive psychology distinguishes intentional and unintentional, conscious and unconscious, effortful and automatic, slow and fast processes (for example), and humans deploy these in diverse domains from foresight to communication, and from theory-of-mind to morality.”

Perhaps the last word on this should go to Descartes. He believed that humans mastery of thoughts and feelings separated them from animals which he considered to be “mere machines.”

Were he with us today, and witnessing generative AI’s insatiable appetite for data, its’ hidden recesses of learning, the speed and power of its insurgency, and human uncertainty how to turn the thing off, perhaps his judgement of these machines would be less disparaging; more akin to Mira Murati, OpenAI’s chief technology officer, who announced with some degree of understatement this month, “We are looking at the future of the interaction between ourselves and machines.”

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

The 7 Decade History of ChatGPT

By MIKE MAGEE

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.”

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Altman, Ive, and AI

BY KIM BELLARD

Earlier this year I urged that we Throw Away That Phone, arguing that the era of the smartphone should be over and that we should get on to the next big thing.  Now, I don’t have any reason to think that either Sam Altman, CEO of OpenAI, and Jony Ive, formerly and famously of Apple and now head of design firm LoveFrom, read my article but apparently they have the same idea.  

Last week The Information and then Financial Times reported that OpenAi and LoveFrom are “in advanced talks” to form a venture in order to build the “iPhone of artificial intelligence.”  Softbank may fund the venture with as much as $1b.  There have been brainstorming sessions, and discussions are said to be “serious,” but a final deal may still be months away. The new venture would draw on talent from all three firms.

Details are scare, as are comments from any of the three firms, but FT cites sources who suggest Mr. Altman sees “an opportunity to create a way of interacting with computers that is less reliant on screens.” which is a sentiment I heartily agree with.  The Verge similarly had three sources who agreed that the goal is a “more natural and intuitive user experience.”

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The Next Pandemic May Be an AI one

By KIM BELLARD

Since the early days of the pandemic, conspiracy theorists have charged that COVID was a manufactured bioweapon, either deliberately leaked or the result of an inadvertent lab leak. There’s been no evidence to support these speculations, but, alas, that is not to say that such bioweapons aren’t truly an existential threat.  And artificial intelligence (AI) may make the threat even worse.

Last week the Department of Defense issued its first ever Biodefense Posture Review.  It “recognizes that expanding biological threats, enabled by advances in life sciences and biotechnology, are among the many growing threats to national security that the U.S. military must address.  It goes on to note: “it is a vital interest of the United States to manage the risk of biological incidents, whether naturally occurring, accidental, or deliberate.”  

“We face an unprecedented number of complex biological threats,” said Deborah Rosenblum, Assistant Secretary of Defense for Nuclear, Chemical, and Biological Defense Programs. “This review outlines significant reforms and lays the foundation for a resilient total force that deters the use of bioweapons, rapidly responds to natural outbreaks, and minimizes the global risk of laboratory accidents.”

And you were worried we had to depend on the CDC and the NIH, especially now that Dr. Fauci is gone.  Never fear: the DoD is on the case.  

A key recommendation is establishment of – big surprise – a new coordinating body, the Biodefense Council. “The Biodefense Posture Review and the Biodefense Council will further enable the Department to deter biological weapons threats and, if needed, to operate in contaminated environments,” said John Plumb, Assistant Secretary of Defense for Space Policy. He adds, “As biological threats become more common and more consequential, the BPR’s reforms will advance our efforts not only to support the Joint Force, but also to strengthen collaboration with allies and partners.”

Which is scarier: that DoD is planning to operate in “contaminated environments,” or that it expects these threats will become “more common and more consequential.” Welcome to the 21st century.  

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Asking Bard And ChatGPT To Find The Best Medical Care, I Got Truth And Truthiness

BY MICHAEL MILLENSON

If you ask ChatGPT how many procedures a certain surgeon does or a specific hospital’s infection rate, the OpenAI and Microsoft chatbot inevitably replies with some version of, “I don’t do that.”

But depending upon how you ask, Google’s Bard provides a very different response, even recommending a “consultation” with particular clinicians.

Bard told me how many knee replacement surgeries were performed by major Chicago hospitals in 2021, their infection rates and the national average. It even told me which Chicago surgeon does the most knee surgeries and his infection rate. When I asked about heart bypass surgery, Bard provided both the mortality rate for some local hospitals and the national average for comparison. While sometimes Bard cited itself as the information source, beginning its response with, “According to my knowledge,” other times it referenced well-known and respected organizations.

There was just one problem. As Google itself warns, “Bard is experimental…so double-check information in Bard’s responses.” When I followed that advice, truth began to blend indistinguishably with “truthiness” – comedian Stephen Colbert’s memorable term to describe information that’s seen as true not because of supporting facts, but because it “feels” true.

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Can we trust ChatGPT to get the basics right?

by MATTHEW HOLT

Eric Topol has a piece in his excellent newsletter Ground Truth‘s today about AI in medicine. He refers to the paper he and colleagues wrote in Nature about Generalist Medical Artificial Intelligence (the medical version of GAI). It’s more on the latest in LLM (Large Language Models). They differ from previous AI which was essentially focused on one problem, and in medicine that mostly meant radiology. Now, you can feed different types of information in and get lots of different answers.

Eric & colleagues concluded their paper with this statement: “Ultimately, GMAI promises unprecedented possibilities for healthcare, supporting clinicians amid a range of essential tasks, overcoming communication barriers, making high-quality care more widely accessible, and reducing the administrative burden on clinicians to allow them to spend more time with patients.” But he does note that “there are striking liabilities and challenges that have to be dealt with. The “hallucinations” (aka fabrications or BS) are a major issue, along with bias, misinformation, lack of validation in prospective clinical trials, privacy and security and deep concerns about regulatory issues.”

What he’s saying is that there are unexplained errors in LLMs and therefore we need a human in the loop to make sure the AI isn’t getting stuff wrong. I myself had a striking example of this on a topic that was purely simple calculation about a well published set of facts. I asked ChatGPT (3 not 4) about the historical performance of the stock market. Apparently ChatGPT can pass the medical exams to become a doctor. But had it responded with the same level of accuracy about a clinical issue I would be extremely concerned!

The brief video of my use of ChatGPT for stock market “research” is below:

Ultrasound is Ultra-Cool

BY KIM BELLARD

AI continues to amaze – ChatGPT is now passing Wharton Business School exams, Microsoft and Google are doubling down in their AI efforts – and I’m as big a fan as anyone, but I want to talk about a technology that has been more under the radar, so to speak: ultrasound.  

Yes, ultrasound.  Most of us have probably had an ultrasound at some point (especially if you’ve been pregnant) and Dr. Eric Topol continues his years-long quest to replace the ancient stethoscope technology with ultrasound, but if you think ultrasound is just another nifty tool in the imaging toolbox, you’ve missed a lot. 

Let’s start with the coolest use I’ve seen: ultrasound can be used for 3D printing.  Inside the body.  

This news on this dates back to last April, when researchers from Concordia University published their findings in Nature (I found out about it last week).  Instead of the more common “Additive Manufacturing” (AM) approach to 3D printing, these researchers use Direct Sound Printing (DSP).  

The paper summarizes their results: “To show unique future potentials of DSP, applications such as RDP [Remote Distance Printing] for inside body bioprinting and direct nanoparticle synthesizing and pattering by DSP for integrating localized surface plasmon resonance with microfluidics chip are experimentally demonstrated.”

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Searching For The Next Search

By KIM BELLARD

I didn’t write about ChatGPT when it was first introduced a month ago because, well, it seemed like everyone else was. I didn’t play with it to see what it could do.  I didn’t want it to write any poems. I didn’t have any AP tests I wanted it to pass. And, for all you know, I’m not using it to write this. But when The New York Times reports that Google sees ChatGPT as a “Code Red” for its search business, that got my attention.

A few months ago I wrote about how Google saw TikTok as an existential threat to its business, estimating that 40% of young people used it for searches. It was a different kind of search, mind you, with video results instead of links, but that’s what made it scary – because it didn’t just incrementally improve “traditional” search, as Google had done to Lycos or Altavista, it potentially changed what “search” was.    

TikTok may well still do that (although it is facing existential issues of its own), but ChatGPT could pose an even greater threat. Why get a bunch of search results that you still have to investigate when you could just ask ChatGPT to tell you exactly what you want to know?

Look, I like Google as much as anyone, but the prospect that its massive dominance of the search engine market could, in the near future, suddenly come to an end gives me hope for healthcare.  If Google isn’t safe in search, no company is safe in any industry, healthcare included.

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