Ground Truths

Few, if any, physician researchers have done more to understand the long-term impact of Covid than Dr. Ziyad Al-Aly, a professor, nephrologist, and epidemiologist along with his team at Washington University, St. Louis. Here is the transcript (with links to the audio) of our conversation that was recorded one 7 September 2023.

Eric Topol (00:00):

Welcome to Ground Truths, and this podcast is a special one for me. I get to meet professor Dr. Ziyad Ali for the first time, even though we've been communicating for years. So welcome, Ziyad.

Ziyad Al-Aly (00:15):

Well, thank you. Thank you. Thank you for having me. It's really a delight and pleasure and an honor to be with you here today. So thank you. Thank you for the invitation, and most importantly, thank you for all the stuff that you do and you've been doing over the past several years, communicating science to the whole world, especially during the pandemic and enormously grateful for all your effort.

Background in Lebanon, the move to Wash U., and Epidemiology

Eric Topol (00:33):

Well, you're too kind and we're going to get into your work, which is more than formidable. But before I do that, because you have been a leading light in the pandemic and understanding, especially through the large veterans affairs population, the largest healthcare system in the United States, the toll of covid. But before we touch on that a bit on your background first, you're a young guy. You haven't even hit 50 yet, my goodness. Right. And you grew up in Lebanon, as I understand it, and you were already coding when you were age 14, I think, right? Pretty wild. And then perhaps the death of your father at a young age of multiple myeloma had a significant impact on your choice to go into medicine. Is that right?

Ziyad Al-Aly (01:28):

Yeah, that's how it is. So I grew up in Lebanon, and when I was growing up, the computer revolution at that time was happening and all of a sudden in my surroundings, there's these people who have these Commodore 64. So I decided that I wanted one. I asked my parents to get me one. They got me one. I learned coding at that age, and my passion was I thought I wanted to do then why not to do computer science. And then my dad fell ill with multiple myeloma and it was an aggressive form and he required initially a lot of chemotherapy and then subsequently hospitalizations. I do remember vividly visiting him in the hospital and then connected with the profession of medicine. I was not on that track. I didn't really, that's not all my youth. I wanted to be a coder. I wanted to be a computer scientist. I wanted to do basically work with computers all my life. That's what my passion was. And then redirected all that energy to medicine.

Eric Topol (02:32):

Well, you sure did it well. And you graduated from one of the top medical schools, universities at American University of Beirut, and came to St. Louis where you basically have for now 24 years or so, went on to train in medicine and nephrology and became a leading light before the pandemic. You didn't know it yet, I guess, but you were training to be a pandemic researcher because you had already made the link back in 2016, as far as I know, between these protein pump inhibitors and kidney disease later, cardiovascular disease and upper GI cancers. Can you tell us, was that your first big finding in your work in epidemiology?

Ziyad Al-Aly (03:22):

Yeah, we started doing epi. I started doing epidemiology or clinical epi right after fellowship, trained with mentors and subsequently developed my own groups and my own funding. And initially our initial work was in pharmaco-epidemiology. We were very, very interested in figuring out how do we leverage this big data to try to understand the long-term side effects of medication, which was really not available in clinical trials. Most clinical trials for these things track them for maybe 30 days or at most for few months. And really long-term risk profile of these medications have not been characterized previously. So we did that using big data and then subsequently discovered the world of environmental epidemiology. We also did quite a bit of work and environmental linking air pollution to non-communicable disease. And in retrospect, reflecting on that now, I sort of feel there was training ground that was training wheel out, how to really optimize our thinking, asking the right question, the right question that matters to people addressing it rigorously using data and also communicating it the wider public. And that was my training, so to speak, before the pandemic. Yeah,

Eric Topol (04:37):

Yeah. Well, you really made some major, I just want to point out that even though I didn't know of your work before the pandemic, it was already momentous the link between air pollution and diabetes, the link of PPIs and these various untoward organ events, serious events. So now we go into the pandemic and what you had access to with the VA massive resource, you seize the opportunity with your colleagues. Had some of this prior work already been through that data resource?

Ziyad Al-Aly (05:18):

Yes, yes. Our work on PPI on adverse events of medications, including proton pump inhibitors, was all using VA data. And then our work using environmental epidemiology, linking air pollution to chronic disease was also using VA data. But we linked it with NASA data with sort of satellite data from NASA that capture PM 2.5. But NASA has these wonderful satellites that if a chemical is on earth and has a chemical signature that can actually see it from space and measure its concentration. So that data is actually all available free of charge. So what we did is I went to these massive databases at NASA and link them to our VA data, and then we're able to analyze the relationship between exposure to high levels of air pollution in the United States and then subsequent disease in veterans in our database.

Eric Topol (06:11):

That was ingenious to bring in the NASA satellite data. Big thinker. That's what you are. So now you are confronted with the covid exposure among what millions of veterans. Of course, you have controls and you have cases and you're now seeing data that says every system is being hit here and you write, you and your colleagues wrote papers on virtually every system, no less the entire long covid. What were the surprises that you encountered when you were looking at these data?

Initial Shock on Covid’s Non-Pulmonary Sequelae Identified

Ziyad Al-Aly (06:47):

I remember the initial shock and our first paper when we did our first paper and there was a systematic approach looking at all organ systems. We weren't expecting that because at that time we were thinking SARS-CoV-2 is a respiratory virus. We know respiratory virus may have some post-acute sequela and maybe cardiovascular systems, but we weren't really expecting to see hits in nearly every organ system. And remember when we first got the results from what then became our nature paper, our first paper in nature around this, I doubted this. I couldn't really believe that this is really true. I looked at the association with diabetes and I told Yen, my colleague here who's really absolutely, absolutely wonderful, told him, there must be a mistake here. You made an error. There's an error in a model for sure. This is not believable. That can't be like SARS-CoV-2 and diabetes.

(07:39):

This is impossible. There wasn't really an arrow in my brain that sort of linking SARS-CoV-2 diabetes. I doubted it. And we went back to the model, went back to the data, rebuilt the cohort, redid the whole experiment again with controls. The same thing happened again. I still was not believing it, and it was like, end, there is something wrong here. It's weird. It's strange. This is not how these things work. Again, from medical school, from all my education, we're not trained to think that viruses, especially respiratory viruses, have these myriad effects and all these organ systems. So I doubted i…

Eric Topol (00:00):

Hello, this is Eric Topol, and I'm thrilled to have a chance to have a conversation with Magdalena Skipper, who is the Editor-in-Chief of Nature. And a historic note. Back in 2018, she became the first woman editor of Nature in its 149 years, and only the eighth editor of all times. Having taken over for Philip Campbell, who had been previously the editor for 22 years, we're going to ask her if she's going to do 22 or more years, but we're going to have a fun conversation because there's so much going on in medical publishing, and I think, you know, that Nature is the number one cited science journal in the world. So, welcome, Magdalena.

Magdalena Skipper (00:41):

Thank you very much. Real pleasure to be here and chatting with you today, Eric. Thank you.

How COVID-19 Affected Nature

Eric Topol (00:47):

Well, you know, we're still, of course, in the pandemic world. It's obviously not as bad as it had been, but there's still things going on with new variants and Long Covid, and it's not, the virus isn't going away. But first thing I wanted to get into was how did Nature handle this frenetic craziness? I mean, it was putting out accelerated publications on almost a daily or weekly basis and putting out like a speed, velocity of the likes that we've not seen. This must have been really trying for the whole crew. What, what do you think?

Magdalena Skipper (01:29):

It was! And, you know, the first thing I, I think I will recognize two things at the same time. So the first one, as you say, at a time, such as the pandemic, but actually at any point when there is a, a new health emergency that is spreading, especially something as unknown, as new as, as it was the case with SARS-CoV-2. And of course, in the beginning, we really knew nothing about what we were facing if speed is of the essence, but equally what's truly important is of course, the rigor itself. So that combination of needing to publish as quickly as possible, but at the same time as rigorously evaluating the papers as possible, that was actually quite a challenge. And of course, you know, what we sometimes forget when we talk about, well, researchers themselves, but also editors and publishers is of course, as individuals, as human beings.

(02:33):

They are going through all the trauma, all the constraints associated with various lockdowns concerns about the loved ones, perhaps those ones who are in the care. You know, in many cases of course there would've been the elderly who are individuals would've been concerned by or indeed children, because of course, schools in so many places were. And all the while, while we were dealing with these very human, very ordinary daily preoccupations, we were very focused on the fact that we had a responsibility and a duty to publish papers and evaluate them as quickly as possible. It really was an extraordinary time. And, and you know, one other thing I should emphasize is, of course, it's not just the manuscript editors who evaluate the research, it's the reporters on my team as well who are going out of their the way to find out as much information to report as robustly, find as many sources to, to interview as possible.

(03:44):

And, and, you know, I also have to mention colleagues who work on production side of nature actually make Naturehappen, be published online on a daily and then of course weekly basis. And literally from one week to the next all our operations had to be performed from home. And it's really remarkable that the issue was not late. We published the issue, just as you know, from as lockdowns came in. And as it happens, the production side of Nature is mainly based in, in London. So most of that team effectively found themselves not being able to go to the office effectively from one day to the next. So it really was an extraordinary time and, and a time that as I said was, was a time of great responsibility. But looking back on it, I'm actually incredibly proud of, of my team, what, what they achieved

Eric Topol (04:47):

Did they hold up? I mean, they hadn't, they didn't get burnout from lack of sleep and lack of everything. Are they still hanging in there?

Magdalena Skipper (04:55):

So they are hanging in there. You'll be glad to hear. But I think, very importantly, we were there for one another insofar that we could be, of course, we were all at home remotely. We were not meeting, but we had virtual meetings, which were regular of course in as a whole team, but also in, in subgroups as we sub-teams, as we worked together, that human contact in addition to of course, loved ones and families and friends, that human contact in a professional setting was, was really, really necessary. And clearly what I'm describing was affected all of us one way or another. Sometimes there is a tendency not to remember. That also applies to editors, publishers, and of course researchers themselves. I mean, very clearly they were at the forefront of the issue facing the same problems.

Nature and Challenge of Generative A.I.

Eric Topol (05:57):

Well, a new challenge has arisen, not that the pandemic of course has gone away, but now we have this large language models of AI, Generative AI, which you've written editorials at Nature, which, of course, is it human or is it the machine? What do you think about that challenge?

Magdalena Skipper (06:19):

Well of course, you know, the way I like to think about it is AI, of course, broadly is, has been around for a very long time, a number of decades, right? And steadily over the last several years, we have seen AI emerge as a really powerful and important tool in research right across a number of disciplines. The reason why we are all talking about AI right now, and I really think all of us are talking about AI all the time, is, of course, specifically the emergence of generative AI, the large language models that, that you just mentioned. And they sort of burst onto the scene for all of us really last year in the autumn with chat GPT and GPT-4 and so on. But it's important to remember that, of course, when we talk about AI, there are other models, other approaches, and machine learning in general has been creating quite some revolution in research already.

(07:36): You know, probably the best example that will be familiar to many of the  listeners was of course Alpha Fold which, you know, Nature published a couple of years ago and, and has been really revolutionized structural biology. But, of course, there are many other examples which are now becoming developing much more rapidly, becoming much more, I would say, commonplace in, in research practice. You know, not just predicting structure from sequencing from sequence. And I say just so flippantly now, of course, it was such and it continues to be such an incredible tool. But of course now we have AI approaches, which actually suggest new protein design, new, new small molecule design. We've had in the last couple of years, we've had identification of new potential antibiotics that are effective against bacterial strains that have otherwise been resistant to any known antibiotics.

(08:48):

And, and of course, it's not just in biomedicine. Material science--I think it's very helpful, hopeful when it comes to, to AI tools as well. And then, and of course, generative AI indeed helps us in some of these contexts already. But I think your question perhaps was more focused on the publishing, the communication, the sort of output of, of research, which of course is also very important. In some way. The reason why I answered, I began to answer the question the way I did, is because I'm actually very excited about harnessing the power of AI in augmenting research itself. Helping navigate enormous data sets generate hypotheses to be tested finding new ways to advance projects. I think that's a very exciting opportunity. And we're just beginning to see the first applications of it.

(10:04):

Now, in terms of publishing you referred to some editorials that we wrote about thi…

Transcript

Eric Topol (00:00):

This is a real great opportunity to speak to one of the most impressive medical informaticists and leaders in AI in the United States and worldwide. Dr. John Halamka, just by way of background, John, his baccalaureate in Stanford was at U C S F/Berkeley for combined MD PhD trained in emergency medicine at U C L A. He went on to Harvard where he, for 20 years was the Chief Information Officer at Beth Israel Deaconess. And then in 2020 he joined Mayo Clinic to head its platform to help transform Mayo Clinic to be the global leader in digital healthcare. So welcome, John. It's so great to have you. And by the way, I want to mention your recent book came out in April, one of many books you've written, redefining the Boundaries of Medicine, the High Tech High Touch Path into the Future.

John Halamka (01:00):

Well, a thrilled to be with you today, and you and I need to spend more time together very clearly.

Eric Topol (01:06):

Yeah, I really think so. Because this is the first time we've had a one-on-one conversation. We've been on panels together, but that's not enough. We've got to really do some brainstorming, the two of us. But first I wanted to get into, because you have been on a leading edge of ai and Mayo is doing big things in this space, what are you excited about? Where do you think things are right now?

John Halamka (01:35):

So you and I have been in academic healthcare for decades, and we know there's some brilliant people, well-meaning people, but sometimes the agility to innovate isn't quite there, whether it's a fear of failure, it's the process of getting things approved. So the question of course is can you build to scale the technology and the processes and change policies so that anyone can do what they want much more rapidly? And so what's been exciting over these last couple of years at Mayo is we started with the data and we know that anything we do, whether it's predictive or regenerative, starts with high quality curated data. And so by de-identifying all the multimodal data of Mayo and then working with other partners around the world to create a distributed federated approach for anyone to train anything, suddenly you're empowering a very large number of innovators. And then you've seen what's happened in society. I mean, culturally, people are starting to say, wow, this ai, it could actually reduce burden, it could democratize access to knowledge. I actually think that yes, there need to be guidelines and guardrails, but on the whole, this could be very good. So here we have a perfect storm, the technology, the policy, the cultural change, and therefore these next couple of years are going to be really productive.

Implementing a Mayo Randomized AI Trial

Eric Topol (02:59):

Well, and especially at Mayo, the reason I say that is not only do they recruit you, having had a couple of decades of experience in a Harvard program, but Mayo's depth of patient care is extraordinary. And so that gets me to, for example, you did a randomized trial at Mayo Clinic, which there aren't that many of by the way in AI where you gave E C G reading power of AI to half the primary care doctors and the other half you didn't for determining whether the patients had poor cardiac function that is low ejection fraction. And now as I understand it, having done that randomized trial published it, you've implemented that throughout the Mayo Clinic system as far as this AI ECG support. Is that true?

John Halamka (03:56):

Well, right, and let me just give you a personal example that shows you how it's used. So I have an SVT [supraventricular tachycardia] , and that means at times my resting heart rate of 55 goes to one 70. It's uncomfortable. It's not life-threatening. I was really concerned, oh, may I have underlying cardiomyopathy, valvular disease, coronary artery disease. So Paul Friedman and Peter Newsworthy said, Hey, we're going to take a six lead ECG wearable, send it to your home and just record a bunch of data and your activities of daily living. And then we buy 5G cell phone. We'll be collecting those six leads and we'll run it through all of our various validated AI systems. And then we'll tell you based on what the AI suggests, whether you're at high risk or not for various disease states. So it says your ejection fraction 70%. Oh, good. Don't have to worry about that. Your likelihood of developing AFib 3% cardiomyopathy, 2% valvular disease, 1%. So bottom line is without even going to a bricks and mortar facility here, I have these validated algorithms, at least doing a screen to see where maybe I should get additional evaluation and not.

Eric Topol (05:12):

Yeah, well see what you're bringing up is a whole other dimension. So on the one hand that what we talked about was you could give the primary care doctors who don't read electrocardiograms very well, you give them supercharged by having a deep learning interpretation set for them. But on the other, now you're bringing up this other patient facing story where you're taking a cardiogram when somebody's perfectly fine. But from that, from having deep learning of cardiograms, millions of cardiograms, you're telling what their risks are that they could develop things like atrial fibrillation. So this is starting to span the gamut of what the phase that we went through or still going through, which is taking medical images, whether it's a cardiogram or a scan of some sort, and seeing things with machines that humanize really can't detect or perceive. So yeah, we're just starting to get out of the block here, John. And you've already brought up a couple of major applications that we were not even potentially used three, four or five years ago that Mayo Clinics leading the charge, right?

The Power of Machine Eyes

John Halamka (06:26):

Well, yeah, and let me just give you two quick other examples of these are in studies now, right? So they're not ready for active patient use. The animate GI product does an overread of endoscopy. And what we're finding is that the expert human, I mean anywhere in the world, expert humans miss about 15% of small polyps. They're just hard to see. Prep may not be perfect, et cetera. The machine misses about 3%. So that's to say a human augmented with overread is five times better than a human alone pancreatic cancer, my father-in-law died about 11 years ago of stage four pancreatic cancer. So this is something that I'm very sensitive about, very often diagnosed late, and you can't do much. What we've been able to see is looking at pancreatic cancer, early films that were taken, abdominal CT scans and these sorts of things, algorithms can detect pancreatic cancer  two years before it is manifested clinically. And so here's the ethical question I'll pose to you. I know you think about a lot of this Scripps Mayo, UCSF, Stanford, we probably have thousands and thousands of abdominal CTs that were read normal. Is it an ethical imperative as these things go through clinical trials and are validated and FDA approved to rerun algorithms on previous patients to diagnose disease we didn't see?

Eric Topol (08:03):

Well, that is a really big important question because basically we're relieving all this stuff on the table that doesn't get diagnosed, can't be predicted because we're not even looking for it. And now whether it's retina, that is a gateway to so many systems of the body, or as you're mentioning various scans like an abdominal CT and many others that like mammography for heart disease risk and all sorts of things that weren't even contemplated that machine eyes can do. So it's really pretty striking and upending cancer diagnosis, being able to understand the risk of any individual for particular types of cancer so that you can catch it at the earliest possible time when it's microscopic before it spreads. This, of course, is a cardinal objective. People don't die of cancer per se. They die of its metastasis, of course, for the most part. So that gets me now …

Transcript with Links

Eric Topol (00:00):

This is Eric Topol, and I'm so excited to have the chance to speak to Melanie Mitchell. Melanie is the Davis Professor of Complexity at the Santa Fe Institute in New Mexico. And I look to her as one of the real, not just leaders, but one with balance and thoughtfulness in the high velocity AI world of large language models that we live in. And just by way of introduction, the way I got to first meet Professor Mitchell was through her book, Artificial Intelligence, A Guide for Thinking Humans. And it sure got me thinking back about four years ago. So welcome, Melanie.

Melanie Mitchell (00:41):

Thanks Eric. It's great to be here.

The Lead Up to ChatGPT via Transformer Models

Eric Topol (00:43):

Yeah. There's so much to talk about and you've been right in the middle of many of these things, so that's what makes it especially fun. I thought we'd start off a little bit of history, because when we both were writing books about AI back in 2019 publishing the world kind of changed since . And in November when ChatGPT got out there, it signaled there was this big thing called transformer model. And I don't think many people really know the difference between a transformer model, which had been around for a while, but maybe hadn't come to the surface versus what were just the deep neural networks that ushered in deep learning that you had so systematically addressed in your book.

Melanie Mitchell (01:29):

Right. Yeah. Transformers are, were kind of a new thing. I can't remember exactly when they came out, maybe 2018, something like that, right from Google. They were an architecture that showed that you didn't really need to have a recurrent neural network in order to deal with language. So that was one of the earlier things, you know, and Google translate and other language processing systems, people were using recurrent neural networks, networks that sort of had feedback from one time step to the next. But now we have the transformers, which instead use what they call an attention mechanism where the entire text that the system is dealing with is available all at once. And the name of the paper, in fact was Attention is All You need. And that by attention is all you need they meant this particular attention mechanism in the neural network, and that was really a revolution and enabled this new era of large language models.

Eric Topol (02:34):

Yeah. And as you aptly pointed out, that was in, that was five years ago. And then it took like, oh, five years for it to become in the public domain of Chat GPT. So what was going on in the background?

Melanie Mitchell (02:49):

Well, you know, the idea of language models (LLMs) that is neural network language models that learn by trying to predict the next word in a, in a text had been around for a long time. You know, we now have GPT-4, which is what's underlying at least some of ChatGPT, but there was GPT-1 and GPT-2, you probably remember that. And all of this was going on over those many years. And I think that those of us in the field have seen more of a progression with the increase in abilities of these increasingly large, large language models. that has really been an evolution. But I think the general public didn't have access to them and ChatGPT was the first one that like, was generally available, and that's why it sort of seemed to appear out of nothing.

SPARKS OF ARTIFICIAL GENERAL INTELLIGENCE

Sentience vs Intelligence

Eric Topol (03:50):

Alright. So it was kind of the, the inside world of the computer science kinda saw a more natural progression, but people were not knowing that LLMs were on the move. They  were kinda stunned that, oh, look at these conversations I can have and how, how humanoid it seemed. Yeah. And you'll recall there was a fairly well-publicized event where a Google employee back I think last fall was, put on suspension, ultimately left Google because he felt that the AI was sentient. Maybe you'd want to comment that because that's kind of a precursor to some of the other things we're going to discuss,

Melanie Mitchell (04:35):

Right? So yeah, so one of the engineers who was working with their version of ChatGPT, which I think at the time was called LaMDA was having conversations with it and came to the conclusion that it was sentient, whatever that means, , you know, that, that it was aware that it had feelings that it experienced emotions and all of that. He was so worried about this and he wanted, you know, I think he made it public by releasing some transcripts of his conversations with it. And I don't think he was allowed to do that under his Google contract, and that was the issue.  tThat made a lot of news and Google pushed back and said, no, no, of course it's not sentient. and then there was a lot of debate in the philosophy sphere of what sentient actually means, how you would know if something is sentient. And it Yeah. and it's kind of gone from there.

Eric Topol (05:43):

Yeah. And then what was interesting is then in March based upon GPT-4 the Microsoft Research Group published this sparks paper where they said, it seems like it has some artificial general intelligence, AGI qualities, kind of making the same claim to some extent. Right?

Melanie Mitchell (06:05):

Well, that's a good question. I mean, you know, intelligence is one thing, sentience is another. There's a question of whether, you know, how they're related, right? Or if they're related at all, you know, and what they all actually mean. And these terms, this is one of the problems. Of course, these terms are not well-defined, but most, I think most people in AI would say that intelligence and sentience are different. You know something can be intelligent or act intelligently without having any sort of awareness or sense of self or, you know, feelings or whatever sentience might mean. So I think that the sparks of AGI paper from Microsoft was more about this, that saying that they thought GPT-4 four, the system they were experimenting with, showed some kind of generality in its ability to deal with different kinds of tasks. You know, and this, this contrasts with the old, older fashioned ai, which typically was narrow only, could do one task, you know, could play chess, could play Go, could do speech recognition, or could, you know, generate translations. But it, they couldn't do all of those things. And now we have these language models, which seemed to have some degree of generality.

The Persistent Gap Between Humans and LLMs

Eric Topol (07:33):

Now that gets us perfectly to an important Nature feature last week which was called the “Easy Intelligence Test that AI chatbots fail.” And it made reference to an important study you did. First, I guess the term ARC --Abstract and Reasoning Corpus, I guess that was introduced a few years back by Francois Chollet. And then you did a ConceptARC test. So maybe you can tell us about this, because that seemed to have a pretty substantial gap between humans and GPT-4.

Melanie Mitchell (08:16):

Right? So, so, so Francois Chollet is a researcher at Google who put together this set of sort of intelligence test like puzzles visual reasoning puzzles that tested for abstraction abilities or analogy abilities. And he put it out there as a challenge. A whole bunch of people participated in a competition to get AI programs to solve the problems, and none of them were very successful. And so what, what our group did was we thought that, that the original challenge was fantastic, but the prob one of the problems was it was too hard, it was even hard for people. And also it didn't really systematically explore concepts, whether a, a system understood a particular concept. So, as an example, think about, you know, the concept of two things being the same, or two things being different. Okay?

(09:25):

So I can show you two things and say, are these the same or are they different? Well, it turns out that's actually a very subtle question. 'cause when we, y…

Transcript with some hyperlinks

Eric Topol (00:00):

Hello, Eric Topol here. And what a privilege to have as my guest Al Gore, as we discuss things that are considered existential threats. And that includes not just climate change but also recently the concern about A.I. No one has done more on the planet to bring to the fore the concerns about climate change. And many people think that the 2006 film, An Inconvenient Truth, was the beginning, but it goes way back into the 1980s. So, Al it's really great to have you put in perspective. Here we are with the what's going on in Canada with more than 12 million acres of forest fires that are obviously affecting us greatly, no less the surface temperature of the oceans. And so many other signs of this climate change that you had warned us about decades ago are now accelerating. So maybe we could start off out, where are we with climate change and the climate reality?

The Good News on Climate Change

Al Gore (01:00):

Oh, well, first of all, thank you so much for inviting me to be on your podcast again, Eric. It's always a pleasure and especially because you're the host and we, we have very interesting conversations that aren't on the podcast. So, , I'm looking forward to this one. So, to start with climate you know, the old cliche, there's good news and bad news. Unfortunately, there's an abundance of bad news but there's also an awful lot of good news. Let me start with that first and then turn to the more worrying trends. We have seen the passage in the US last August of the largest and most effective best funded climate legislation passed by any nation in all of history. The so-called Inflation Reduction Act is an extraordinary piece of legislation.

(01:55):

 It's billed as allocating $369 billion to climate solutions. But actually, the heavy lifting in that legislation is done by tax credits, most of which are open-ended and uncapped, and a few without any time limits, most a 10-year duration. And the enthusiastic response to the legislation after President Biden signed it has now made it clear that that early estimate of 369 billion is a low-ball estimate, because Goldman Sachs, for example, is predicting that it will end up allocating 1.2 trillion to climate solutions. A lot of other investors and others using economic models are estimating more than a trillion. So, it's really a fantastic piece of legislation and other nations are beginning to react and respond and copy it. One month after that law was passed the voters of Australia threw out their climate denying government and replaced it with a climate-friendly government, which immediately then set about passing legislation that adopts the same goals as the US IRA and the Australian context.

(03:19):

And they stopped the biggest new coal mine there. And anyway, one month after that, in October, the voters of Brazil threw out their former president often called the “Trump of the Tropics” and replaced him with a new president, a former president who's a new president, who has pledged to protect the Amazon and the European Union in responding to the evil, evil and cruel invasion of Ukraine by Russia. And the attempted blackmail of nations in Europe, dependent on Russian gas and oil responded not by bending their knee to Vladimir Putin, but by saying, wait a minute, this makes renewable energy, freedom, energy. And so they accelerated their transition. And so these are all excellent signs and qualifies as good news. The other good news is not all that new, but it's still continuing to improve.

(04:28):

And that is the astonishing reductions in cost for electricity produced by solar and wind, and the reductions in cost for energy storage, principally in batteries and electric vehicles and a hundred other less well known technologies that are extremely important. We're in the midst of early stages of a sustainability revolution that has the magnitude of the industrial revolution, coupled with the speed of the digital revolution. And we're seeing it all over the place. It’s really quite heartening. One quick example last, the, the biggest single source of global warming pollution is the generation of electricity with gas and coal. Well, last year, if you look at all the new electricity generation capacity installed worldwide 90% of it was renewable. In India, 93% was solar and wind. And India's pledged not to give permits for any new coal burning plants for at least five years, which means never, probably because this cost reduction curve, as I mentioned, is still continuing downward electric vehicles, we're now seeing that the purchases have reached 15% of the market globally.

(05:56):

Norway's already at 50%. They've actually outlawed the sale of any new internal combustion engines. And indeed, many national and even municipal and state jurisdictions have prospectively served notice that they, you won't be able to buy them after a certain day, 2030,  in many cases and the auto companies and truck and bus companies have long since diverted their research money all their R & D is going into EVs now. And that's the second largest source of global warming pollution. I could go through the others, but I want, I'll just tell you that there is a lot of good news.

And the Bad News

Now, the bad news is we're still seeing the crisis get worse, faster than we're deploying all of these solutions. And, the inertia in our political and economic systems is partly a direct result of huge amounts of lobbying and campaign contributions and the century old net of political and economic influence built up by the fossil fuel industry.

(07:18):

And they're opposing every single solution at the state level, the local level, the national level, the international level. Now, this COP 28 [the 2023 United Nations Climate Change Conference] coming up at the end of the year in the United Arab Emirates is actually chaired by an oil and gas company CEO-- It's preposterous. And they already have in the last two COPS, more lobbyists registered as participants than all than the five or six largest national delegations combined. And we're seeing them really oppose this change. And meanwhile, the manifestations of the crisis are steadily worsening. You mentioned the fires in Canada that are predicted to burn all summer long. And I was in New York City last week, and you, you know, from the news stories it, it was horrific. I got there the day after the worst day, oh my God.

(08:21):

But I saw and heard from people just the tremendous problems that people have. It's also going on in Siberia, by the way, and these places that are typically beyond the reach of TV crews and networks that don't capture our attention unless something happens to blow the smoke to where we live. And that's what's happened here. But there are many other extremely worrying manifestations that aren't getting much attention. I do think we're going to solve this, Eric. I'm very optimistic, but the question is whether we will solve it in time. We are what's the right way to say this? We're tiptoeing through a minefield with tripwires and toward the edge of a cliff. I don't want to torture the metaphor, but actually there are several extremely dangerous threats to ecological systems that are in a state of balance now, and are being pushed out of their equilibrium state into a different format.

(09:35):

The ocean currents--we're already seeing it with the jet stream in the northern hemisphere. You may have seen on the weather maps. They're now using these a lot where it's getting loopier and more disorganized. That's what the last few winners has, has pulled these big loops, have pulled arctic air down into areas far south in the US and in other regions, by the way. And it’s making a lot of the extreme events worse. Now, we're entering an El Nino phase in the Pacific Ocean comes around every so often, and this one is predicted to be a strong one, and that's going to accentuate the temperature increase. You know, it was [recently] 1…

TRANSCRIPT

Eric Topol (00:00):

Hello, this is Eric Topol, and it's really a delight for me to welcome Hannah Davis who was the primary author of our recent review on Long Covid and is a co-founder of the Patient-Led Research Collaborative. And we're going to get into some really important topics about citizen science, Long Covid and related matters. So, Hannah, welcome.

Hannah Davis (00:27):

Thank you so much for having me.

Eric Topol (00:29):

Well, Hannah, before we get into it I thought because you had a very interesting background before you got into the patient led research collaborative organization with graphics and AI and data science. Maybe you could tell us a bit about that.

Hannah Davis (00:45):

Sure. Yeah. Before I got sick, I was working in machine learning with a particular focus on generative models for art and music. so I did some projects like translating data sets of landscapes into emotional landscapes. I did a project called The Laughing Room, where there was a room and you went in and the room would listen to you and laugh if it thought you said something funny, . and then I did a lot of generative music based on sentiment. So I, I did a big project where I was generating music from the sentiment of novels and a lot of kind of like critical projects, looking at biases in data sets, and also curating data sets to create desired outcomes in these generative models.

Eric Topol (01:30):

So, I mean, in a way again, you were ahead of your time because that was before ChatGPT in November last year, and you were ahead of the generative AI curve. And here again, you're way ahead in in the citizen science era as it particularly relates to the pandemic. So, I, I wonder if you could just tell us a bit I think it was back, we go back to March, 2020. Is that when you were hit with Covid?

Hannah Davis (01:59):

Yes.

Eric Topol (02:00):

And when did you realize that it wasn't just an acute phase illness?

Hannah Davis (02:06):

 for me, honestly, I was not worried at all. I, my first symptom was that I couldn't parse a text message. I just couldn't read it, thought I was tired. an hour later, took my temperature, realized I had a fever, so that's when I kind of knew I was sick. but I really just truly believed the narrative I was going to get better. I was 32 at the time. I had no pre-existing conditions. I just was, you know, laying around doing music stuff, not concerned at all. And I put a calendar note to donate plasma two weeks out, and I was like, you know, I'm going to hit that mark. I'm going to donate plasma, contribute, it'll be fine. And that day came and went. I was still, you know, pretty sick with a mild case. You know, I didn't have to be hospitalized.

(02:49):

I didn't have severe respiratory symptoms. but my neurological symptoms were substantial and did increase kind of over time. And so I, I was getting concerned. Three weeks went by, still wasn't better. And then I read Fiona Lowenstein’s op-ed in the New York Times. They were also very young. They were 26 at the time, they had been hospitalized, and they had this prolonged recovery, which we now know as Long Covid. and they started the Body Politic Support Group joined that saw thousands of people with the same kind of debilitating brain fog, the same complete executive functioning loss, inability to drive, forgetting your family members' names who were all extremely young, who all had mild cases. and that's kind of when I got concerned because I realized, you know, this was not just happening to me. This was happening to so many people, and no one understood what was happening.

Eric Topol (03:49):

Right. extraordinary. And, and was a precursor, foreshadowing of what was to come. Now, here it is, well over three years later. And you're still affected by all this, right?

Hannah Davis (04:02):

Yes. Pretty severely.

Eric Topol (04:04):

Yeah. And I learned about that when I had the chance to work with you on the review. You were the main driver of this review, and I remember asking you, because I, I didn't know anyone in the world that was tracking Long Covid like you and to be the primary author. And then you sent this outline, and I had never seen an outline in all my years in academic medicine. I never saw an outline like this of the review. I said, oh my God, this is incredible. So I know that during that time when we worked on the review together, along with Lisa McCorkell and Julia Moore Vogel, that, you know, there, there were times when you couldn't work on it right there, there were just absolutely, you would have some good days or bad days. And, and that's the kind of, is that kind of the way is, how it goes in any given unit time?

Hannah Davis (04:55):

I think generally, I, I communicated as like 40% of my function is gone. So, like, I used to be able to have very, very full days, 12 hour days would work, would socialize, would do music, whatever. you know, I, I have solidly four functional hours a day. on a good day, maybe that will be six. On a bad day, that's zero. And when I push myself by accident, I can get into a crash that can be three to seven days easily. Hmm. and then I'm, then I'm just not, you know, able to be present. I don't feel here. I don't feel cognitively able, I can't drive. And then I'm just completely out of the world for a bit of time.

Eric Topol (05:35):

Yeah. Wow. So back in the early days of when you were first got sick and realized that this was not going to just go away, you worked with others to form this Patient -Led Research Collaborative organization, and here you are, you didn't have a medical background. You certainly had a data science and computing backgrounds. But what were your thoughts? I mean, citizen science has taken on more of a life in recent years, certainly in the last decade. And here there's a group of you that are kind of been leading the charge. we'll get to, you know, working with RECOVER and NIH in just a moment. But what were your thoughts as to whether this could have an impact at working with these, the other co-founders?

Hannah Davis (06:27):

I think at first we really didn't realize how much of an impact we were going to have. The reason we started collecting data in the first place really was to get answers for ourselves as patients. You know, we saw all these kind of anecdotes happening in the support group. We wanted to get a sense of which were happening the most at what frequency, et cetera. and it really wasn't until after that when like the CDC and WHO started reaching out, asking for that data, which was gray literature at the time that we kind of realized we needed to formalize this and, and put out an official paper which was what ended up being the second paper. But the group that we formed really is magical, I think like, because the primary motivator to join the group was being sick and wanting to understand what was happening. And because everyone in the group only has the kind of shared experience of, of living with Long Covid, we ended up with a very, very diverse group. Many, many different and I think that really contributed to our success in both creating this data, but also communicating and, and doing actionable policy and advocacy work with it.

Eric Topol (07:42):

Did you know the folks before? Or did you all come together because of digital synapses?

Hannah Davis (07:47):

Digital synapses? I love that. Absolutely. No, we didn't know each other at all. they're now all, you know, they're my best friends by far. you know, we've been through this, this huge thing together. but no, we didn't meet in person until just last September, actually. And many of them we still haven't even met in person. which makes it even more magical to me.

Eric Topol (08:13):

Well, that's actually pretty extraordinary. So together you've built a formidable force to stand up for the millions and millions of people. As you wrote in the review, 65 million people around the world who are suffering in one way or another from Long Covid. So just to c…

Link to the book: The AI Revolution in Medicine

Link to my review of the book

Link to the Sparks of Artificial General Intelligence preprint we discussed

Link to Peter’s paper on GPT-4 in NEJM

Transcript (with a few highlights in bold of many parts that could be bolded!)

Eric Topol (00:00):

Hello, I'm Eric Topol, and I'm really delighted to have with me Peter Lee, who's the director of Microsoft Research and who is the author, along with a couple of colleagues for an incredible book called The AI Revolution in Medicine, GPT-4 and Beyond. Welcome, Peter.

Peter Lee (00:20):

Hello Eric. And thanks so much for having me on. This is a real honor to be here.

Eric Topol (00:24):

Well, I think you are in the enviable position of having spent now more than seven months looking at GPT-4’s S capability, particularly in the health and medicine space. And it was great that you recorded that in a book for everyone else to learn because you had such a nice head start.  I guess what I wanted to start with is, I mean, it's, it's a phenomenal book. I [holding the book up], this prop. I can't resist

Peter Lee (00:52):

Eric Topol (00:53):

When, when I got it, I, I couldn't, I stayed up most of the night because I couldn't put it down. It was, it is so engrossing. But when you, when you first got your hands on this and started testing it, what were, what were your initial thoughts?

Peter Lee (01:09):

Yeah. I, let me first start by saying thank you for the nice words about the book, but really, so much of the credit goes to the co-authors, Carey Goldberg and Zach Kohane and Corey in particular took my overly academic writing. I suspect you have the same kind of writing style as well as Zach's pretty academic writing and helped turn it into something that would be approachable to non-computer scientists and as she put it, as much as possible as a page turner. So I'm glad that her work helped make the, the book an easy read. I,

Eric Topol (01:54):

I want to just say you're very humble because the first three chapters that you wrote yourself were clearly the, the best ones for me. Anyway. I don't mean to interrupt, but it, it, it is an exceptional book, really.

Peter Lee (02:06):

Oh thank you very much. It means a lot. Hearing that from you. You know, my own view is that the, the best writing and the best analyses and the best ideas for applications or not of this type of technology in medicine are yet to come. But you're right that I did benefit from this seven-month head start. And so, you know, I think the timing is, is very good. but I'm hoping that much better books and much better writings and ideas will come, you know, when you start with something like this, I, I suspect, Eric, you had the same thing. you start off with a lot of skepticism and I, in fact, I sort of now made light with this. I talk about the nine stages of grief that you have to go through.

(02:55):

 I was extremely skeptical. Of course, I was very aware of GPT 2, GPT 3 and GPT 3.5. I understand, you know, what goes into those models really deeply. and so some of the claims, when I was exposed to the early development, GPT-4 just seemed outlandish and impossible. So I, I was, you know, skeptical, somewhat quietly skeptical. We've all been around the block before and, you know, we've heard lots of AI claims and I was in that state for maybe more than two weeks. And then I started to become in that two weeks annoyed, because I know that some of my colleagues like falling into what I felt was the trap of getting fooled by this technology. And then that turned into frustration and fear. I actually got angry. And one colleague who I won't name I've since had to apologize because then I into the phase of amazement because you start to encounter things that you can't explain that this thing seems to be doing that turns into joy.

(04:04):

 I remember the exhilaration of thinking, wow, I did not think I would live long enough to see a technology like this. and then intensity, There was a period of about three days when I didn't sleep, I was just experimenting. Then you run into some limits and some areas of puzzlement and that's a phase of chagrin. And then real dangerous missteps and mistakes that this system can make that you realize might end up really hurting people. and then, you know, ChatGPT gets released and to our surprise it catches fire with people. And we learn directly through communications that some clinicians are using it in clinical settings. And that heightens the concern. And I, I can't say I'm in the ninth stage of enlightenment yet, but you do become very committed to wanting to help the medical community get up to speed and to be in a position to take ownership of the question of whether, when, and how a technology like this should be used. and that was really the motivating force behind the book. And it, it was really that journey. And that journey also has given me patience with everyone else in the world, because I realize everyone else in the world has to go through those same nine, nine stages.

Eric Topol (05:35):

Well, those stages that you went through are actually a great way to articulate this pluripotent technology. I mean, I think you, you touched on that chat. ChatGPT was released November 30th and within 90 days had a billion distinct users, which is beyond anything in history. And then of course, this transcended that quite a bit as you showed in the book coming out in you know, just a very short time in March. right. And I think a lot of people want access to GPT-4 because they know that there is this jump in its capabilities. But the book starts off after Sam Altman's forward, which was also nice because he said, you know, this is just an early, as you pointed out there, there's a lot more to come in the large language model space.

(06:30):

But the grabber to me was this futuristic, this second year medical resident who's using an app on the phone to get to the latest GPT to help manage her patient, and then all the other things that it's doing to check on her patients and do all the things that are the tasks that clinicians don't really want to do, that they need help with. And that just grabs you as to the futuristic potential, which may not be so far away. And I think then you get into the nuts and bolts, but one of the things that I think is a misnomer that you really nailed is how you say it isn't just that it generates, but it really is great at editing and analyzing. And here it's, it's called generative AI. Can you, can you expound on that? And it's unbelievable conversationalist capability.

Peter Lee (07:23):

Yeah. you know, the term Generative AI, I tried for a while to push back on this, but I think it's just caught on and I've given up on that. And I get it. You know, I, I think especially with ChatGPT it's of course reasonable for the public to be, you know infatuated with a thing that can write love letters, write poetry and that generative capability. and of course, you know school children writing their essays and so on this way. But as you say one thing we have discovered through a lot of experimentation is it's actually somewhat of a marginal generator of text. I would not say at all. That is, it is not as good a poet as good human poets. It's not the, you know, people have programmed GPT-4 to try to write whole novels and it can do that,

(08:24):

they aren't great. and it's a challenge, you know within Microsoft, our Nuance division has been integrating GPT-4 to help write clinical and encounter notes. and you can tell it's just hitting at the very limits of the capabilities in and of the intelligence of GPT-4 to be able to do that well. But one area where it really excels is in evaluating or judging or reviewing things. And we've seen that over and over again. in chapter three. You know, I have this example of its analysis of some contemporary poetry which is just stunning in its kind of insights and its use of metaphor and allegory. And but then in other situation…

Transcript

Eric (00:00):

Okay. Hello, this is Eric Topol and this is a rare privilege for me to interview my favorite epidemiologist, Dr. Michael Osterholm. He is the Regents Professor of the University of Minnesota. He's director of CIDRAP, which is certainly one of the leading entities around the world for public health. And, we've been friends for the last few years, which we'll we'll talk about. So, welcome Michael. Such a great privilege to have you today.

Michael (00:31):

Well, thank you, the honor, really is mine. As I have shared with you and others know very well--you have been a real mentor to me and many others during this pandemic. And, I could never repay you adequately for all that you've helped teach me throughout these last three years. It's been immeasurable.

Eric (00:49):

No, if you're too kind, I think it's much different. The opposite way. I've learned so much from you because this isn't my area, as you well know. I thought we'd start with, of course, right now things are relatively good for the pandemic in the United States and mostly around the world, with relatively less cases, less hospitalizations and deaths. But obviously still people are getting infected. And maybe you can tell us about the recent case that you went through that would be enlightening.

End of the Pandemic?

Michael (01:28):

Yeah, I think we're all trying to understand when the pandemic ends. And, as we've discussed many times before, we'll probably know that about a year after it ends, then we'll say, yep, that was the end of it. Don’t for a moment think that at the end means that there won't be cases. You know, for every infectious agent that we think of when causing a pandemic, they still come back, whether it be influenza, or potentially coronaviruses. They will, they will continue to circulate. It's a matter of how many cases occur, how many people die. And I think that's an important point. There isn't really a definition for when a pandemic ends. It's, I guess it's just when you feel like it's over. And clearly the world has come to that conclusion already. You don't need a, an epidemiologist or a politician to tell 'em that the pandemic's over that they feel that we're still seeing about 165 deaths a day in this country from Covid.

(02:24):

So it's hardly gone away completely. But we do have to acknowledge it. Most of those deaths are older individuals, people who have not been vaccinated recently with bivalent boosters. And in that regard, we could surely even reduce the illnesses further. I don't have any faith right now in the surveillance systems that have been set up to look at cases around the world. We've pretty much dismantled that. We are not testing people that we results in reports being made to public health agencies, whether in this country or anywhere else in the world. So I really look at two other things. One is deaths. And even they're realizing that still is a challenge in terms of how complete death reporting is due to covid. But then the other thing we're looking at, which has been really, you might say, public health revolution during the pandemic, and I say revolution cause it's really changed things.

(03:19):

And that is the issue of wastewater surveillance. And we've been able to ascertain in many areas of the world, in fact, with using wastewater surveillance, a much better sense of how much virus is in the community. And so, just in following with your very thoughtful comment about case numbers dropping, that's exactly what we're seeing in most locations in this country too. We, for example, here in the Minneapolis St. Paul area, have seen a dramatic decrease in wastewater activity in the last two months. So I think we're in a place right now where I can hope it'll only get better. On the other hand, you know, I have a lot of respect for this virus, and frankly, we all ought to have a lot of humility. We don't know if another variant will emerge that with, given how much immunity we have in our population will somehow break through that and cause increase in surgeon cases or whether this will become kind of the norm and we'll see less and less.

On Getting Covid

(04:16):

Now, you asked me about my case. Yeah. I have to say that, I speak about this with, with really some trepidation in the sense that I was not gonna get this. I had and very faithful throughout the course of the pandemic, where in my N 95 respirator when I went out and about, I had been fit tested. In addition, when we finally did socialize in our home, we had a, what became affectionately known as the Osterholm Home Rule. You could not have had known contact with someone of the, with Covid in the five previous days. You could have no symptoms yourself on the day of, and you had to test negative bilateral flow test within three to four hours of coming. And we would entertain small four, the six party, parties, and it was going wonderful.

(05:07):

And then on March 10th, the night of March 10th, a colleague from work came over with Fern and myself. Three of us had dinner. We went down our elevator in our building here, which were 31 stories up. No one else is in the elevator. And then we proceeded to go to a very small music venue where we wore N95s. We were some distance from any other people, and we were there for an hour and 45 minutes. And, literally two days later, almost 48 hours later, all three of us developed symptoms. None of us converted for another 24 hours. And then at that point, we all three tested lateral flow positive.. We all three took Paxlovid. I took it and was starting to feel better after that fifth day.

(05:59):

And then I kind of crashed and at that point, I got a second, , five day course of Paxlovid and started to feel better. And, I'm you know, was very happy to have this behind me. However, over the course of the last 10 days, I have really had significant fatigue. You know I'm not one that sleeps a lot But, I can tell you there are multiple times in a day where I'm doing something like even doing what I'm doing right now where I just feel like I just need to fall asleep. It’s been really a challenge. The other thing that happened, which was in retrospect a little bit more concerning than I realized at the time, there was a period at about day 10 to 14 into my illness, I started losing my memory on many, many things of, you know, importance.

(06:53):

I couldn't, for example, tell you what was that drink that is: a champagne, orange juice combination. I couldn't find the word mimosa if my life depended on it. If somebody asked me who was in sleepless in Seattle, I had to think about now the movie who was in it. I couldn't remember. And I mean, in retrospect, I wasn't that concerned thinking, ah, it's not that bad. And it was actually quite remarkable. This lasted about two and a half, three weeks. And now I think, I think at least according to those around me, I have gained most of my memory back. But now I have the fatigue picture. So, as much as I don't know where I picked up the virus, all three of us picked it up. And as much asI feel like I have survivor's guilt right now in the sense that, you know, I'm not that concerned about getting infected in a public exposure given I probably have some pretty good protection, at least for a few more weeks. But nonetheless,  I think this potential fatigue issue is really a challenge.

Eric (07:52):

Yeah. The things that you're bringing up with this, like for example, I know you had had, the initial series and three boosters including the bivalent. Was that sometime in September last year, or,

Michael (08:04):

Yeah, it was seven and a half months before,

Eric (08:07):

Yeah. So,

Michael (08:07):

So, so that was, and I tried to get it at six months in the second. But in Minnesota we actually have a registry. And so it's not just your white card that, you know, you could do it. And it wasn't, I was trying to do something illegal, but you know, this vaccine's just sitting there. So I tried to get another bivalent at six mon…