[0.0][S02] I'm hoping AI contributes to bringing back[3.11] [3.11][S02]the joy of practicing medicine.[4.677] [4.677][S01] So wait, it actually works then.[6.51] [6.51][S02] Yes, it does.[6.89] [6.89][S01] You record a cough and it[7.73] [7.73][S01]can tell if you've got TB.[9.022] [9.022][S02] In short, yes.[10.23] [10.23][S02]By combining those modalities, by getting[13.61] [13.61][S02]to a deeper understanding at the cellular level of what's[17.81] [17.81][S02]going on, we'll finally be able to crack, for example,[21.6] [21.6][S02]triple-negative breast cancers or more advanced cancers[25.07] [25.07][S02]that, today, we don't have good solutions for.[27.652] [27.652][S01] Are you concerned that people[29.36] [29.36][S01]are trying to diagnose themselves with generative AI?[31.676] [31.676][S02] Yeah.[32.509] [32.509][S02]I mean, I would say, even before, you really[34.85] [34.85][S02]shouldn't do that, right?[35.93] [35.93][S02]You shouldn't play your own doctor.[38.635] [38.635][MUSIC PLAYING][41.545] [42.53][S01] Welcome back to "Google DeepMind, The Podcast."[45.18] [45.18][S01]I'm Professor Hannah Fry.[46.74] [46.74][S01]In this episode, I'm talking to Joelle Barral,[49.53] [49.53][S01]Senior Director of Research at Google DeepMind,[52.32] [52.32][S01]about AI for health.[54.54] [54.54][S01]Now, for years, we have joked about how the internet is[57.11] [57.11][S01]the hypochondriac's best friend, capable of turning[59.94] [59.94][S01]a small headache into a terminal illness and vice versa,[63.45] [63.45][S01]with a quick search and a click of a button.[66.13] [66.13][S01]But quietly, behind the scenes, the role[69.36] [69.36][S01]of algorithms in medical care has been shifting.[72.19] [72.19][S01]We've already talked a lot on this podcast[74.16] [74.16][S01]about the impact of AI on drug discovery and research[77.22] [77.22][S01]into proteins.[78.49] [78.49][S01]But now, AI promises to change diagnosis and treatment, too.[84.1] [84.1][S01]Welcome to the podcast, Joelle.[86.04] [86.04][S01]I mean, there have been some pretty big changes already[89.4] [89.4][S01]with AI.[90.85] [90.85][S01]Should we expect that health care[92.67] [92.67][S01]will look very different in 10 or 15 years[95.1] [95.1][S01]time to how it looks now?[96.217] [96.217][S02] Absolutely.[97.3] [97.3][S02]I believe so.[97.96] [97.96][S02]I think health care is really posed[100.83] [100.83][S02]to be drastically changed with AI,[103.3] [103.3][S02]but it may not look too different.[106.5] [106.5][S02]I like to think of-- or at least I picture health care[109.29] [109.29][S02]as a fairly sticky ecosystem.[111.45] [111.45][S02]So 10, 15 years from now, we will likely still[116.1] [116.1][S02]go to our primary care doctor and then follow through[121.36] [121.36][S02]with specialist visits, et cetera.[123.79] [123.79][S02]But each of them is going to be augmented, if you wish,[127.42] [127.42][S02]by an AI agent.[128.66] [128.66][S02]So underneath, the system will be very different,[131.8] [131.8][S02]but it may not appear as different from what[134.89] [134.89][S02]we know today.[135.603] [135.603][S01] How long have you been[137.02] [137.02][S01]working in AI in health care?[138.502] [138.502][S02] Oh, wow.[139.46] [139.46][S02]My whole career, I would say.[140.74] [140.74][S02]I was still a student when, I think it was CS229,[145.27] [145.27][S02]the class on machine learning was taking off.[148.43] [148.43][S02]And I remember working with--[150.76] [150.76][S02]I was further along in my PhD, but working with students[154.06] [154.06][S02]on how to segment the larynx.[156.612] [156.612][S02]And AI was this new tool that could really[159.19] [159.19][S02]help us do that better.[160.85] [160.85][S02]And I haven't stopped ever since.[163.33] [163.33][S01] But why this area for you?[164.93] [164.93][S01]What's the appeal of it for you?[166.263] [166.263][S02] In health care, you're always challenged, right?[168.888] [168.888][S02]There are lots of things that we don't do that well[171.43] [171.43][S02]or you don't have infinite data.[173.23] [173.23][S02]And so you're never perfect.[175.07] [175.07][S02]So you're always trying to leverage the best tools[177.71] [177.71][S02]to do what you're trying to do.[180.11] [180.11][S02]And then I joined Google a decade ago[182.69] [182.69][S02]to work on surgical robotics.[184.92] [184.92][S02]And I remember vividly.[186.21] [186.21][S02]It was the beginning of us really[188.36] [188.36][S02]realizing that, with images, anything that a human could[192.77] [192.77][S02]do in terms of interpreting those images,[195.63] [195.63][S02]machines were going to be able to do.[198.03] [198.03][S02]And so I sat down with my surgeon colleague,[201.38] [201.38][S02]and we took 100 cholecystectomies,[204.27] [204.27][S02]which are when you remove the gallbladder.[206.66] [206.66][S02]We manually segmented livers and gallbladders.[210.81] [210.81][S02]And I fed a pretty simple neural net[214.07] [214.07][S02]to see whether the algorithm would be able to decipher[217.1] [217.1][S02]between those two things.[218.61] [218.61][S02]And I remembered being incredibly surprised, actually,[221.1] [221.1][S02]when it did that perfectly.[222.75] [222.75][S02]Because most of the other tools we had at our disposal[226.04] [226.04][S02]were never perfect.[227.45] [227.45][S02]It did the job, but not always.[229.29] [229.29][S02]Now, livers and gallbladders really don't look the same.[233.16] [233.16][S02]It's a very easy task.[234.98] [234.98][S02]Any student will be able to tell you which one is which.[238.73] [238.73][S02]But still, that really, for me, meant, OK, now we[242.47] [242.47][S02]have something that will be infinitely more[245.23] [245.23][S02]powerful than any of the algorithms I had been working on[250.15] [250.15][S02]previously.[251.06] [251.06][S02]And people forget, right?[252.22] [252.22][S02]But when I was still a student, we didn't know--[255.29] [255.29][S02]it was hard with computer vision to decipher a QR code.[259.315] [259.315][S02]That was where we were back then.[262.34] [262.34][S02]And so, again, AI has really opened up[268.096] [268.096][S02]a world of capabilities far beyond anything[272.74] [272.74][S02]we could envision before.[274.365] [274.365][S01] OK.[274.99] [274.99][S01]Well, let's start off with some of the examples of places[277.75] [277.75][S01]where AI has already made a lot of headway.[280.46] [280.46][S01]I'm thinking about diagnosis here and maybe[282.55] [282.55][S01]medical imaging in particular.[284.18] [284.18][S01]Talk to me a bit about what's been going on.[286.1] [286.1][S02] Yeah.[286.55] [286.55][S02]We've seen a lot of that over the last 10 years.[288.55] [288.55][S02]That's what we typically call "narrow AI."[290.75] [290.75][S02]Narrow because it's solving one task.[293.175] [293.175][S02]You mentioned medical imaging.[294.62] [294.62][S02]So you can take a chest X-ray, or an MRI,[297.83] [297.83][S02]or we've done also diabetic retinopathy work.[301.83] [301.83][S02]Those are already tools that have been FDA-cleared,[305.1] [305.1][S02]for example, and deployed in the clinic.[307.02] [307.02][S02]And they are augmenting radiologists in the sense[310.58] [310.58][S02]that, for those specific tasks, the machine[313.28] [313.28][S02]does a pretty damn good job at annotating[317.06] [317.06][S02]the image for the physician.[318.69] [318.69][S01] Let me make sure I understand this then.[320.07] [320.07][S01]OK.[320.57] [320.57][S01]So if we take diabetic retinopathy as an example,[323.76] [323.76][S01]this is people who have diabetes and, potentially, it's[327.8] [327.8][S01]a cause of blindness.[328.757] [328.757][S01]Is that right?[329.34] [329.34][S02] Absolutely.[330.423] [330.423][S02]It's the leading cause of preventable blindness worldwide.[333.15] [333.15][S02]And it's pretty simple.[335.22] [335.22][S02]So you take an image of the back of the eye with a fundus camera,[339.78] [339.78][S02]and that gives you an image of the retina that[343.16] [343.16][S02]then ophthalmologists will grade on a scale from 1 to 5[347.45] [347.45][S02]to indicate whether patients have[350.54] [350.54][S02]no disease, moderate disease, or severe disease.[355.36] [355.36][S02]And what was striking when we started working on that[359.22] [359.22][S02]particular problem was the fact that it was actually really hard[363.72] [363.72][S02]to get to what we call "ground truth," meaning,[365.76] [365.76][S02]as we're training our machine learning models to learn a task,[370.69] [370.69][S02]we first need to provide them with a set of ground-truth[375.15] [375.15][S02]images.[376.23] [376.23][S02]That means the pairs of the image and the label.[380.23] [380.23][S02]And for that particular task, we asked maybe 1, 2,[385.18] [385.18][S02]3 ophthalmologists, and often we got different answers.[388.99] [388.99][S02]And I think one thing we did particularly well[391.65] [391.65][S02]was to be very rigorous in trying[394.47] [394.47][S02]to establish ground truth.[396.31] [396.31][S02]And so we ended up working with 50--[398.31] [398.31][S02]5-0-- ophthalmologists.[400.06] [400.06][S02]And we realized that, actually, on some images,[402.79] [402.79][S02]they really didn't agree with each other.[405.63] [405.63][S02]Some would have given 1, 2, 3.[408.125] [408.125][S02]Each one of the scores could have potentially[410.61] [410.61][S02]been the diagnosis a patient would have received[413.05] [413.05][S02]because patients only see one.[414.46] [414.46][S02]They don't go and have their images graded by--[416.77] [416.77][S01] 50 times.[417.645] [417.645][S02] --50 people.[417.98] [417.98][S02]Yeah.[418.84] [418.84][S01] Boy.[418.915] [418.915][S01]So some ophthalmologists would say,[420.373] [420.373][S01]this is a mild or no disease, and others[422.71] [422.71][S01]would say all the way up to severe, on the same image.[425.54] [425.54][S02] Precisely, which sometimes is not too surprising,[428.39] [428.39][S02]meaning that most ophthalmologists-- and that's[430.87] [430.87][S02]true for most physicians-- see what's in their community.[435.37] [435.37][S02]And the rare cases, by definition,[438.078] [438.078][S02]they don't see them very often.[439.37] [439.37][S02]So it can get very hard to properly annotate an image,[443.17] [443.17][S02]if you don't see that type very often.[445.168] [445.168][S01] Well, how do you do that, then?[446.96] [446.96][S01]If even the experts don't agree, how[449.23] [449.23][S01]do you decide what the correct answer is?[451.82] [451.82][S02] Well, that's exactly why we had to go to 50.[454.4] [454.4][S02]Then you have this consensus among all ophthalmologists.[457.79] [457.79][S02]And sometimes you go to even deeper experts[460.72] [460.72][S02]to really get to what is the correct label for that image.[464.39] [464.39][S02]And that way, you properly train your algorithms.[466.96] [466.96][S02]And then we also did a lot of rigorous testing[469.99] [469.99][S02]to make sure that we had algorithms in which we could--[472.912] [472.912][S02]I mean, which we could trust.[474.12] [474.12][S01] But then I suppose, on the flip side of that,[476.07] [476.07][S01]even though that makes the labeling quite difficult,[478.41] [478.41][S01]if you know that experts are disagreeing,[481.83] [481.83][S01]then actually you can create something[484.07] [484.07][S01]that is a new gold standard really, better than the experts.[487.697] [487.697][S02] Absolutely.[488.78] [488.78][S02]And I think that's where the AI has really its role to play.[491.875] [491.875][S02]It's for tasks that it is really good at that are really not[496.82] [496.82][S02]very easy to be done by a single human.[501.26] [501.26][S01] I seem to remember reading this paper about where[505.55] [505.55][S01]some researchers had all of these images[507.41] [507.41][S01]of the back of the eye and were like, you know what?[509.91] [509.91][S01]Let's just see, for fun, if you can[512.72] [512.72][S01]tell the sex of the patient based on the blood vessels[515.99] [515.99][S01]at the back of the eye.[517.169] [517.169][S01]And no ophthalmologist in the world, I think,[519.32] [519.32][S01]could do this reliably, but the AI can.[523.427] [523.427][S02] Yeah, absolutely.[524.76] [524.76][S02]It was very interesting to then interrogate those images[528.53] [528.53][S02]for other things than just diabetic retinopathy[531.6] [531.6][S02]and see that the model performed better than flipping a coin.[536.62] [536.62][S02]And for some things--[538.2] [538.2][S02]I'm not sure you need a model to decipher[541.11] [541.11][S02]the sex of your patient.[542.32] [542.32][S01] [LAUGHS][543.27] [543.27][S02] But I do think for indications[546.12] [546.12][S02]of potential cardiac disease, for example,[548.44] [548.44][S02]that can be a useful tool.[551.32] [551.32][S02]Because if you're going to do that screening[553.26] [553.26][S02]for diabetic retinopathy anyway, but now you[556.29] [556.29][S02]have a test that can also provide you[558.15] [558.15][S02]with additional information, you could potentially[562.44] [562.44][S02]screen for more diseases.[564.22] [564.22][S02]So, indeed.[564.97] [564.97][S02]And it was both interestingly scientifically[568.72] [568.72][S02]to realize how those images contained a lot more information[572.46] [572.46][S02]than we initially thought, and with, potentially,[576.42] [576.42][S02]also applications that could derive from that.[578.74] [578.74][S01] Well, does it imply maybe[580.38] [580.38][S01]that even though you've created an algorithm[583.08] [583.08][S01]to do this narrow task, actually there is potential for the AI[586.83] [586.83][S01]to help improve the overall understanding of the human body?[591.27] [591.27][S02] I would answer maybe in two ways.[593.27] [593.27][S02]The first is, in radiology, there's this thing called[596.53] [596.53][S02]"incidental findings."[598.6] [598.6][S02]Because it's very often that, if you[600.52] [600.52][S02]screen me head to toe with MRI right now, you'll find things.[604.93] [604.93][S02]You'll find nodules, things that are abnormal.[608.06] [608.06][S02]They will get me worried.[609.32] [609.32][S02]But actually, we never did clinical trials[612.19] [612.19][S02]to see whether having that thing somewhere is good or bad.[615.26] [615.26][S02]And many people live with those things[617.29] [617.29][S02]forever, without any issues.[619.43] [619.43][S02]So you don't really want to do that type of screening that[622.33] [622.33][S02]will get everyone worried.[625.03] [625.03][S02]And the other thing I would say is, when you do a blood test,[629.75] [629.75][S02]you do a blood test for a very particular thing.[632.305] [635.71][S02]The lab who did the blood test for finding a particular virus[640.39] [640.39][S02]is not responsible for checking everything.[643.02] [643.02][S02]And if they haven't noticed that you had something else that[645.52] [645.52][S02]wasn't what you got blood drawn, they're[649.098] [649.098][S02]not responsible for that.[650.14] [650.14][S02]And so for imaging, it's always a little ambiguous[654.99] [654.99][S02]what to do with those incidental findings.[657.28] [657.28][S02]And for those aspects of narrow AI, maybe--[662.07] [662.07][S02]I think the jury is still out whether or not[665.47] [665.47][S02]we want the AI to always look for everything else, when it's[668.64] [668.64][S02]looking at one particular image, even if it could[671.73] [671.73][S02]provide pretty helpful alarms.[674.7] [674.7][S01] Those are images that we've spoken about,[677.14] [677.14][S01]but are there other inputs that you[679.95] [679.95][S01]can use for this very narrow AI type of diagnosis?[683.53] [683.53][S02] Absolutely.[684.78] [684.78][S02]I would say all types of modalities, right?[687.01] [687.01][S02]We've done a lot of very interesting work with sound,[690.25] [690.25][S02]for example, leveraging sound to be able to detect tuberculosis[694.5] [694.5][S02]from cough.[695.29] [695.29][S02]And people have also explored that quite a bit during COVID,[699.75] [699.75][S02]obviously.[701.1] [701.1][S02]And so we see good biomarkers, actually,[705.03] [705.03][S02]in sound that can help also provide some relatively cheap[710.2] [710.2][S02]ways to diagnose those types of diseases.[713.045] [713.045][S01] So, wait.[713.92] [713.92][S01]It actually works then?[714.98] [714.98][S02] Yes, it does.[715.18] [715.18][S01] You record a cough, and it[716.763] [716.763][S01]can tell if you've got TB.[718.06] [718.06][S02] In short, yes, that's exactly what it is.[720.44] [720.44][S01] OK.[721.208] [721.208][S01]But there are limitations to this stuff, though, right?[723.5] [723.5][S01]I mean, are they getting it right every single time?[727.76] [727.76][S02] No.[728.51] [728.51][S02]I think that's a very good point.[731.34] [731.34][S02]Like with any algorithms, there is sensitivity and specificity.[735.23] [735.23][S02]So it can have either false positives, false negatives,[738.62] [738.62][S02]so thinking that something exists[740.23] [740.23][S02]when it doesn't, or missing something[742.33] [742.33][S02]that it should have picked.[745.39] [745.39][S02]In general, though, what we do and what[748.78] [748.78][S02]we did with the diabetic retinopathy paper[751.12] [751.12][S02]is really checking how it does with respect to the best[757.0] [757.0][S02]physicians or the best panels of physicians,[759.77] [759.77][S02]and then letting the human, who is always[762.79] [762.79][S02]working with the algorithm, decide[766.76] [766.76][S02]where on that specificity sensitivity tradeoff[770.49] [770.49][S02]they want to operate.[771.63] [771.63][S02]And so they can decide that, really,[773.69] [773.69][S02]they cannot afford false alarms, or the other way around,[778.55] [778.55][S02]that they cannot afford missing anything.[780.6] [780.6][S02]But in both cases, I think for an algorithm[784.13] [784.13][S02]to be cleared by regulatory authorities,[786.42] [786.42][S02]it needs to have shown very strong performance.[788.635] [788.635][S01] OK.[789.26] [789.26][S01]Let me break that down then.[790.47] [790.47][S01]So it could be that an algorithm could say that it's TB when it[794.93] [794.93][S01]isn't.[795.78] [795.78][S01]But it also could be that an algorithm[797.75] [797.75][S01]could miss a genuine case of TB and say that it was fine.[802.363] [802.363][S01]And obviously, you don't want either of those things[804.53] [804.53][S01]to happen.[805.62] [805.62][S01]But what percentage accuracy almost[808.1] [808.1][S01]are you willing to accept before you say,[810.27] [810.27][S01]OK, this model is adding value?[812.64] [812.64][S02] So it really depends.[814.475] [814.475][S02]If you're going to deploy your--[817.07] [817.07][S02]let's say it's a screening test, in places[819.47] [819.47][S02]that had nothing before.[820.69] [820.69][S02]It's a very different story than if you're[822.44] [822.44][S02]replacing an existing alternate way of screening[826.65] [826.65][S02]that same population.[828.04] [828.04][S02]If you're replacing something, you'd better be at least better.[832.38] [832.38][S02]If you're coming in where there was before a void, then[839.17] [839.17][S02]public health authorities are going[840.9] [840.9][S02]to decide what they deem as acceptable.[844.11] [844.11][S02]It also depends what will happen to the patients that are[847.26] [847.26][S02]provided with that diagnosis.[849.46] [849.46][S02]If they go home and you never see them again,[851.62] [851.62][S02]it's a different story than if your tool is[854.43] [854.43][S02]a pre-screening tool.[855.85] [855.85][S02]And then you're going to reroute your patients[858.0] [858.0][S02]to an additional screening tool for confirmation.[860.532] [860.532][S01] When you launch these things,[862.24] [862.24][S01]for example, the TB model or diabetic retinopathy,[865.51] [865.51][S01]does that mean that you aim initially[867.69] [867.69][S01]to go to places where they don't have these existing screening[870.84] [870.84][S01]tests in place?[872.275] [872.275][S02] It really depends.[873.65] [873.65][S02]For diabetic retinopathy, for example,[876.75] [876.75][S02]it is something that we've indeed deployed in Thailand.[880.03] [880.03][S02]And then we often work with partners[881.7] [881.7][S02]to really bring it where it makes a difference.[885.19] [885.19][S02]We've already screened 700,000 people in Thailand.[888.14] [888.14][S02]And we're 10X-ing that over the next few years.[891.59] [891.59][S01] Why Thailand?[892.815] [892.815][S02] So Thailand is one[894.19] [894.19][S02]of those countries in which the patient population[896.68] [896.68][S02]that each ophthalmologist has to care for is fairly large.[900.58] [900.58][S02]There are many places in which there aren't really[903.22] [903.22][S02]enough doctors.[904.63] [904.63][S02]And so it's a good example of a place in which AI screening can[910.48] [910.48][S02]really help improve outcomes.[913.93] [913.93][S01] Let's say that you have[915.46] [915.46][S01]a situation in which some sort of medical imaging[918.88] [918.88][S01]is being used in conjunction with a doctor.[923.47] [923.47][S01]What happens if the two disagree?[926.1] [926.1][S02] Ah.[926.85] [926.85][S02][CHUCKLES] I think at the end of the day,[929.36] [929.36][S02]the doctor is really the one making the call.[931.81] [931.81][S02]So it's very important that they retain control,[936.22] [936.22][S02]and they are the one making the decision,[938.23] [938.23][S02]and they are the ones signing their name on the report.[943.35] [943.35][S02]And so they would have to explain, right?[947.58] [947.58][S02]If they're saying the machine is wrong,[949.26] [949.26][S02]it could be for a variety of reasons.[953.46] [953.46][S02]In other cases, you could think of the algorithm[957.77] [957.77][S02]making the physician rethink.[960.126] [960.126][S02]So it's checking your work, like a spell checker, if you wish.[963.87] [963.87][S02]Sometimes I disagree with my spell checker,[966.92] [966.92][S02]with accents on words in French or things.[969.92] [969.92][S02]It's not perfect.[971.42] [971.42][S02]But I will double-check.[972.565] [972.565][S01] OK.[973.19] [973.19][S01]Well, let's talk about some of the more advanced stuff here.[975.69] [975.69][S01]Because up until now, I mean, the examples[977.75] [977.75][S01]that we've been using here is an MRI, or photo[981.83] [981.83][S01]of the back of an eye, or the sound of a cough.[986.0] [986.0][S01]What about the more holistic view?[988.32] [988.32][S01]So, I mean, really good medicine doesn't see a human[992.25] [992.25][S01]as a collection of interesting or otherwise medical problems.[995.63] [995.63][S01]It sees a human as a human.[997.67] [997.67][S01]Can you use AI to think more holistically?[1001.15] [1001.15][S02] AI is indeed very good at looking together[1006.26] [1006.26][S02]at things that, up until now, we were only--[1010.4] [1010.4][S02]or mostly capable of looking at individually.[1014.33] [1014.33][S02]And maybe bring insights that, up until now, we didn't have.[1020.123] [1020.123][S01] Like what?[1021.04] [1021.04][S01]Like what?[1021.71] [1021.71][S01]I mean, if you do manage to connect up everything[1024.14] [1024.14][S01]we understand about the cell, to everything we understand[1026.515] [1026.515][S01]about an organ, to eventually everything[1028.819] [1028.819][S01]we understand about human, what do you[1031.01] [1031.01][S01]find in those connections?[1033.317] [1033.317][S02] So back in the days, we did quite a bit of work[1035.9] [1035.9][S02]in virtual staining.[1038.46] [1038.46][S02]So when you have an H&E slide--[1040.92] [1040.92][S02]so that's what happens when any part of any piece of tissue[1046.369] [1046.369][S02]removed from your body, in surgery,[1048.8] [1048.8][S02]typically, will be sliced and looked at under the microscope.[1053.82] [1053.82][S02]And the way we do that is that we make[1058.34] [1058.34][S02]hypothesis as to what might be happening.[1062.69] [1062.69][S02]And we stain accordingly so that it makes those things visible.[1065.66] [1065.66][S02]And you can think of some of those techniques[1068.695] [1068.695][S02]as making that visible but without having to stain.[1072.17] [1072.17][S02]So really revealing some of that information,[1075.44] [1075.44][S02]if it's already in the tissue, but without having[1080.14] [1080.14][S02]to bring additional things that are[1083.11] [1083.11][S02]consuming that piece of tissue.[1084.77] [1084.77][S01] Would destroy the tissue for future tests.[1087.02] [1087.02][S02] Exactly, exactly.[1088.353] [1088.353][S02]So to me, there's also something quite profound in the way[1092.29] [1092.29][S02]we're now interrogating tissue with more and more instruments,[1097.04] [1097.04][S02]with things like transcriptomics, genomics,[1099.38] [1099.38][S02]single-cell, et cetera.[1100.85] [1100.85][S02]And so that's really a wonderful application for AI,[1104.51] [1104.51][S02]because the AI can look at each modality and, if you wish,[1109.28] [1109.28][S02]provide us with eyes for that particular scientific[1112.45] [1112.45][S02]instrument.[1113.28] [1113.28][S02]And then it goes beyond, because it's also[1115.03] [1115.03][S02]capable at bridging between those different types[1119.6] [1119.6][S02]of scientific instruments.[1121.05] [1121.05][S01] So then, you presumably[1123.11] [1123.11][S01]can start to combine some of these quite complex AI tools[1127.325] [1127.325][S01]on imaging, on genomics, on all the various things that you're[1131.24] [1131.24][S01]describing.[1132.24] [1132.24][S01]Does that actually allow you to advance in your understanding[1136.46] [1136.46][S01]of diseases?[1137.028] [1137.028][S01]I mean, I'm thinking of cancer care here, for instance.[1139.32] [1139.32][S02] Yeah, it's a great example.[1140.7] [1140.7][S02]And that's what we are actually precisely working[1142.742] [1142.742][S02]on with the L'Institut Curie, the Curie Institute in Paris.[1148.94] [1148.94][S02]That is very advanced in exploiting all of those most[1154.52] [1154.52][S02]recent modalities to better understand cancer,[1158.57] [1158.57][S02]in that case, women's cancer--[1160.41] [1160.41][S02]so uterine cancer or breast cancer,[1162.57] [1162.57][S02]which, despite our best efforts for many, many decades,[1166.2] [1166.2][S02]we're still short of answers for many women.[1170.03] [1170.03][S02]And we're really hoping that by combining those modalities,[1175.35] [1175.35][S02]by getting to a deeper understanding[1178.4] [1178.4][S02]at the cellular level of what's going on,[1181.5] [1181.5][S02]we'll finally be able to crack, for example,[1184.57] [1184.57][S02]triple-negative breast cancers or some of the-- again, more[1190.26] [1190.26][S02]advanced cancers that, today, we don't have good solutions for.[1194.71] [1194.71][S01] As well as cells and genomics and imaging, I mean,[1198.61] [1198.61][S01]there are other data sources that the AI[1200.85] [1200.85][S01]can bring into the equation here as well, right?[1203.077] [1203.077][S02] Absolutely.[1204.16] [1204.16][S02]And I think that's also maybe one[1208.05] [1208.05][S02]of the secrets AI will be able to decipher, which is,[1213.426] [1213.426][S02]health is in everything we do.[1217.0] [1217.0][S02]It's how many steps you walked today,[1219.22] [1219.22][S02]what you ate for breakfast, lunch,[1220.78] [1220.78][S02]and dinner, whether you've interacted with friends[1223.23] [1223.23][S02]or been lonely all day long.[1225.4] [1225.4][S02]And so it's typically really, really hard[1228.57] [1228.57][S02]to translate that into either healthy habits[1233.52] [1233.52][S02]or understand how much of that is factoring into something[1237.73] [1237.73][S02]physical that is going on.[1239.78] [1239.78][S02]And we're doing a lot of work with sensors.[1242.33] [1242.33][S02]I'm wearing this Fitbit today.[1244.13] [1244.13][S02]It's just one of the data sources[1245.56] [1245.56][S02]that can really be leveraged, in combination with everything[1248.29] [1248.29][S02]else, to try to both better understand[1251.29] [1251.29][S02]health, sometimes just as an individual,[1253.7] [1253.7][S02]and also when we're trying to change behavior,[1258.34] [1258.34][S02]accompany us on that journey.[1259.548] [1259.548][S01] I mean, you're talking[1260.965] [1260.965][S01]about quite different quality of data here.[1262.78] [1262.78][S01]You've got slides from biopsies and genomic data[1267.61] [1267.61][S01]and then step count.[1269.0] [1269.0][S01]It sort of feels like a quite fuzzy thing around the edges.[1272.69] [1272.69][S01]Would it actually make a difference?[1274.762] [1274.762][S02] It does.[1275.72] [1275.72][S02]I mean, it's been shown over and over again,[1277.7] [1277.7][S02]for example, how much sleep matters[1279.65] [1279.65][S02]for both cardiovascular health and in oncology[1284.6] [1284.6][S02]in terms of preventing cancer.[1286.55] [1286.55][S02]So it's one of my hopes, that AI will bring those[1289.72] [1289.72][S02]two together in a way that is harder[1291.52] [1291.52][S02]to do in our traditional health care systems[1293.92] [1293.92][S02]and also very hard to do for us as individuals[1296.66] [1296.66][S02]because you see that type of impact at a population level.[1301.83] [1301.83][S02]As an individual, it's really hard to convince yourself[1305.115] [1305.115][S02]that you should go to bed one hour earlier because that's[1307.49] [1307.49][S02]actually the best thing you can do for your health.[1309.42] [1309.42][S01] There's quite a lot of buzz[1311.045] [1311.045][S01]at the moment about digital twins.[1312.48] [1312.48][S01]Does this also add to the whole holistic view of health?[1316.14] [1316.14][S01]I mean, is there an idea of making[1317.81] [1317.81][S01]a kind of digital twin of yourself[1319.67] [1319.67][S01]for health care purposes?[1321.24] [1321.24][S02] Yeah.[1322.073] [1322.073][S02]We see that a lot.[1323.79] [1323.79][S02]And it can mean different things for different people.[1326.04] [1326.04][S02]There is the idea that the digital twin[1328.28] [1328.28][S02]is kind of a simulation of someone with a similar persona,[1333.18] [1333.18][S02]for example, and then that can be a good proxy[1335.54] [1335.54][S02]to interrogate the potential impact of different types[1340.25] [1340.25][S02]of interventions.[1341.46] [1341.46][S02]And then digital twin can mean something quite different[1344.24] [1344.24][S02]for the pharma industry, for example, where there's[1347.21] [1347.21][S02]a lot of work trying to see how far we can go with in silico[1351.59] [1351.59][S02]arms, for example, of clinical trials,[1354.04] [1354.04][S02]if we manage to assemble virtual cohorts that are allowing us[1358.41] [1358.41][S02]to, in the end, gain as much knowledge about the safety[1365.34] [1365.34][S02]and efficacy of a drug, but without needing[1368.25] [1368.25][S02]as many people for that particular clinical trial.[1371.957] [1371.957][S01] Oh, that's so interesting.[1373.54] [1373.54][S01]So is this about, I don't know, making[1376.05] [1376.05][S01]a cohort of simulated humans almost-- maybe not[1380.208] [1380.208][S01]the whole thing-- maybe just an organ[1381.75] [1381.75][S01]or whatever it is you're particularly focusing on--[1384.575] [1384.575][S01]but so that you don't necessarily[1385.95] [1385.95][S01]have to use as many people in your clinical trial?[1388.2] [1388.2][S02] Exactly.[1388.62] [1388.62][S02]For clinical trials, that would be exactly that.[1390.782] [1390.782][S01] But then to be able to do that effectively, you[1393.24] [1393.24][S01]have to really, really understand what real humans look[1396.72] [1396.72][S01]like, which means having a wealth of data[1399.36] [1399.36][S01]from individual patients somewhere along the way.[1402.46] [1402.46][S01]How do people feel about contributing their own data[1407.46] [1407.46][S01]for this kind of end?[1408.85] [1408.85][S01]For the research of medical purposes?[1411.9] [1411.9][S02] I think when we talk about privacy,[1414.57] [1414.57][S02]we see different concerns in different parts of the world.[1418.82] [1418.82][S02]In Europe, there are a lot of sovereignty concerns.[1421.2] [1421.2][S02]People want to know that the data is--[1423.32] [1423.32][S02]I mean, not in all countries-- but is staying on their soil.[1427.71] [1427.71][S02]And we have solution for that.[1430.16] [1430.16][S02]All of the trusted public cloud solutions,[1432.81] [1432.81][S02]for example, are complying with the highest levels of regulation[1438.95] [1438.95][S02]on that topic.[1440.04] [1440.04][S02]In other parts of the world, we see a lot of appetite for people[1443.48] [1443.48][S02]to not think that they are sick for nothing.[1445.92] [1445.92][S02]By that, I mean, it's actually quite compelling[1448.28] [1448.28][S02]to know that if you're sick but you're contributing data[1451.07] [1451.07][S02]to research, you're helping making the last person that[1454.34] [1454.34][S02]is going to have the same fate a little less sick, if you wish.[1457.98] [1457.98][S02]So I think really enabling that virtuous cycle is really[1463.85] [1463.85][S02]where we have to be.[1465.12] [1465.12][S01] Because it does feel like health care data is[1467.51] [1467.51][S01]a particularly sensitive case.[1469.52] [1469.52][S01]Because, on the one hand, exactly as you described,[1471.72] [1471.72][S01]there is huge potential to advance our understanding[1475.41] [1475.41][S01]and improve conditions for people who--[1478.88] [1478.88][S01]future generations.[1480.28] [1480.28][S01]But on the other hand, if health care data[1484.11] [1484.11][S01]gets into the wrong hands, or is misused, or used irresponsibly,[1489.34] [1489.34][S01]I mean, there is real sort of--[1491.07] [1491.07][S01]I think people have real concerns[1493.23] [1493.23][S01]about the potential ramifications of that.[1495.31] [1495.31][S01]So, I mean, how do you strike that balance?[1498.727] [1498.727][S02] Absolutely.[1499.81] [1499.81][S02]Once you have the technology to actually protect the data,[1502.36] [1502.36][S02]there's also accountability.[1503.79] [1503.79][S02]That meaning that if we're saying that this research will[1507.3] [1507.3][S02]be helpful to the people, that indeed, at the end of the day,[1510.19] [1510.19][S02]it is research that is helpful to the people[1512.25] [1512.25][S02]where the data originated.[1513.52] [1513.52][S02]And I think that's a very important principle[1517.05] [1517.05][S02]in the same way that you shouldn't do clinical trials[1519.94] [1519.94][S02]in places of the world-- in some places of the world--[1522.19] [1522.19][S02]and then the drugs benefit in other places of the world.[1524.59] [1524.59][S01] So you're not just extracting from one[1526.02] [1526.02][S01]to give to another.[1527.02] [1527.02][S02] Precisely.[1527.73] [1527.73][S01] OK.[1528.37] [1528.37][S01]I also want to ask you a bit more about the patient's[1530.578] [1530.578][S01]experience in all of this.[1532.56] [1532.56][S01]Because I think-- are large language models[1535.01] [1535.01][S01]changing the game here already?[1536.58] [1536.58][S01]I mean, are you concerned that people[1538.73] [1538.73][S01]are trying to diagnose themselves with generative AI?[1541.597] [1541.597][S02] Yeah.[1542.43] [1542.43][S02]I mean, I would say, even before, you really[1544.67] [1544.67][S02]shouldn't do that, right?[1545.75] [1545.75][S02]You shouldn't play your own doctor.[1548.87] [1548.87][S01] People do, though, don't they?[1550.62] [1550.62][S01]Let's be honest.[1551.25] [1551.25][S02] I think people want[1552.667] [1552.667][S02]to know, and especially in places[1554.78] [1554.78][S02]where waiting time to see a physician[1557.105] [1557.105][S02]is becoming a real issue.[1559.02] [1559.02][S02]Then, of course, patients are trying[1562.72] [1562.72][S02]to learn as much as possible and to decipher what's happening,[1567.713] [1567.713][S02]and sometimes quite effectively when they are there because they[1570.38] [1570.38][S02]are their best advocate-- or a parent trying to figure[1573.02] [1573.02][S02]something out for their child.[1575.39] [1575.39][S02]And sometimes we have a couple of examples[1577.67] [1577.67][S02]where, for rare diseases, actually,[1579.78] [1579.78][S02]we've seen patients do a remarkable job.[1582.12] [1582.12][S02]Now, I would say, at Google, we have always[1584.6] [1584.6][S02]tried to provide the most helpful answers to our users.[1588.9] [1588.9][S02]And so with symptoms, for example,[1591.96] [1591.96][S02]we have the knowledge cards that you've probably[1594.95] [1594.95][S02]noticed that are telling you about a disease[1599.09] [1599.09][S02]with authoritative content, like, trying[1601.52] [1601.52][S02]to be clear in the explanation of common symptoms[1605.9] [1605.9][S02]and potential options for treatment,[1608.43] [1608.43][S02]but never crossing that line and telling you what[1611.93] [1611.93][S02]your own diagnosis might be.[1614.46] [1614.46][S02]Now, with large language models--[1617.99] [1617.99][S02]so our model, Gemini, will also not tell you[1622.73] [1622.73][S02]what your diagnosis will be.[1625.1] [1625.1][S02]It will tell you, sorry, I'm not a medical doctor.[1627.78] [1627.78][S02]You should go and see a doctor.[1629.85] [1629.85][S02]But you can get more helpful answers if you're asking,[1635.462] [1635.462][S02]what potential conditions could explain this?[1638.15] [1638.15][S02]And it will give you more of the textbook answer.[1641.1] [1641.1][S02]I like to think of it as those family[1643.22] [1643.22][S02]guide for health care, those--[1646.11] [1646.11][S02]I don't know, the big, thick books that you might have,[1649.71] [1649.71][S02]where you have small children and you're[1652.98] [1652.98][S02]trying to know what to do in those particular cases.[1655.99] [1655.99][S02]Well, it's that same level.[1658.21] [1658.21][S02]If we believe that large language models are going[1660.54] [1660.54][S02]to be able to provide diagnosis, we[1663.18] [1663.18][S02]need to continue the research.[1664.48] [1664.48][S02]And that's what we're doing with our work[1666.27] [1666.27][S02]with AMIE, the Articulate Medical Intelligence Explorer.[1669.85] [1669.85][S02]It's a research project in which we[1672.54] [1672.54][S02]are trying to see which conversational abilities[1677.4] [1677.4][S02]a large language model can have to establish a diagnosis.[1681.82] [1681.82][S02]So can it ask the right question,[1683.64] [1683.64][S02]the way a physician would do, like dialoguing with a patient?[1689.11] [1689.11][S01] How close are we to seeing[1690.81] [1690.81][S01]a system like AMIE being used actually in medical settings?[1695.693] [1695.693][S02] So we've been working on this project[1697.86] [1697.86][S02]for quite a while already.[1699.84] [1699.84][S02]And our first research papers were all[1702.45] [1702.45][S02]based on research done with patient actors[1706.86] [1706.86][S02]and simulated scenarios.[1708.74] [1708.74][S02]We're now doing a clinical study under IRB approval[1712.64] [1712.64][S02]with Harvard at the Beth Israel Medical Center[1716.06] [1716.06][S02]to see what happens, again, in a very controlled environment,[1720.32] [1720.32][S02]with physician supervision, with this system.[1723.48] [1723.48][S02]So that's our next step.[1724.92] [1724.92][S02]Hard for me to predict how long it[1727.85] [1727.85][S02]is before such a system would really help physicians[1731.84] [1731.84][S02]in the clinic.[1733.19] [1733.19][S02]But we are already seeing, in parts of the world,[1738.18] [1738.18][S02]similar systems that are typically[1739.64] [1739.64][S02]being used to answer low-risk questions, if you wish,[1745.43] [1745.43][S02]and that are always supervised.[1747.42] [1747.42][S02]So there is a physician maybe double-checking the answer[1750.062] [1750.062][S02]within a period of time, a short amount of time, like, say,[1752.52] [1752.52][S02]10 minutes of a model, or 15 minutes from a model,[1755.66] [1755.66][S02]answering a patient.[1757.08] [1757.08][S02]But you see how it's really a step-by-step approach.[1761.47] [1761.47][S01] Yeah, absolutely.[1762.785] [1762.785][S02] And I think that's very important, again.[1765.48] [1765.48][S02]Because if you have a model that says the right thing 9 out of 10[1770.3] [1770.3][S02]times, but the 10th, it's doing something really bad, well,[1774.03] [1774.03][S02]maybe it wasn't so good at all for none of those cases.[1778.217] [1778.217][S01] The benefits don't outweigh the costs.[1780.3] [1780.3][S02] Precisely.[1780.98] [1780.98][S01] That is interesting, though.[1782.647] [1782.647][S01]Because you're right, that if you're[1785.24] [1785.24][S01]talking to a real physician, I mean,[1787.2] [1787.2][S01]they will tell you that patients don't[1789.53] [1789.53][S01]come in with a sort of very clear, refined list[1793.535] [1793.535][S01]of relevant information for you.[1796.47] [1796.47][S01]I mean, they'll come in and say, oh, I don't feel so great.[1798.96] [1798.96][S01]And it's like, it's your job then to interrogate that[1801.168] [1801.168][S01]and actually find out precisely what's at the heart of it,[1804.938] [1804.938][S01]which bits of information are relevant and which bits aren't.[1807.48] [1807.48][S01]That must be a very difficult thing to mimic within an AI.[1813.063] [1813.063][S02] Indeed.[1813.98] [1813.98][S02]And also, I like to often think of the AI of tomorrow[1817.67] [1817.67][S02]as something that will be like the physician in your family.[1824.16] [1824.16][S02]There's often this physician in your family[1826.455] [1826.455][S02]that gets asked all questions about anything[1830.19] [1830.19][S02]that has to do with health care of all family members, all[1833.76] [1833.76][S02]the cousins, and everyone.[1835.06] [1835.06][S02]Even if they are a dermatologist,[1836.95] [1836.95][S02]they will be asked cardiology questions,[1839.13] [1839.13][S02]and they will help people navigate the system, et cetera.[1841.59] [1841.59][S02]But not all families have physicians.[1843.82] [1843.82][S02]And so I'd like to think that the AI we'll build tomorrow[1847.29] [1847.29][S02]will provide everyone with that, the equivalent[1850.23] [1850.23][S02]of a physician in your family.[1852.04] [1852.04][S02]But the key element is that that member of your family[1854.94] [1854.94][S02]actually knows you, right?[1857.28] [1857.28][S02]And they know you on the long term.[1859.6] [1859.6][S02]So they know if you're very anxious.[1861.24] [1861.24][S02]And when you're saying that you have[1863.16] [1863.16][S02]a bad headache for the last three weeks,[1865.21] [1865.21][S02]you've said that for the last 20 years,[1866.89] [1866.89][S02]and they can safely discard it.[1868.6] [1868.6][S02]Or if you're calling them, but you've actually never called[1871.77] [1871.77][S02]them, they know that it's something[1873.9] [1873.9][S02]they should pay attention to.[1875.29] [1875.29][S02]They will recognize the tone of your voice,[1877.45] [1877.45][S02]et cetera, which I think makes this--[1881.24] [1881.24][S02]I give the example of the dermatologist that[1883.47] [1883.47][S02]ends up having to say something about everything--[1885.78] [1885.78][S02]but pretty safe, right, in the end.[1887.55] [1887.55][S02]It's actually not going to provide bad advice to someone[1891.17] [1891.17][S02]that has a cardiac condition because they know the person,[1894.44] [1894.44][S02]and they will tell them to actually seek[1897.02] [1897.02][S02]cardiologist advice at the right time.[1900.45] [1900.45][S02]So we would want our AI systems to do the same.[1903.32] [1903.32][S02]But for that, it means they need to know you enough.[1905.58] [1905.58][S02]And it's not just very quickly saying your latest symptoms[1909.92] [1909.92][S02]and getting an answer.[1911.38] [1911.38][S02]Those systems have to be built.[1913.32] [1913.32][S01] I guess one of the differences of a physician[1915.74] [1915.74][S01]in your family is that they're not[1917.157] [1917.157][S01]really prone to hallucinations.[1918.496] [1918.496][LAUGHTER][1919.063] [1919.063][S02] Some of them might, but you're right.[1921.23] [1921.23][S01] Some of them might.[1922.522] [1922.522][S01]But how do you create something like that, when we are still[1926.93] [1926.93][S01]in a world where generative AI does have these hallucinations,[1930.44] [1930.44][S01]or is forgetful about timelines, or mistakes information-- all[1934.88] [1934.88][S01]of the common mistakes that you see in generative AI?[1937.497] [1937.497][S02] Yeah.[1938.33] [1938.33][S02]So that's why you cannot just take a large language model off[1944.61] [1944.61][S02]the shelf and apply it in health care and expect that to be[1949.17] [1949.17][S02]acceptable.[1950.73] [1950.73][S02]And I think we've done a lot of work first with Med-PaLM.[1954.34] [1954.34][S02]That was our first model fine-tuned on a medical corpus.[1957.91] [1957.91][S02]And we demonstrated that it was doing better[1961.41] [1961.41][S02]at answering medical license exams than, initially,[1967.11] [1967.11][S02]an average student and then a panel of experts.[1970.02] [1970.02][S02]And then we did Med Gemini.[1971.59] [1971.59][S01] What was Med Gemini?[1972.96] [1972.96][S02] Oh, Med Gemini is our large-language-model Gemini,[1976.45] [1976.45][S02]which I hope--[1977.07] [1977.07][S02]I know you've played with.[1978.153] [1978.153][S01] Hey, endlessly.[1980.13] [1980.13][S02] And we fine-tuned it on a medical corpus.[1983.68] [1983.68][S02]And so it's really inheriting the long context[1986.64] [1986.64][S02]of Gemini, its reasoning capabilities,[1988.6] [1988.6][S02]its native multi-modality.[1991.2] [1991.2][S02]But on top of that, it has seen medical data in a way that[1997.29] [1997.29][S02]Gemini hadn't.[1998.86] [1998.86][S02]And it's also being evaluated for specific medical purposes.[2003.36] [2003.36][S01] There is this other aspect of the human AI[2006.42] [2006.42][S01]collaboration, though.[2007.72] [2007.72][S01]I mean, if some of these tools are designed for doctors,[2010.75] [2010.75][S01]is there a risk that doctors, I guess, lose some of their skills[2015.18] [2015.18][S01]or start to become overly reliant on these kind of models?[2018.28] [2018.28][S02] There's always that risk, right?[2020.94] [2020.94][S02]In health care, I would say, though,[2022.63] [2022.63][S02]that for me, we don't really have a choice in the sense[2025.98] [2025.98][S02]that we have a big shortage of health care professionals[2029.28] [2029.28][S02]in many parts of the world.[2030.79] [2030.79][S02]So the question is, how do we address that?[2034.27] [2034.27][S02]How do we make sure that people, globally,[2037.53] [2037.53][S02]can be as healthy as possible and don't suffer from things[2043.2] [2043.2][S02]for which we have answers?[2044.46] [2044.46][S02]There are a lot of things for which we still[2046.35] [2046.35][S02]don't have answers.[2047.17] [2047.17][S02]But for a lot of others, where medicine can actually[2051.33] [2051.33][S02]provide an answer, I think we have a responsibility[2054.27] [2054.27][S02]to leverage the technology we have[2056.19] [2056.19][S02]to bring that to those people.[2058.02] [2058.02][S02]Rather than over-reliance, for me the question is,[2060.85] [2060.85][S02]how are we going to train the next generation, right?[2063.383] [2063.383][S02]And what is actually--[2064.3] [2064.3][S01] Of doctors.[2064.969] [2064.969][S02] Of doctors.[2066.052] [2066.052][S02]It's true of doctors within our conversation.[2068.179] [2068.179][S02]It can be true of other professions[2071.05] [2071.05][S02]within the context of the new era we're in with gen AI.[2075.53] [2075.53][S02]But for doctors, there might be things where actually the AI is[2079.54] [2079.54][S02]really doing a really good job.[2081.02] [2081.02][S02]And when it's not, it's also telling you[2082.96] [2082.96][S02]that it doesn't know, et cetera.[2084.55] [2084.55][S02]So you can rely on the AI.[2086.659] [2086.659][S02]And it's OK to not be as good at those tasks[2088.747] [2088.747][S02]that the AI is really good at, because there[2090.58] [2090.58][S02]are so many other things that you need to learn[2092.739] [2092.739][S02]and you need to do well.[2094.219] [2094.219][S01] So there are some things, then,[2096.011] [2096.011][S01]that you think the AI just isn't going to touch.[2098.57] [2098.57][S01]I mean, things like empathy, for instance.[2100.79] [2100.79][S02] Well, actually, that one is a good one[2102.998] [2102.998][S02]because we always check that our models have good bedside[2107.23] [2107.23][S02]manners.[2108.375] [2108.375][S02]And they actually do.[2109.85] [2109.85][S02]They're not bad at all.[2111.05] [2111.05][S02]They can adapt to their audience in a way[2113.11] [2113.11][S02]that few human beings can.[2115.75] [2115.75][S02]They can leverage the right language for a five-year-old,[2118.93] [2118.93][S02]or a 70-year-old, or a 40-year-old.[2121.3] [2121.3][S02]And if you're not a native speaker,[2123.76] [2123.76][S02]they can speak in your own language.[2125.47] [2125.47][S02]So actually, when they're judged on empathy,[2129.01] [2129.01][S02]they actually do pretty well.[2131.34] [2131.34][S01] How on Earth do you train it to be more empathic?[2134.14] [2134.14][S02] Well, in the same way[2135.64] [2135.64][S02]you train them for everything else.[2137.28] [2137.28][S02]They are based on a lot of conversations[2141.762] [2141.762][S02]that they've seen in the training data.[2143.8] [2143.8][S02]And if those conversations are empathic,[2146.4] [2146.4][S02]then they learn to also leverage the same words,[2151.27] [2151.27][S02]the same tone, et cetera.[2152.62] [2152.62][S02]And you do need to do some time to fine-tune them or provide[2156.93] [2156.93][S02]them with more specific examples,[2159.01] [2159.01][S02]because not everything on the web is empathic.[2162.465] [2162.465][S01] Do you think, though,[2163.84] [2163.84][S01]that there are some aspects of medical care[2167.25] [2167.25][S01]that really AI can't touch?[2170.88] [2170.88][S02] I've built surgical robots.[2172.63] [2172.63][S02]So even the haptics and all of that,[2174.82] [2174.82][S02]I tend to think that, at some point, we will have ways.[2177.49] [2177.49][S02]But today, whenever you need a physical exam,[2180.175] [2180.175][S02]like if you're checking someone's stomach, and you--[2184.33] [2184.33][S02]a lot of things, you absolutely need physicians.[2187.28] [2187.28][S02]I paused because I think every single time[2190.12] [2190.12][S02]we say the technology will never do this, we get wrong.[2193.9] [2193.9][S02]And at some point, things change in a way[2196.93] [2196.93][S02]that, actually, they can also be helpful in those circumstances.[2201.735] [2201.735][S02]It's coming as something that is augmenting physicians.[2206.21] [2206.21][S02]It's not replacing them in all of the tasks they do daily.[2212.42] [2212.42][S02]I see AI much more as the one paying[2214.42] [2214.42][S02]the debt for what the digital industry has done to physicians,[2218.12] [2218.12][S02]which is asking them to enter lots and lots of data[2221.41] [2221.41][S02]in computers for years, changing their job fairly drastically,[2227.8] [2227.8][S02]making them miserable in some cases.[2230.03] [2230.03][S02]I do think that we see a lot of burnout in the profession.[2235.49] [2235.49][S02]And I'm hoping AI contributes to bringing back[2239.65] [2239.65][S02]the joy of practicing medicine.[2241.473] [2241.473][S02]But to me, it's paying that debt, right?[2243.14] [2243.14][S02]It's like, finally, with all of the data[2245.17] [2245.17][S02]that physicians and all their health[2246.7] [2246.7][S02]care professionals have taken so much time to enter,[2250.25] [2250.25][S02]we are going to be able to derive insights or knowledge[2255.19] [2255.19][S02]and help them.[2256.118] [2256.118][S01] Here's why it was all worth it, I guess.[2258.285] [2258.285][S02] Yes.[2258.9] [2258.9][S01] But that is interesting listening to you[2260.41] [2260.41][S01]talk about it, though.[2261.14] [2261.14][S01]Because, I mean, so many of the conversations that I get to have[2263.807] [2263.807][S01]on this podcast are about, I don't know, AGI and long-term--[2268.09] [2268.09][S01]these holistic systems and advances[2272.05] [2272.05][S01]towards completely doing things in a different way than we[2274.87] [2274.87][S01]always have.[2275.75] [2275.75][S01]But the way that you're describing your vision of this,[2278.48] [2278.48][S01]it's almost like the AI here is a tool that will change[2282.43] [2282.43][S01]every aspect of medicine.[2284.15] [2284.15][S01]But fundamentally, the idea of a patient-doctor relationship[2287.95] [2287.95][S01]is unchanged.[2289.283] [2289.283][S02] You're right.[2290.45] [2290.45][S02]I think I might be a little maybe[2294.43] [2294.43][S02]more cautious than what we're envisioning[2298.59] [2298.59][S02]in other aspects of society.[2301.62] [2301.62][S02]And it also depends, again, where in the world[2306.09] [2306.09][S02]we are envisioning that impact of technology.[2308.43] [2308.43][S02]Because I think that technology will bring health care to parts[2311.97] [2311.97][S02]of the world that probably didn't have access[2315.24] [2315.24][S02]to health care at all, or only in a very limited way,[2319.26] [2319.26][S02]in ways that we are not yet envisioning.[2323.11] [2323.11][S02]And that might be beyond anything we've described today.[2328.56] [2328.56][S02]But I'm also a bit skeptical of health care being entirely[2333.9] [2333.9][S02]revolutionized overnight, and it will[2336.69] [2336.69][S02]be completely different tomorrow from how it looked yesterday.[2342.51] [2342.51][S02]That being said, I'm also absolutely blown away[2345.36] [2345.36][S02]with models like AMIE.[2348.0] [2348.0][S02]If you think about it, you can now[2350.85] [2350.85][S02]have a conversation with a large language model that[2356.74] [2356.74][S02]is very similar to the conversation you would have[2360.67] [2360.67][S02]with an incredibly well-educated specialist,[2365.53] [2365.53][S02]pulling knowledge from all around the world.[2369.08] [2369.08][S02]Meaning, this very small clinical trial that happened[2373.36] [2373.36][S02]in the '50s in some remote place and was published back then,[2379.66] [2379.66][S02]echoing some new things that are starting to be seen somewhere[2383.86] [2383.86][S02]else.[2384.61] [2384.61][S02]Up until now, serendipity sometimes played a very big role[2390.16] [2390.16][S02]in, if you had a rare condition, as to whether you[2394.24] [2394.24][S02]would be treated or not.[2395.42] [2395.42][S02]And now, with our large language models,[2398.21] [2398.21][S02]I think we're completely changing that serendipity game.[2402.023] [2402.023][S01] It's always nice to finish a podcast on a very[2404.44] [2404.44][S01]positive and optimistic note.[2406.04] [2406.04][S01]But I think there's a lot to look forward to.[2407.917] [2407.917][S02] Absolutely.[2409.0] [2409.0][S01] Thank you.[2409.67] [2409.67][S02] Thank you.[2410.33] [2410.33][S01] I think medicine is a special case.[2412.63] [2412.63][S01]Because between the false positives[2414.71] [2414.71][S01]and the false negatives and, crucially, the potential harms[2417.77] [2417.77][S01]of either, this is something that has a very high bar[2421.82] [2421.82][S01]to get right.[2423.11] [2423.11][S01]And I think there is something reassuring about Joelle's[2426.17] [2426.17][S01]no-nonsense determination to take the scientific approach[2430.34] [2430.34][S01]here, to carefully and cautiously evaluate[2433.82] [2433.82][S01]the benefit of using AI to make sure that we end up[2437.63] [2437.63][S01]with the best health care outcomes for as many people as[2440.96] [2440.96][S01]possible.[2441.96] [2441.96][S01]You've been listening to "Google DeepMind, The Podcast," with me,[2445.11] [2445.11][S01]Professor Hannah Fry.[2446.16] [2446.16][S01]If you enjoyed that episode, then[2447.65] [2447.65][S01]do subscribe to our YouTube channel.[2449.67] [2449.67][S01]You can also find us on your favorite podcast platform.[2452.79] [2452.79][S01]And we have plenty more episodes on a whole range of topics[2455.66] [2455.66][S01]to come, so do check those out, too.[2457.92] [2457.92][S01]See you next time.[2458.69] [2458.69][MUSIC PLAYING][2461.74]