1 00:00:00,000 --> 00:00:02,280 Welcome to the deep dive. 2 00:00:02,280 --> 00:00:04,880 We're diving into AI and medicine today, 3 00:00:04,880 --> 00:00:07,920 especially its potential for creating new treatments. 4 00:00:07,920 --> 00:00:10,000 You've given us some really interesting stuff to work with, 5 00:00:10,000 --> 00:00:12,800 a transcript from a Harvard Medical School seminar 6 00:00:12,800 --> 00:00:16,800 on AI and medicine, and then some research excerpts 7 00:00:16,800 --> 00:00:19,640 on, well, algorithmic bias. 8 00:00:19,640 --> 00:00:20,640 So let's unpack this, 9 00:00:20,640 --> 00:00:22,760 we'll pull out the important bits from all this, 10 00:00:22,760 --> 00:00:25,480 look at some surprising real world examples, 11 00:00:25,480 --> 00:00:27,560 and by the end, to be left with a question 12 00:00:27,560 --> 00:00:30,120 to really chew on, hopefully, sound good. 13 00:00:30,120 --> 00:00:31,120 Sounds great. 14 00:00:31,120 --> 00:00:32,440 Perfect. 15 00:00:32,440 --> 00:00:33,480 So let's jump right in. 16 00:00:33,480 --> 00:00:36,320 The potential of AI for discovering new treatments, 17 00:00:36,320 --> 00:00:37,280 that's where we'll start. 18 00:00:37,280 --> 00:00:39,880 Yeah, I mean, AI is already having a bay impact 19 00:00:39,880 --> 00:00:41,120 on how we treat patients, 20 00:00:41,120 --> 00:00:43,440 and it's only gonna get bigger from here. 21 00:00:43,440 --> 00:00:45,200 I was really struck by that story in the seminar 22 00:00:45,200 --> 00:00:47,200 about a six year old child. 23 00:00:47,200 --> 00:00:50,120 This kid just suddenly loses the ability to walk and talk. 24 00:00:50,120 --> 00:00:51,120 Oh, wow. 25 00:00:51,120 --> 00:00:52,600 Like it sounds like a movie plot, 26 00:00:52,600 --> 00:00:53,440 that this really happened. 27 00:00:53,440 --> 00:00:54,280 Yeah, absolutely. 28 00:00:54,280 --> 00:00:56,280 Imagine how desperate the parents were. 29 00:00:56,280 --> 00:00:57,680 I mean, doctor after doctor, 30 00:00:57,680 --> 00:01:00,200 and nobody could figure out what was wrong. 31 00:01:00,200 --> 00:01:02,800 Thankfully, they were able to get into this program 32 00:01:02,800 --> 00:01:05,520 called the Undiagnosed Disease Network. 33 00:01:05,520 --> 00:01:08,240 And this network, they use AI to analyze 34 00:01:08,240 --> 00:01:10,160 millions of genetic records, 35 00:01:10,160 --> 00:01:12,120 looking for anything that could explain, 36 00:01:12,120 --> 00:01:14,120 you know, explain what was happening to this child. 37 00:01:14,120 --> 00:01:14,960 Wow. 38 00:01:14,960 --> 00:01:17,200 And in this case, the AI was able to pinpoint 39 00:01:17,200 --> 00:01:19,680 a rare gene mutation as the cause. 40 00:01:19,680 --> 00:01:22,160 So it's like AI found the needle in the haystack. 41 00:01:22,160 --> 00:01:23,000 Right. 42 00:01:23,000 --> 00:01:25,080 And here's the really wild part. 43 00:01:25,080 --> 00:01:28,600 Just by making a simple change to the child's diet, 44 00:01:28,600 --> 00:01:30,760 again identified through AI, 45 00:01:30,760 --> 00:01:34,400 they were able to regain the ability to walk and talk. 46 00:01:34,400 --> 00:01:35,640 It's amazing, right? 47 00:01:35,640 --> 00:01:36,480 It's mind blowing. 48 00:01:36,480 --> 00:01:38,320 It's like, yeah, AI can tackle 49 00:01:38,320 --> 00:01:40,280 those really complex, difficult cases. 50 00:01:40,280 --> 00:01:41,120 Right. 51 00:01:41,120 --> 00:01:43,200 But it's also changing how we approach everyday healthcare. 52 00:01:43,200 --> 00:01:44,040 Absolutely. 53 00:01:44,040 --> 00:01:46,560 I think one really good example of that is, 54 00:01:46,560 --> 00:01:48,280 you know, that smartphone app that was developed 55 00:01:48,280 --> 00:01:50,640 to diagnose melanoma from skin images. 56 00:01:50,640 --> 00:01:53,600 Like early detection of melanoma is so critical. 57 00:01:53,600 --> 00:01:54,440 Right. 58 00:01:54,440 --> 00:01:56,400 And we have AI that's putting that power 59 00:01:56,400 --> 00:01:57,760 right in our pockets. 60 00:01:57,760 --> 00:01:58,600 Right. 61 00:01:58,600 --> 00:01:59,800 Okay. 62 00:01:59,800 --> 00:02:02,120 So moving on, the seminar also talked about 63 00:02:02,120 --> 00:02:06,080 how AI can help us make the best use of existing drugs. 64 00:02:06,080 --> 00:02:08,200 It's kind of like we have this huge medicine cabinet, 65 00:02:08,200 --> 00:02:09,240 but sometimes we just don't know 66 00:02:09,240 --> 00:02:10,480 which medicine to take, right? 67 00:02:10,480 --> 00:02:11,320 Absolutely. 68 00:02:11,320 --> 00:02:13,160 AI can really help with that. 69 00:02:13,160 --> 00:02:16,040 By analyzing data like gene expression patterns 70 00:02:16,040 --> 00:02:17,960 and, you know, patient histories, 71 00:02:17,960 --> 00:02:20,000 AI can identify treatment options 72 00:02:20,000 --> 00:02:23,200 that might not be obvious to human doctors. 73 00:02:23,200 --> 00:02:24,040 Right. 74 00:02:24,040 --> 00:02:25,360 And we had this amazing story in the seminar 75 00:02:25,360 --> 00:02:27,960 about a teenager with severe ulcerative colitis. 76 00:02:27,960 --> 00:02:29,200 I mean, nothing was working. 77 00:02:29,200 --> 00:02:30,040 Oh, wow. 78 00:02:30,040 --> 00:02:31,400 And this poor kid was facing a colectomy, 79 00:02:31,400 --> 00:02:33,280 which is major surgery. 80 00:02:33,280 --> 00:02:34,120 Right. 81 00:02:34,120 --> 00:02:34,960 Awful. 82 00:02:34,960 --> 00:02:38,120 So a team of computer scientists got involved. 83 00:02:38,120 --> 00:02:38,960 Yes. 84 00:02:38,960 --> 00:02:41,800 Started analyzing the patient's gene expression data. 85 00:02:41,800 --> 00:02:42,720 Okay, I have to guess. 86 00:02:42,720 --> 00:02:43,560 Okay. 87 00:02:43,560 --> 00:02:44,400 Was it essential oils? 88 00:02:44,400 --> 00:02:45,240 No, not quite. 89 00:02:45,240 --> 00:02:46,080 Okay. 90 00:02:46,080 --> 00:02:49,200 It turned out that a treatment derived from indigo, you know. 91 00:02:49,200 --> 00:02:50,040 Why? 92 00:02:50,040 --> 00:02:50,880 Like the dye. 93 00:02:50,880 --> 00:02:51,720 The dye? 94 00:02:51,720 --> 00:02:52,560 Yeah, indigo. 95 00:02:52,560 --> 00:02:54,440 It has anti-inflammatory properties. 96 00:02:54,440 --> 00:02:55,360 That's amazing. 97 00:02:55,360 --> 00:02:56,200 Who would have thought? 98 00:02:56,200 --> 00:02:57,020 I know. 99 00:02:57,020 --> 00:02:57,860 It's really cool. 100 00:02:57,860 --> 00:02:59,400 Indigo, the dye. 101 00:02:59,400 --> 00:03:02,360 So it might seem counterintuitive, but it worked. 102 00:03:02,360 --> 00:03:04,280 The teenager avoided surgery 103 00:03:04,280 --> 00:03:06,920 and they've been in remission for three years now. 104 00:03:06,920 --> 00:03:10,280 So it's like AI is all about connecting the dots, 105 00:03:10,280 --> 00:03:12,360 sometimes in ways that we just wouldn't think of. 106 00:03:12,360 --> 00:03:13,200 Right. 107 00:03:13,200 --> 00:03:14,200 Which is what makes it so powerful. 108 00:03:14,200 --> 00:03:15,040 Absolutely. 109 00:03:15,040 --> 00:03:16,600 It's not just medication though, right? 110 00:03:16,600 --> 00:03:19,520 It can actually help us refine surgical techniques. 111 00:03:19,520 --> 00:03:20,360 Totally. 112 00:03:20,360 --> 00:03:24,200 And the seminar talked about a study on AI-assisted suturing. 113 00:03:24,200 --> 00:03:25,200 This was in pigs. 114 00:03:25,200 --> 00:03:26,040 Okay. 115 00:03:26,040 --> 00:03:29,080 And the AI actually helped make the suturing 116 00:03:29,080 --> 00:03:32,960 much more precise, which led to better overall outcomes. 117 00:03:32,960 --> 00:03:34,840 So you're thinking robot surgeons in the future? 118 00:03:34,840 --> 00:03:35,840 Potentially, yeah. 119 00:03:35,840 --> 00:03:37,000 That's wild, okay. 120 00:03:37,000 --> 00:03:39,120 But with all this amazing potential, 121 00:03:39,120 --> 00:03:42,200 I mean, I can't help but think about the downsides too. 122 00:03:42,200 --> 00:03:44,960 What about those biases that we always hear about with AI? 123 00:03:44,960 --> 00:03:45,800 Yeah. 124 00:03:45,800 --> 00:03:48,600 I mean, it's really important to understand that AI systems, 125 00:03:48,600 --> 00:03:50,920 they learn from the data that they are given. 126 00:03:50,920 --> 00:03:53,560 And if that data reflects societal biases, 127 00:03:53,560 --> 00:03:57,280 then the AI system might perpetuate them, unfortunately. 128 00:03:57,280 --> 00:03:59,600 So if there are already disparities in healthcare, 129 00:03:59,600 --> 00:04:03,040 the AI could unintentionally make those disparities worse. 130 00:04:03,040 --> 00:04:04,400 That's right. 131 00:04:04,400 --> 00:04:06,640 Think about something like maternal mortality rates, 132 00:04:06,640 --> 00:04:08,200 which are already significantly higher 133 00:04:08,200 --> 00:04:10,120 for certain demographics. 134 00:04:10,120 --> 00:04:10,960 Right. 135 00:04:10,960 --> 00:04:14,920 If an AI is trained on data that reflects those disparities, 136 00:04:14,920 --> 00:04:18,040 then it might not recommend the best course of treatment 137 00:04:18,040 --> 00:04:19,360 for everyone equally. 138 00:04:19,360 --> 00:04:22,880 So it's like the AI is inheriting the same biases 139 00:04:22,880 --> 00:04:24,600 that already exist in the real world. 140 00:04:24,600 --> 00:04:25,440 Exactly. 141 00:04:25,440 --> 00:04:26,360 That's unsettling. 142 00:04:26,360 --> 00:04:29,680 And we also need to consider representation 143 00:04:29,680 --> 00:04:32,040 in genetic studies and clinical trials. 144 00:04:32,040 --> 00:04:32,880 Right. 145 00:04:32,880 --> 00:04:35,280 Because if certain populations are underrepresented 146 00:04:35,280 --> 00:04:39,720 in that data, then the AI might not be as effective for them. 147 00:04:39,720 --> 00:04:40,560 Yeah. 148 00:04:40,560 --> 00:04:42,320 So it's not just about developing the AI, 149 00:04:42,320 --> 00:04:44,800 it's about making sure that it's trained on data 150 00:04:44,800 --> 00:04:47,200 that's truly representative of everyone. 151 00:04:47,200 --> 00:04:48,040 Absolutely. 152 00:04:48,040 --> 00:04:49,760 If we want AI to benefit all of us, 153 00:04:49,760 --> 00:04:51,160 it needs to learn from a data set 154 00:04:51,160 --> 00:04:53,040 that's diverse and inclusive. 155 00:04:53,040 --> 00:04:54,680 And the research you provided, 156 00:04:54,680 --> 00:04:56,800 it actually included some real world examples 157 00:04:56,800 --> 00:04:58,760 that really drive this point home, 158 00:04:58,760 --> 00:05:02,240 like that New Zealand passport photo algorithm. 159 00:05:02,240 --> 00:05:03,080 Oh yeah. 160 00:05:03,080 --> 00:05:04,680 There was that case where it failed 161 00:05:04,680 --> 00:05:06,920 to recognize a man's open eyes. 162 00:05:06,920 --> 00:05:08,640 Probably because the training data 163 00:05:08,640 --> 00:05:10,280 didn't include enough images 164 00:05:10,280 --> 00:05:12,200 of people from his ethnicity. 165 00:05:12,200 --> 00:05:13,840 That's so frustrating. 166 00:05:13,840 --> 00:05:16,160 Like imagine being told that your eyes aren't open 167 00:05:16,160 --> 00:05:18,960 when they clearly are, just because a computer program 168 00:05:18,960 --> 00:05:20,640 doesn't recognize your face properly. 169 00:05:20,640 --> 00:05:21,480 Right. 170 00:05:21,480 --> 00:05:22,320 It can have real world impacts. 171 00:05:22,320 --> 00:05:23,960 And there was that other case with, 172 00:05:24,840 --> 00:05:27,640 you know, a recidivism prediction algorithm 173 00:05:27,640 --> 00:05:28,920 that was used in the justice system. 174 00:05:28,920 --> 00:05:29,760 Yeah. 175 00:05:29,760 --> 00:05:31,560 And it turned out that it was assigning 176 00:05:31,560 --> 00:05:34,160 higher risk scores to black defendants. 177 00:05:34,160 --> 00:05:35,000 Wow. 178 00:05:35,000 --> 00:05:37,440 Even if they didn't reoffend compared to white defendants 179 00:05:37,440 --> 00:05:38,800 with similar histories. 180 00:05:38,800 --> 00:05:39,680 That's a little scary. 181 00:05:39,680 --> 00:05:42,480 It really emphasizes the need to, 182 00:05:42,480 --> 00:05:45,440 to not just blindly trust these AI systems. 183 00:05:45,440 --> 00:05:46,120 Right. 184 00:05:46,120 --> 00:05:48,080 Without, without understanding their limitations 185 00:05:48,080 --> 00:05:50,640 because the consequences can be, can be really serious. 186 00:05:50,640 --> 00:05:51,480 Yeah. Absolutely. 187 00:05:51,480 --> 00:05:52,800 AI is a powerful tool. 188 00:05:52,800 --> 00:05:53,640 Yeah. 189 00:05:53,640 --> 00:05:55,480 But like any tool, it can be misused. 190 00:05:55,480 --> 00:05:58,360 So how do we, how do we ensure that AI and medicine 191 00:05:58,360 --> 00:06:01,960 is used ethically and for the good of everyone? 192 00:06:01,960 --> 00:06:04,480 Well, it really starts with how we design 193 00:06:04,480 --> 00:06:05,960 and train these systems. 194 00:06:05,960 --> 00:06:08,880 We need to make sure that they're trained on data 195 00:06:08,880 --> 00:06:11,920 that is diverse and representative. 196 00:06:11,920 --> 00:06:15,040 And we need to be very aware of the potential for bias. 197 00:06:15,040 --> 00:06:16,880 It's almost like raising a child, right? 198 00:06:16,880 --> 00:06:19,000 You have to, you have to teach them the right values. 199 00:06:19,000 --> 00:06:21,240 You have to make sure that they're exposed to, 200 00:06:21,240 --> 00:06:23,000 to a wide range of experiences. 201 00:06:23,000 --> 00:06:24,120 That's a great analogy. 202 00:06:24,120 --> 00:06:24,960 Yeah. 203 00:06:24,960 --> 00:06:26,400 Like we need to be just as careful 204 00:06:26,400 --> 00:06:29,240 about the information that we feed these AI systems 205 00:06:29,240 --> 00:06:31,560 as we are about the information that we give, 206 00:06:31,560 --> 00:06:32,840 you know, to our children. 207 00:06:32,840 --> 00:06:34,240 And we need to be vigilant. 208 00:06:34,240 --> 00:06:35,080 Mm-hmm. 209 00:06:35,080 --> 00:06:35,920 Right? 210 00:06:35,920 --> 00:06:37,360 We can't just assume that AI is gonna solve 211 00:06:37,360 --> 00:06:39,800 all our problems without creating any new ones. 212 00:06:39,800 --> 00:06:40,640 Exactly. 213 00:06:40,640 --> 00:06:43,920 AI is a powerful tool, but it's not a magic bullet. 214 00:06:43,920 --> 00:06:44,760 Right. 215 00:06:44,760 --> 00:06:46,960 It's not a test to use it wisely and responsibly. 216 00:06:46,960 --> 00:06:49,240 So I'm curious, like what are some specific things 217 00:06:49,240 --> 00:06:52,000 that we can do to mitigate bias 218 00:06:52,000 --> 00:06:55,240 and ensure that AI is being used for good in medicine? 219 00:06:55,240 --> 00:06:57,920 Well, I think one crucial step is carefully 220 00:06:57,920 --> 00:07:00,000 framing the problem for the AI. 221 00:07:00,000 --> 00:07:02,160 Like we need to be incredibly clear 222 00:07:02,160 --> 00:07:04,440 about what we're asking the AI to do 223 00:07:04,440 --> 00:07:06,960 and make sure that its goals really align 224 00:07:06,960 --> 00:07:08,880 with our ethical considerations. 225 00:07:08,880 --> 00:07:11,480 Like imagine you're giving instructions to someone. 226 00:07:11,480 --> 00:07:13,080 Like when I order my coffee. 227 00:07:13,080 --> 00:07:13,920 Okay. 228 00:07:13,920 --> 00:07:15,240 And then we need to be very specific. 229 00:07:15,240 --> 00:07:16,080 Exactly. 230 00:07:16,080 --> 00:07:19,240 We have to be just as precise when we're instructing AI, 231 00:07:19,240 --> 00:07:21,720 making sure it understands our priorities 232 00:07:21,720 --> 00:07:25,720 and that it doesn't inadvertently produce biased outcomes. 233 00:07:25,720 --> 00:07:27,200 So, you know, we've talked about the importance 234 00:07:27,200 --> 00:07:29,840 of diverse and representative training data. 235 00:07:29,840 --> 00:07:30,680 Right. 236 00:07:30,680 --> 00:07:32,520 Which, you know, it's kind of like how clinical trials 237 00:07:32,520 --> 00:07:34,320 need to have a variety of participants. 238 00:07:34,320 --> 00:07:35,160 Absolutely. 239 00:07:35,160 --> 00:07:37,080 It's all about reflecting the real world. 240 00:07:37,080 --> 00:07:37,920 Right. 241 00:07:37,920 --> 00:07:39,240 Not just the narrow sliver of it. 242 00:07:39,240 --> 00:07:41,120 And then there's this other really interesting aspect 243 00:07:41,120 --> 00:07:45,160 using AI to actually detect and quantify bias itself. 244 00:07:45,160 --> 00:07:46,000 Oh, interesting. 245 00:07:46,000 --> 00:07:46,840 Yeah. 246 00:07:46,840 --> 00:07:49,960 Like we can analyze how an algorithm's output changes 247 00:07:49,960 --> 00:07:53,320 when we, you know, tweak factors like race or gender. 248 00:07:53,320 --> 00:07:56,280 And that can help us pinpoint potential biases. 249 00:07:56,280 --> 00:07:59,480 So we can actually use AI to fight against bias. 250 00:07:59,480 --> 00:08:00,320 Right. Exactly. 251 00:08:00,320 --> 00:08:01,160 That's incredible. 252 00:08:01,160 --> 00:08:02,000 Yeah. 253 00:08:02,000 --> 00:08:02,840 It's just the beginning. 254 00:08:02,840 --> 00:08:04,920 And as AI continues to evolve, 255 00:08:04,920 --> 00:08:06,600 we're going to find even more ways 256 00:08:06,600 --> 00:08:09,880 to ensure its ethical and responsible use. 257 00:08:09,880 --> 00:08:11,760 This has been really eye-opening conversation. 258 00:08:11,760 --> 00:08:14,120 And we've only just scratched the surface. 259 00:08:14,120 --> 00:08:15,960 But before we jump into part two, 260 00:08:15,960 --> 00:08:18,800 I want to leave our listeners with something to think about. 261 00:08:18,800 --> 00:08:22,200 Imagine that there's a highly accurate AI melanoma detection 262 00:08:22,200 --> 00:08:23,600 app available right now. 263 00:08:23,600 --> 00:08:24,440 Uh-huh. 264 00:08:24,440 --> 00:08:25,280 Would you use it? 265 00:08:25,280 --> 00:08:26,360 If not, why? 266 00:08:26,360 --> 00:08:28,440 What factors would influence your decision? 267 00:08:28,440 --> 00:08:29,280 That's a great question. 268 00:08:29,280 --> 00:08:31,240 It really gets to the heart of this whole idea 269 00:08:31,240 --> 00:08:33,800 of how we balance technological advancement 270 00:08:33,800 --> 00:08:36,760 with personal agency and trust in medical AI. 271 00:08:36,760 --> 00:08:38,560 And, you know, there are no easy answers, 272 00:08:38,560 --> 00:08:40,520 but it's definitely a conversation worth having. 273 00:08:40,520 --> 00:08:41,360 Definitely. 274 00:08:41,360 --> 00:08:42,760 We'll be back with part two soon. 275 00:08:42,760 --> 00:08:45,000 We'll delve even deeper into other exciting aspects 276 00:08:45,000 --> 00:08:46,560 of AI and medicine. 277 00:08:46,560 --> 00:08:47,520 So stay tuned. 278 00:08:47,520 --> 00:08:49,640 Back again for more of the deep dive. 279 00:08:49,640 --> 00:08:51,080 So we're really getting into it now, 280 00:08:51,080 --> 00:08:53,400 exploring the potential of AI and medicine. 281 00:08:53,400 --> 00:08:55,480 And one thing that stood out in that Harvard seminar 282 00:08:55,480 --> 00:08:58,720 transcript, it was how the role of doctors 283 00:08:58,720 --> 00:09:00,960 is going to change in this future with AI. 284 00:09:00,960 --> 00:09:04,880 I mean, are we going to see, like, robot doctors anytime soon? 285 00:09:04,880 --> 00:09:06,640 Well, that's the question everybody's asking. 286 00:09:06,640 --> 00:09:10,400 But the seminar really emphasized that AI is, 287 00:09:10,400 --> 00:09:12,960 it's more like a highly skilled assistant, you know? 288 00:09:12,960 --> 00:09:13,960 Right. 289 00:09:13,960 --> 00:09:17,560 It's really about augmenting doctors' abilities, 290 00:09:17,560 --> 00:09:19,880 not necessarily replacing them entirely. 291 00:09:19,880 --> 00:09:20,380 OK. 292 00:09:20,380 --> 00:09:22,480 So think of it more as a powerful tool that 293 00:09:22,480 --> 00:09:25,160 helps doctors do their jobs even better. 294 00:09:25,160 --> 00:09:27,880 So like a super-powered sidekick for doctors. 295 00:09:27,880 --> 00:09:28,760 Exactly. 296 00:09:28,760 --> 00:09:30,680 OK, that makes me feel a little bit better. 297 00:09:30,680 --> 00:09:33,800 But in what ways, like, what are some specific ways 298 00:09:33,800 --> 00:09:37,000 that this sidekick could actually change how doctors work? 299 00:09:37,000 --> 00:09:41,560 Well, imagine if AI could handle all of those data-heavy tasks. 300 00:09:41,560 --> 00:09:45,720 You know, pulling up relevant research, analyzing lab results, 301 00:09:45,720 --> 00:09:48,640 even suggesting potential diagnoses based on a patient 302 00:09:48,640 --> 00:09:51,280 symptoms that would free up doctors to really focus 303 00:09:51,280 --> 00:09:54,320 on what they do best, which is interacting with patients, 304 00:09:54,320 --> 00:09:56,240 applying their years of experience, 305 00:09:56,240 --> 00:09:58,080 making those nuanced decisions. 306 00:09:58,080 --> 00:10:00,160 So it's like having a super-efficient research 307 00:10:00,160 --> 00:10:02,240 assistant who can just instantly give you all 308 00:10:02,240 --> 00:10:03,240 the information you need. 309 00:10:03,240 --> 00:10:03,740 Exactly. 310 00:10:03,740 --> 00:10:05,680 So you can spend more time actually talking 311 00:10:05,680 --> 00:10:07,880 to the patient, understanding their concerns. 312 00:10:07,880 --> 00:10:08,360 Right. 313 00:10:08,360 --> 00:10:10,280 That personalized approach is really key. 314 00:10:10,280 --> 00:10:13,160 It helps us move away from that one-size-fits-all model 315 00:10:13,160 --> 00:10:13,720 of medicine. 316 00:10:13,720 --> 00:10:14,320 Right. 317 00:10:14,320 --> 00:10:16,080 And toward a more tailored approach, 318 00:10:16,080 --> 00:10:18,800 where treatments are really designed for each patient's 319 00:10:18,800 --> 00:10:20,560 unique circumstances and needs. 320 00:10:20,560 --> 00:10:22,160 And that actually reminds us of the story 321 00:10:22,160 --> 00:10:24,520 we were talking about earlier about the teenager 322 00:10:24,520 --> 00:10:26,120 with ulcerative colitis. 323 00:10:26,120 --> 00:10:29,280 Like, the AI didn't just spit out a general treatment 324 00:10:29,280 --> 00:10:30,280 recommendation. 325 00:10:30,280 --> 00:10:33,680 It actually analyzed that patient's specific gene 326 00:10:33,680 --> 00:10:37,400 expression data to figure out what was going to work for them. 327 00:10:37,400 --> 00:10:37,720 Yeah. 328 00:10:37,720 --> 00:10:39,800 And that's just the, I mean, that's one example. 329 00:10:39,800 --> 00:10:40,200 Yeah. 330 00:10:40,200 --> 00:10:41,400 Think about drug development. 331 00:10:41,400 --> 00:10:45,200 Like, AI can identify subgroups of patients 332 00:10:45,200 --> 00:10:49,440 who are most likely to benefit from a particular medication, 333 00:10:49,440 --> 00:10:52,280 even if those medications weren't effective in larger, 334 00:10:52,280 --> 00:10:54,240 more general trials. 335 00:10:54,240 --> 00:10:57,520 So that has huge implications for how we design and test 336 00:10:57,520 --> 00:10:58,320 new therapies. 337 00:10:58,320 --> 00:11:01,040 So instead of spending years and millions of dollars 338 00:11:01,040 --> 00:11:03,720 on drug trials that ultimately might not work, 339 00:11:03,720 --> 00:11:07,120 you could actually use AI to target those trials more 340 00:11:07,120 --> 00:11:09,160 effectively and bring life-saving medications 341 00:11:09,160 --> 00:11:10,240 to people faster. 342 00:11:10,240 --> 00:11:11,160 Exactly. 343 00:11:11,160 --> 00:11:11,720 Yeah. 344 00:11:11,720 --> 00:11:13,840 It's really, it's really amazing what it can do. 345 00:11:13,840 --> 00:11:15,920 And it's not just drug development. 346 00:11:15,920 --> 00:11:19,280 The seminar also talked about how AI can help doctors 347 00:11:19,280 --> 00:11:22,480 stay up to date, you know, with this ever-growing mountain 348 00:11:22,480 --> 00:11:23,600 of medical knowledge. 349 00:11:23,600 --> 00:11:24,720 I can imagine. 350 00:11:24,720 --> 00:11:26,360 Keeping up with all the latest research, 351 00:11:26,360 --> 00:11:28,560 it must feel like drinking from a fire hose. 352 00:11:28,560 --> 00:11:30,080 It really is, yeah. 353 00:11:30,080 --> 00:11:32,360 So AI can kind of act as a filter. 354 00:11:32,360 --> 00:11:32,800 OK. 355 00:11:32,800 --> 00:11:35,080 For that fire hose of information, 356 00:11:35,080 --> 00:11:37,040 highlighting the most relevant studies, 357 00:11:37,040 --> 00:11:40,440 flagging new developments that might impact a doctor's practice. 358 00:11:40,440 --> 00:11:42,600 So it's like having a personalized medical journal. 359 00:11:42,600 --> 00:11:43,240 That's pretty cool. 360 00:11:43,240 --> 00:11:44,960 It would save doctors so much time. 361 00:11:44,960 --> 00:11:45,360 Right. 362 00:11:45,360 --> 00:11:46,840 And it would help ensure that they're not 363 00:11:46,840 --> 00:11:47,960 missing out on anything important. 364 00:11:47,960 --> 00:11:49,200 Yeah, absolutely. 365 00:11:49,200 --> 00:11:49,560 OK. 366 00:11:49,560 --> 00:11:51,760 So it all sounds pretty amazing. 367 00:11:51,760 --> 00:11:56,480 But I know there are also concerns about the potential 368 00:11:56,480 --> 00:11:58,840 downsides of AI in medicine. 369 00:11:58,840 --> 00:11:59,680 What are those? 370 00:11:59,680 --> 00:12:00,080 Yeah. 371 00:12:00,080 --> 00:12:03,160 Well, of course, we have to be aware of those potential pitfalls. 372 00:12:03,160 --> 00:12:06,600 I think one concern is the risk of overreliance on AI, 373 00:12:06,600 --> 00:12:08,480 you know, where doctors might start seeing it 374 00:12:08,480 --> 00:12:11,680 as the sort of magical solution to every medical problem. 375 00:12:11,680 --> 00:12:15,040 And they might start losing their critical thinking skills. 376 00:12:15,040 --> 00:12:15,440 Right. 377 00:12:15,440 --> 00:12:17,360 So it's important to remember that it's a tool. 378 00:12:17,360 --> 00:12:18,080 Yes. 379 00:12:18,080 --> 00:12:19,800 Not a replacement for human judgment. 380 00:12:19,800 --> 00:12:20,480 Exactly. 381 00:12:20,480 --> 00:12:21,280 And expertise. 382 00:12:21,280 --> 00:12:24,720 Yeah, it's like relying solely on your GPS navigation. 383 00:12:24,720 --> 00:12:25,200 Right. 384 00:12:25,200 --> 00:12:25,680 Yeah. 385 00:12:25,680 --> 00:12:28,320 It might get you to your destination most of the time, 386 00:12:28,320 --> 00:12:30,360 but you still need to be able to think for yourself 387 00:12:30,360 --> 00:12:32,360 and figure out alternative routes 388 00:12:32,360 --> 00:12:35,920 if the GPS sends you down a dead end street. 389 00:12:35,920 --> 00:12:37,280 That's a good analogy. 390 00:12:37,280 --> 00:12:41,520 I also worry about AI negatively impacting 391 00:12:41,520 --> 00:12:43,200 that doctor-patient relationship. 392 00:12:43,200 --> 00:12:43,680 Right. 393 00:12:43,680 --> 00:12:47,080 Which is, I mean, so much of it is built on trust and empathy. 394 00:12:47,080 --> 00:12:47,720 Yeah. 395 00:12:47,720 --> 00:12:50,080 And I feel like if patients start to feel like they're 396 00:12:50,080 --> 00:12:54,200 interacting more with a machine than with a human, 397 00:12:54,200 --> 00:12:56,520 that that could really erode that bond of trust. 398 00:12:56,520 --> 00:12:57,640 It's a valid concern. 399 00:12:57,640 --> 00:12:59,160 But you know, I think AI can also 400 00:12:59,160 --> 00:13:01,040 be used to enhance that relationship. 401 00:13:01,040 --> 00:13:02,440 Because if doctors have more time, 402 00:13:02,440 --> 00:13:04,680 they can actually spend more time with their patients 403 00:13:04,680 --> 00:13:06,480 really listening to their concerns. 404 00:13:06,480 --> 00:13:08,320 So it really comes down to how we use it. 405 00:13:08,320 --> 00:13:08,720 Right. 406 00:13:08,720 --> 00:13:11,160 It can be a powerful tool for good. 407 00:13:11,160 --> 00:13:13,480 But it's up to us to make sure it's 408 00:13:13,480 --> 00:13:16,840 being used in a way that benefits both doctors and patients. 409 00:13:16,840 --> 00:13:17,600 Exactly. 410 00:13:17,600 --> 00:13:19,840 It's about striking that balance, you know, 411 00:13:19,840 --> 00:13:22,920 between technological advancement and that human 412 00:13:22,920 --> 00:13:24,280 touch in medicine. 413 00:13:24,280 --> 00:13:26,160 And speaking of human touch, I mean, 414 00:13:26,160 --> 00:13:29,560 we can't ignore the issue of access and equity. 415 00:13:29,560 --> 00:13:30,160 Right. 416 00:13:30,160 --> 00:13:33,760 AI-powered health care needs to be accessible to everyone. 417 00:13:33,760 --> 00:13:34,360 Absolutely. 418 00:13:34,360 --> 00:13:37,840 Regardless of their socioeconomic status or their background. 419 00:13:37,840 --> 00:13:38,840 Yeah. 420 00:13:38,840 --> 00:13:41,640 We need to be mindful of those potential disparities 421 00:13:41,640 --> 00:13:44,800 and work towards a future where everyone benefits 422 00:13:44,800 --> 00:13:48,320 from the incredible potential of AI in medicine. 423 00:13:48,320 --> 00:13:53,360 It's about making sure that AI serves humanity as a whole, 424 00:13:53,360 --> 00:13:54,880 not just a privileged few. 425 00:13:54,880 --> 00:13:55,480 Absolutely. 426 00:13:55,480 --> 00:13:57,720 And I think we need to talk about regulation and oversight 427 00:13:57,720 --> 00:13:58,360 as well. 428 00:13:58,360 --> 00:14:01,400 As AI becomes more integrated into health care, 429 00:14:01,400 --> 00:14:04,080 we need to make sure that we have robust frameworks in place 430 00:14:04,080 --> 00:14:05,960 to ensure that these systems are safe, 431 00:14:05,960 --> 00:14:08,120 that they're effective, that they're being used ethically. 432 00:14:08,120 --> 00:14:10,520 It's a complex, constantly evolving field. 433 00:14:10,520 --> 00:14:11,800 We can't be complacent. 434 00:14:11,800 --> 00:14:14,080 So it's not just about developing the technology. 435 00:14:14,080 --> 00:14:16,760 It's about making sure we use it responsibly. 436 00:14:16,760 --> 00:14:19,360 And I think that leads to one of the biggest questions that 437 00:14:19,360 --> 00:14:21,200 was raised in the seminar, which is, 438 00:14:21,200 --> 00:14:23,240 who's accountable when AI goes wrong? 439 00:14:23,240 --> 00:14:26,760 If an AI system makes a mistake that harms a patient, 440 00:14:26,760 --> 00:14:27,880 who do we blame? 441 00:14:27,880 --> 00:14:29,720 That's the million dollar question, right? 442 00:14:29,720 --> 00:14:32,960 It's such a tangled web of responsibility. 443 00:14:32,960 --> 00:14:34,680 You have the doctor, the developer, the hospital. 444 00:14:34,680 --> 00:14:35,600 They all play a role. 445 00:14:35,600 --> 00:14:40,440 It's a really complex issue that legal scholars and ethicists 446 00:14:40,440 --> 00:14:42,320 are really grappling with right now. 447 00:14:42,320 --> 00:14:43,920 It reminds me of all those discussions 448 00:14:43,920 --> 00:14:45,320 about self-driving cars. 449 00:14:45,320 --> 00:14:45,840 Right. 450 00:14:45,840 --> 00:14:47,760 Like if a self-driving car gets into an accident, 451 00:14:47,760 --> 00:14:48,440 who's at fault? 452 00:14:48,440 --> 00:14:48,940 Right. 453 00:14:48,940 --> 00:14:53,040 The passenger, the manufacturer, the software developer. 454 00:14:53,040 --> 00:14:55,120 It's a similar dilemma. 455 00:14:55,120 --> 00:14:55,520 Exactly. 456 00:14:55,520 --> 00:14:58,280 And in health care, the stakes are obviously even higher. 457 00:14:58,280 --> 00:14:58,560 Right. 458 00:14:58,560 --> 00:15:01,680 Because we're talking about people's lives and well-being. 459 00:15:01,680 --> 00:15:03,360 So what can we do? 460 00:15:03,360 --> 00:15:06,760 How do we ensure accountability, protect patients 461 00:15:06,760 --> 00:15:10,280 in this new era of AI-driven medicine? 462 00:15:10,280 --> 00:15:11,960 Well, I think we need to be proactive. 463 00:15:11,960 --> 00:15:15,400 Like, rigorous testing and validation of AI systems, 464 00:15:15,400 --> 00:15:17,800 are they're ever used in a clinical setting? 465 00:15:17,800 --> 00:15:20,640 And having clear lines of responsibility and legal 466 00:15:20,640 --> 00:15:23,720 frameworks to address any unintended consequences, 467 00:15:23,720 --> 00:15:25,080 I think that's really important. 468 00:15:25,080 --> 00:15:27,160 It sounds like we're in uncharted territory here. 469 00:15:27,160 --> 00:15:27,880 We are. 470 00:15:27,880 --> 00:15:29,600 But that makes it all the more important 471 00:15:29,600 --> 00:15:31,560 to be having these conversations now. 472 00:15:31,560 --> 00:15:34,600 Before AI becomes so deeply integrated into health care 473 00:15:34,600 --> 00:15:36,680 that it's too late to sort of course correct. 474 00:15:36,680 --> 00:15:37,320 Right. 475 00:15:37,320 --> 00:15:40,960 We need to make sure that AI is used responsibly 476 00:15:40,960 --> 00:15:42,920 and that patients are protected. 477 00:15:42,920 --> 00:15:46,200 And that includes being transparent with patients, 478 00:15:46,200 --> 00:15:46,880 right? 479 00:15:46,880 --> 00:15:48,280 Letting them know how these systems work, 480 00:15:48,280 --> 00:15:50,560 giving them a voice in their own care. 481 00:15:50,560 --> 00:15:51,880 Absolutely, yeah. 482 00:15:51,880 --> 00:15:54,040 Transparency and trust are essential 483 00:15:54,040 --> 00:15:57,560 for any kind of successful integration of AI 484 00:15:57,560 --> 00:15:58,680 into health care. 485 00:15:58,680 --> 00:16:01,120 This deep dive is really highlighting just how much 486 00:16:01,120 --> 00:16:03,120 there is to consider. 487 00:16:03,120 --> 00:16:06,120 AI has the potential to revolutionize medicine. 488 00:16:06,120 --> 00:16:09,240 But we have to approach it thoughtfully, ethically, 489 00:16:09,240 --> 00:16:11,960 with this human-centered focus. 490 00:16:11,960 --> 00:16:13,480 So what else did the seminar talk about? 491 00:16:13,480 --> 00:16:15,520 Well, it also raised this really fascinating question 492 00:16:15,520 --> 00:16:18,560 of how AI might actually change the very definition of what 493 00:16:18,560 --> 00:16:19,800 it means to be a doctor. 494 00:16:19,800 --> 00:16:20,800 Oh, that's interesting. 495 00:16:20,800 --> 00:16:24,080 So as AI takes on more of those tasks that doctors 496 00:16:24,080 --> 00:16:27,720 traditionally do, what does it mean to be a doctor? 497 00:16:27,720 --> 00:16:32,960 Like if AI can diagnose diseases and even perform surgery, 498 00:16:32,960 --> 00:16:34,440 what's left for the humans to do? 499 00:16:34,440 --> 00:16:36,000 That's the big question. 500 00:16:36,000 --> 00:16:38,400 And there's no easy answers. 501 00:16:38,400 --> 00:16:40,840 But the seminar suggested that maybe 502 00:16:40,840 --> 00:16:44,200 the role of the doctor might evolve to focus more 503 00:16:44,200 --> 00:16:48,080 on guiding and counseling and advocating for patients. 504 00:16:48,080 --> 00:16:53,160 It's about embracing those uniquely human qualities 505 00:16:53,160 --> 00:16:56,000 of empathy and communication and understanding 506 00:16:56,000 --> 00:16:58,720 a patient's individual needs and values. 507 00:16:58,720 --> 00:17:02,160 So even in a world of AI-driven health care, 508 00:17:02,160 --> 00:17:05,400 that human connection is still at the heart of it all. 509 00:17:05,400 --> 00:17:07,520 That's the key takeaway. 510 00:17:07,520 --> 00:17:09,480 AI is this powerful tool, but it's 511 00:17:09,480 --> 00:17:11,600 up to us to use it in a way that really 512 00:17:11,600 --> 00:17:14,560 complements and enhances that human element of health care, 513 00:17:14,560 --> 00:17:15,440 not replace it. 514 00:17:15,440 --> 00:17:17,400 This has been such a thought-provoking conversation, 515 00:17:17,400 --> 00:17:19,200 but I feel like we've just scratched the surface. 516 00:17:19,200 --> 00:17:19,760 I know. 517 00:17:19,760 --> 00:17:20,800 What's coming up in part three? 518 00:17:20,800 --> 00:17:22,800 Well, in part three, we're going to shift gears a bit 519 00:17:22,800 --> 00:17:25,960 and delve into that groundbreaking research 520 00:17:25,960 --> 00:17:28,680 that you provided on AI's potential 521 00:17:28,680 --> 00:17:31,760 to unlock new treatments and therapies, 522 00:17:31,760 --> 00:17:34,520 taking personalized medicine to a whole new level. 523 00:17:34,520 --> 00:17:35,520 Ooh, can't wait. 524 00:17:35,520 --> 00:17:37,520 Sounds exciting. 525 00:17:37,520 --> 00:17:39,000 All right, we're back for the final part 526 00:17:39,000 --> 00:17:41,920 of our deep dive into AI in medicine. 527 00:17:41,920 --> 00:17:44,120 And this time, we're zeroing in on something 528 00:17:44,120 --> 00:17:46,280 I know you're particularly interested in, 529 00:17:46,280 --> 00:17:49,360 this cutting-edge research into AI's potential 530 00:17:49,360 --> 00:17:51,520 for developing new treatments and therapies. 531 00:17:51,520 --> 00:17:52,800 Yeah, it's an area that's really 532 00:17:52,800 --> 00:17:55,320 generating a lot of excitement, both among researchers 533 00:17:55,320 --> 00:17:56,280 and clinicians. 534 00:17:56,280 --> 00:17:58,480 I've been going through those research excerpts you sent, 535 00:17:58,480 --> 00:18:00,760 and I've got to say, I'm pretty blown away 536 00:18:00,760 --> 00:18:01,920 by the possibilities. 537 00:18:01,920 --> 00:18:05,560 AI is already changing how we approach drug discovery 538 00:18:05,560 --> 00:18:07,400 and treatment development, and we're really just 539 00:18:07,400 --> 00:18:08,200 at the beginning. 540 00:18:08,200 --> 00:18:09,800 One thing that really struck me was just 541 00:18:09,800 --> 00:18:13,040 how AI can analyze massive amounts of data. 542 00:18:13,040 --> 00:18:15,880 We're talking everything from genetic information 543 00:18:15,880 --> 00:18:19,120 and clinical records to lifestyle factors. 544 00:18:19,120 --> 00:18:22,320 It's like having a whole team of super sleuths combing 545 00:18:22,320 --> 00:18:23,600 through mountains of evidence. 546 00:18:23,600 --> 00:18:24,320 Oh, well, but. 547 00:18:24,320 --> 00:18:25,880 Trying to crack the case of disease. 548 00:18:25,880 --> 00:18:27,400 Yeah, that's a great analogy. 549 00:18:27,400 --> 00:18:29,800 AI can identify these patterns and connections 550 00:18:29,800 --> 00:18:31,880 that humans might just completely miss, 551 00:18:31,880 --> 00:18:33,720 and that can lead to all sorts of new insights 552 00:18:33,720 --> 00:18:37,240 into the causes of disease and potential treatment avenues. 553 00:18:37,240 --> 00:18:41,720 I was particularly fascinated by the research on AI's role 554 00:18:41,720 --> 00:18:44,440 in developing new treatments for Alzheimer's. 555 00:18:44,440 --> 00:18:46,720 It seems like we've been hitting a wall with those traditional 556 00:18:46,720 --> 00:18:47,320 approaches. 557 00:18:47,320 --> 00:18:48,160 Yeah. 558 00:18:48,160 --> 00:18:51,840 But maybe AI is the key to unlocking some new breaksers. 559 00:18:51,840 --> 00:18:54,760 Alzheimer's is such a complex disease, 560 00:18:54,760 --> 00:18:58,480 and it has been really tough to develop effective treatments. 561 00:18:58,480 --> 00:19:00,840 But AI does offer some new hope. 562 00:19:00,840 --> 00:19:01,840 In what way? 563 00:19:01,840 --> 00:19:05,240 Well, AI can analyze brain imaging data, 564 00:19:05,240 --> 00:19:08,800 genetic profiles, cognitive test results, all of that 565 00:19:08,800 --> 00:19:11,720 to identify those early signs of Alzheimer's 566 00:19:11,720 --> 00:19:14,360 and even predict how the disease might progress. 567 00:19:14,360 --> 00:19:16,960 This could lead to interventions that could slow down 568 00:19:16,960 --> 00:19:19,520 or even prevent the onset of symptoms. 569 00:19:19,520 --> 00:19:22,360 So instead of waiting for the disease to really take hold, 570 00:19:22,360 --> 00:19:24,800 you could potentially intervene much earlier. 571 00:19:24,800 --> 00:19:25,280 Exactly. 572 00:19:25,280 --> 00:19:27,480 And maybe even change the trajectory of the disease 573 00:19:27,480 --> 00:19:28,200 entirely. 574 00:19:28,200 --> 00:19:28,880 That's the idea. 575 00:19:28,880 --> 00:19:30,960 And this isn't just limited to Alzheimer's either. 576 00:19:30,960 --> 00:19:34,000 AI is being applied to such a wide range of conditions. 577 00:19:34,000 --> 00:19:36,160 Everything from cancer and heart disease 578 00:19:36,160 --> 00:19:38,120 to rare genetic disorders. 579 00:19:38,120 --> 00:19:40,160 The research mentioned a case where AI 580 00:19:40,160 --> 00:19:43,280 was used to create a personalized cancer treatment. 581 00:19:43,280 --> 00:19:44,120 Oh, wow. 582 00:19:44,120 --> 00:19:46,360 Based on the patient's unique genetic profile, 583 00:19:46,360 --> 00:19:48,400 it's almost like having a drug that's 584 00:19:48,400 --> 00:19:49,640 tailor made just for you. 585 00:19:49,640 --> 00:19:51,760 Yeah, that's the promise of precision medicine, right? 586 00:19:51,760 --> 00:19:52,440 Right. 587 00:19:52,440 --> 00:19:55,520 Using AI to analyze a patient's tumor DNA, 588 00:19:55,520 --> 00:19:57,520 identify the specific mutations that 589 00:19:57,520 --> 00:19:59,600 are driving that cancer, and then predict 590 00:19:59,600 --> 00:20:02,040 which drugs are most likely to be effective. 591 00:20:02,040 --> 00:20:04,800 It's a completely different approach to cancer treatment. 592 00:20:04,800 --> 00:20:06,520 It sounds like something out of science fiction. 593 00:20:06,520 --> 00:20:06,920 I know. 594 00:20:06,920 --> 00:20:07,880 It's pretty incredible. 595 00:20:07,880 --> 00:20:09,000 But it's happening right now. 596 00:20:09,000 --> 00:20:09,400 It is. 597 00:20:09,400 --> 00:20:10,400 It's really happening. 598 00:20:10,400 --> 00:20:14,680 And AI is also revolutionizing drug discovery, 599 00:20:14,680 --> 00:20:17,880 helping us identify promising new compounds, 600 00:20:17,880 --> 00:20:21,200 speeding up the development of life-saving medications. 601 00:20:21,200 --> 00:20:22,800 I mean, traditionally, drug discovery 602 00:20:22,800 --> 00:20:25,160 has been such a long and expensive process. 603 00:20:25,160 --> 00:20:25,840 It really has. 604 00:20:25,840 --> 00:20:27,960 With no guarantee of success. 605 00:20:27,960 --> 00:20:29,640 But it seems like AI has the potential 606 00:20:29,640 --> 00:20:31,520 to completely change that. 607 00:20:31,520 --> 00:20:32,720 Think of it this way. 608 00:20:32,720 --> 00:20:34,960 AI can act like a virtual chemist. 609 00:20:34,960 --> 00:20:35,440 OK. 610 00:20:35,440 --> 00:20:38,480 Analyzing huge libraries of chemical compounds 611 00:20:38,480 --> 00:20:41,760 and predicting which ones might have therapeutic effects. 612 00:20:41,760 --> 00:20:44,600 So that could drastically reduce the time and cost 613 00:20:44,600 --> 00:20:46,000 of bringing new drugs to market. 614 00:20:46,000 --> 00:20:47,080 Yeah, potentially. 615 00:20:47,080 --> 00:20:49,440 That's incredible, making life-saving treatments 616 00:20:49,440 --> 00:20:51,480 available to patients so much sooner. 617 00:20:51,480 --> 00:20:52,640 It's really exciting. 618 00:20:52,640 --> 00:20:55,200 But we do need to proceed with caution. 619 00:20:55,200 --> 00:20:58,160 We need to ensure that AI is being developed and used 620 00:20:58,160 --> 00:21:00,400 responsibly, ethically. 621 00:21:00,400 --> 00:21:02,920 That's something that's come up a lot in our conversation. 622 00:21:02,920 --> 00:21:04,600 It's such an important point. 623 00:21:04,600 --> 00:21:08,200 As powerful as AI is, it's still just a tool. 624 00:21:08,200 --> 00:21:10,280 And like any tool, it can be misused. 625 00:21:10,280 --> 00:21:10,960 Right. 626 00:21:10,960 --> 00:21:12,880 One concern is that AI could actually 627 00:21:12,880 --> 00:21:15,240 worsen existing health disparities. 628 00:21:15,240 --> 00:21:16,400 Oh, how so? 629 00:21:16,400 --> 00:21:19,080 Well, if access to AI-powered health care 630 00:21:19,080 --> 00:21:21,680 is limited to certain groups, it could actually 631 00:21:21,680 --> 00:21:25,120 widen that gap between those who have access to the best care 632 00:21:25,120 --> 00:21:26,240 and those who don't. 633 00:21:26,240 --> 00:21:29,960 So it's really crucial that the benefits of AI and medicine 634 00:21:29,960 --> 00:21:31,320 are accessible to everyone. 635 00:21:31,320 --> 00:21:31,840 Absolutely. 636 00:21:31,840 --> 00:21:35,000 No matter their background, socioeconomic status, 637 00:21:35,000 --> 00:21:35,840 all of that. 638 00:21:35,840 --> 00:21:36,280 Right. 639 00:21:36,280 --> 00:21:39,920 And we also have to be mindful of potential biases 640 00:21:39,920 --> 00:21:43,400 in the data that's used to train these AI systems. 641 00:21:43,400 --> 00:21:45,960 We have to actively work to mitigate those biases. 642 00:21:45,960 --> 00:21:47,880 So if we're not careful, AI could 643 00:21:47,880 --> 00:21:50,160 end up perpetuating and even amplifying 644 00:21:50,160 --> 00:21:52,160 existing inequalities in health care. 645 00:21:52,160 --> 00:21:55,360 It all comes back to that idea of responsible development. 646 00:21:55,360 --> 00:21:58,640 Deployment of AI, making sure it serves humanity as a whole. 647 00:21:58,640 --> 00:21:59,480 Absolutely. 648 00:21:59,480 --> 00:22:04,040 AI has incredible potential to improve health care for everyone. 649 00:22:04,040 --> 00:22:06,640 But it's up to us to make sure that happens. 650 00:22:06,640 --> 00:22:09,080 Well, this deep dive has been quite the journey. 651 00:22:09,080 --> 00:22:11,520 We've explored the possibilities, the challenges, 652 00:22:11,520 --> 00:22:12,920 of AI and medicine. 653 00:22:12,920 --> 00:22:14,680 We've talked about the potential of AI 654 00:22:14,680 --> 00:22:16,080 to discover new treatments. 655 00:22:16,080 --> 00:22:18,080 We've talked about the ethical considerations, 656 00:22:18,080 --> 00:22:19,320 equitable access. 657 00:22:19,320 --> 00:22:22,600 We've really seen how AI is already changing health care 658 00:22:22,600 --> 00:22:25,280 and how it will continue to shape the future of medicine. 659 00:22:25,280 --> 00:22:27,120 Yeah, it's been a pleasure diving deep with you. 660 00:22:27,120 --> 00:22:28,720 Hopefully, this conversation has given you 661 00:22:28,720 --> 00:22:31,920 a better understanding of this complex and rapidly evolving 662 00:22:31,920 --> 00:22:33,680 world of AI and medicine. 663 00:22:33,680 --> 00:22:37,080 We hope you found this deep dive informative and engaging. 664 00:22:37,080 --> 00:22:39,120 We encourage you to keep exploring this topic, 665 00:22:39,120 --> 00:22:42,280 keep asking questions, and be an active participant 666 00:22:42,280 --> 00:22:44,480 in shaping the future of AI in health care. 667 00:22:44,480 --> 00:22:46,840 And remember, just like with any new technology, 668 00:22:46,840 --> 00:22:48,640 it's so important to stay informed 669 00:22:48,640 --> 00:22:51,240 and to engage in those thoughtful discussions 670 00:22:51,240 --> 00:22:53,600 about the potential benefits and risks. 671 00:22:53,600 --> 00:22:59,040 Thank you for joining us on the deep dive.