WEBVTT 1 00:00:06.273 --> 00:00:08.374 Artificial intelligence in oncology is booming. 2 00:00:08.594 --> 00:00:09.855 We have public programs, 3 00:00:09.935 --> 00:00:10.955 private partnerships, 4 00:00:11.756 --> 00:00:13.076 and massive volumes of data. 5 00:00:14.017 --> 00:00:16.098 China is closely observing European research. 6 00:00:16.598 --> 00:00:18.439 Natalie Lasso tells us more about it. 7 00:00:18.739 --> 00:00:23.161 She says she is particularly impressed by the commitment of the Gustav Rusi Institute, 8 00:00:23.421 --> 00:00:26.382 but cutting-edge research does not necessarily guarantee patient access. 9 00:00:26.582 --> 00:00:30.244 The patient caught between academic excellence and industrial deployment. 10 00:00:30.484 --> 00:00:31.985 remains in a fragile position. 11 00:00:32.085 --> 00:00:33.606 The situation is still precarious. 12 00:00:35.447 --> 00:00:37.548 This is the gap I invite you to look at today. 13 00:00:39.389 --> 00:00:40.449 If these topics interest you, 14 00:00:40.610 --> 00:00:43.151 feel free to subscribe to follow the entire series. 15 00:00:44.051 --> 00:00:46.773 So we no longer work in silos when it comes to imaging. 16 00:00:47.373 --> 00:00:49.494 We do liquid biopsies and imaging. 17 00:00:50.095 --> 00:00:52.236 It's also useful for all pharmaceutical companies. 18 00:00:52.296 --> 00:00:53.557 Now everyone is joining in. 19 00:00:54.157 --> 00:00:58.099 How did you first get started in the fields of liquid biopsy and AI? 20 00:00:58.779 --> 00:00:59.580 Is there a formula? 21 00:00:59.960 --> 00:01:01.161 It's true that at this year's 22 00:01:01.601 --> 00:01:02.361 Chicago conference, 23 00:01:02.381 --> 00:01:04.642 we proved we're actually the first in the world to do this. 24 00:01:04.722 --> 00:01:05.582 We're real pioneers. 25 00:01:06.002 --> 00:01:09.924 We presented a study of 1,000 patients and 55,000 metastases. 26 00:01:10.264 --> 00:01:11.585 A Chinese man in the room said, 27 00:01:11.885 --> 00:01:12.705 that's impossible. 28 00:01:12.725 --> 00:01:13.426 You didn't do that. 29 00:01:13.506 --> 00:01:14.726 It's impossible to do that. 30 00:01:15.006 --> 00:01:17.107 How did you manage to get French people to work together? 31 00:01:17.487 --> 00:01:20.148 It was an artificial intelligence lab at Central Suppilic, 32 00:01:20.368 --> 00:01:21.049 the CVN, 33 00:01:21.269 --> 00:01:22.029 and also Okin, 34 00:01:22.289 --> 00:01:22.929 with whom I worked. 35 00:01:23.129 --> 00:01:23.750 Then I told them, 36 00:01:23.970 --> 00:01:24.790 we're going to do a study, 37 00:01:24.870 --> 00:01:26.811 and I would like to use each of your technologies, 38 00:01:26.851 --> 00:01:27.551 which were different. 39 00:01:27.931 --> 00:01:29.252 One of them was working on machine. 40 00:01:29.472 --> 00:01:29.712 learning. 41 00:01:29.713 --> 00:01:30.493 So in deep learning, 42 00:01:30.793 --> 00:01:31.993 I told them it's a bet. 43 00:01:32.534 --> 00:01:33.294 May the best one win. 44 00:01:33.554 --> 00:01:35.355 We'll take the best technology and use it. 45 00:01:36.256 --> 00:01:36.476 Hello, 46 00:01:36.796 --> 00:01:37.316 everyone. 47 00:01:38.077 --> 00:01:38.317 Today, 48 00:01:38.337 --> 00:01:40.838 I am joined here by Professor Nathalie Lassau. 49 00:01:41.579 --> 00:01:49.203 She is a highly distinguished expert in the specialized care of cancer patients and a radiologist currently practicing at the Institute. 50 00:01:49.443 --> 00:01:50.343 Gustave Roussy, 51 00:01:50.443 --> 00:01:51.184 a professor, 52 00:01:51.624 --> 00:01:55.646 PUPH at the University of Saclay and also a researcher. 53 00:01:56.006 --> 00:01:58.127 So I am delighted to be with you today 54 00:01:58.528 --> 00:01:58.968 Nathalie 55 00:01:59.168 --> 00:01:59.408 Hello. 56 00:01:59.909 --> 00:02:00.109 Hello. 57 00:02:00.289 --> 00:02:01.210 It is nice to meet you. 58 00:02:01.290 --> 00:02:02.351 Thank you for the warm welcome. 59 00:02:02.711 --> 00:02:09.336 The reason I came to the Gustave Roussy Institute today to have this conversation is because you have a fascinating background, 60 00:02:09.737 --> 00:02:10.918 an incredible career, 61 00:02:11.378 --> 00:02:13.700 but also a truly unique personality. 62 00:02:14.200 --> 00:02:16.322 And we're really going to try to understand that. 63 00:02:16.582 --> 00:02:18.264 Maybe we can start by introducing this place, 64 00:02:18.265 --> 00:02:19.925 the Gustave Roussy Institute. 65 00:02:20.305 --> 00:02:23.048 So the Gustave Roussy Institute is a truly incredible place. 66 00:02:23.728 --> 00:02:24.369 And in the end, 67 00:02:25.690 --> 00:02:26.510 it draws people in. 68 00:02:27.031 --> 00:02:28.012 That's how I found myself. 69 00:02:28.612 --> 00:02:29.792 incredibly attracted to it. 70 00:02:29.852 --> 00:02:30.793 Once I fell into the pot, 71 00:02:30.873 --> 00:02:32.333 I just never managed to get back out. 72 00:02:32.853 --> 00:02:34.234 That's the story of Gustave Roussy. 73 00:02:34.434 --> 00:02:36.054 It's a place of genuine passion, 74 00:02:36.094 --> 00:02:36.774 of vocation, 75 00:02:36.954 --> 00:02:40.475 of wonderful exchanges with patients that makes you never want to leave. 76 00:02:40.555 --> 00:02:41.556 Especially in oncology, 77 00:02:41.896 --> 00:02:43.556 you form a very strong bond. 78 00:02:43.896 --> 00:02:44.737 And that's why I love it. 79 00:02:45.017 --> 00:02:45.457 Ultrasound, 80 00:02:45.517 --> 00:02:47.217 I'm not from the CT or MRI side. 81 00:02:47.637 --> 00:02:50.458 That's how I was also hired at Gustave as an ultrasound specialist. 82 00:02:50.718 --> 00:02:51.899 I was really immersed in it. 83 00:02:52.339 --> 00:02:52.779 And for me, 84 00:02:52.899 --> 00:02:54.519 it was through research that I managed. 85 00:02:54.539 --> 00:02:55.039 I knew it. 86 00:02:55.380 --> 00:02:56.760 So I had to innovate. 87 00:02:56.920 --> 00:02:57.480 As a result, 88 00:02:57.520 --> 00:02:58.801 I invented some patents. 89 00:02:59.802 --> 00:03:01.303 These were bought by Toshiba, 90 00:03:01.383 --> 00:03:01.963 for example, 91 00:03:02.023 --> 00:03:05.985 which made me known worldwide and established my reputation. 92 00:03:06.505 --> 00:03:08.487 I love being a pioneer in what I do. 93 00:03:08.747 --> 00:03:10.788 We've just filed another patent with Gerbit. 94 00:03:11.348 --> 00:03:12.669 I really like being a pioneer, 95 00:03:12.769 --> 00:03:15.971 being the first demonstrating things and then passing them on, 96 00:03:16.031 --> 00:03:18.372 spreading them so that they benefit patients. 97 00:03:18.892 --> 00:03:22.615 And it is definitely true that I have this drive to discover new things, 98 00:03:22.995 --> 00:03:24.996 to prove things that we might have suspected. 99 00:03:25.708 --> 00:03:28.709 but that hadn't yet been demonstrated and I absolutely love that. 100 00:03:29.570 --> 00:03:32.371 If I look back at the stories about Doppler ultrasound, 101 00:03:32.951 --> 00:03:38.113 there's still this fundamental aspect taking on topics no matter what they are. 102 00:03:38.393 --> 00:03:39.233 It's about cancer, 103 00:03:39.634 --> 00:03:42.635 ultimately looking for things and staying curious, 104 00:03:43.395 --> 00:03:47.697 but as a result not necessarily doing what everyone else does or what's most trendy right now. 105 00:03:48.197 --> 00:03:48.377 Yes, 106 00:03:48.417 --> 00:03:49.758 it's about moving into innovation, 107 00:03:50.178 --> 00:03:50.638 that's for sure. 108 00:03:50.738 --> 00:03:53.239 It's true that I'm I'm vice president for innovation and all that. 109 00:03:54.139 --> 00:03:55.240 It's really about innovating. 110 00:03:55.688 --> 00:03:57.870 innovating in imaging or innovating for the patient, 111 00:03:58.230 --> 00:03:59.551 innovating with oncologists, 112 00:03:59.611 --> 00:04:00.352 with new drugs. 113 00:04:00.672 --> 00:04:02.554 I had worked a lot with pharmaceutical companies. 114 00:04:03.494 --> 00:04:09.439 So it's about seeking different topics and combining them because medicine is very compartmentalized and siloed. 115 00:04:09.900 --> 00:04:11.581 You have radiologists on one side, 116 00:04:12.001 --> 00:04:13.162 oncologists on the other. 117 00:04:14.023 --> 00:04:14.403 Early on, 118 00:04:14.503 --> 00:04:16.485 I worked closely with oncologists, 119 00:04:16.585 --> 00:04:18.346 sometimes even more than with radiologists, 120 00:04:18.507 --> 00:04:19.888 because you had to understand their issues. 121 00:04:20.248 --> 00:04:20.568 Obviously, 122 00:04:20.628 --> 00:04:21.889 what did they need for their patients? 123 00:04:22.210 --> 00:04:25.032 A tool that would allow in early stage trials. 124 00:04:25.352 --> 00:04:27.073 to know whether a drug was working or not. 125 00:04:27.533 --> 00:04:27.913 So I thought, 126 00:04:27.953 --> 00:04:28.193 okay, 127 00:04:28.273 --> 00:04:33.335 I'm going to develop my technique that will help the oncologist determine when a pharmaceutical company proposes a new drug, 128 00:04:33.415 --> 00:04:34.996 whether we can quickly know if it works or not. 129 00:04:35.776 --> 00:04:36.196 That was it. 130 00:04:36.736 --> 00:04:38.817 It's a very maybe practical approach as well. 131 00:04:39.257 --> 00:04:39.677 Pragmatic, 132 00:04:39.717 --> 00:04:40.738 you've got it exactly right. 133 00:04:41.138 --> 00:04:44.219 It is not something that is driven by the expectations of other people. 134 00:04:44.959 --> 00:04:46.940 It is really just more about being basic. 135 00:04:47.997 --> 00:04:48.717 That's exactly it. 136 00:04:49.138 --> 00:04:50.858 Every time I talk to my young colleagues, 137 00:04:50.898 --> 00:04:51.679 I always tell them, 138 00:04:52.199 --> 00:04:52.439 okay, 139 00:04:52.539 --> 00:04:53.980 what is the question being asked? 140 00:04:54.340 --> 00:04:55.800 We do a study at the end, 141 00:04:55.820 --> 00:05:00.202 we publish and we deliver a result that allows us to better monitor patients, 142 00:05:00.262 --> 00:05:02.323 better select patients or better assist them. 143 00:05:02.823 --> 00:05:04.464 We need something concrete for the patients. 144 00:05:04.824 --> 00:05:05.324 That is to say, 145 00:05:05.365 --> 00:05:08.426 there are so many beautiful publications with little significant p-values, 146 00:05:08.786 --> 00:05:10.587 but the oncologist can't do anything with them. 147 00:05:11.727 --> 00:05:13.168 What I want every time is to say, 148 00:05:13.248 --> 00:05:13.528 okay, 149 00:05:13.628 --> 00:05:14.608 we're doing research, 150 00:05:14.669 --> 00:05:15.589 but afterwards, 151 00:05:16.029 --> 00:05:18.711 It has to be usable in routine practice for patients. 152 00:05:19.171 --> 00:05:19.571 That's it. 153 00:05:19.931 --> 00:05:21.852 And that the end of the road is always complicated. 154 00:05:22.292 --> 00:05:23.533 We realized that in research, 155 00:05:23.793 --> 00:05:25.034 a lot of things are developed, 156 00:05:25.174 --> 00:05:27.475 but they remain pending and are never applied. 157 00:05:28.136 --> 00:05:28.556 And the end, 158 00:05:28.596 --> 00:05:29.416 which is always hard, 159 00:05:29.516 --> 00:05:30.117 ultimately to, 160 00:05:30.617 --> 00:05:31.157 can we use it? 161 00:05:31.257 --> 00:05:33.018 Can we implement it routinely for patients? 162 00:05:33.198 --> 00:05:34.119 It's always hard at the end. 163 00:05:35.179 --> 00:05:35.920 It's complicated. 164 00:05:36.360 --> 00:05:37.361 And that's the hardest part. 165 00:05:38.161 --> 00:05:41.383 And that's the strength and value of what you do and what is recognized. 166 00:05:41.803 --> 00:05:43.344 Why does it come to an end too soon? 167 00:05:43.784 --> 00:05:45.305 Because it's hard to implement at the end. 168 00:05:45.633 --> 00:05:47.094 People stop right after they finish. 169 00:05:47.334 --> 00:05:47.455 No, 170 00:05:47.595 --> 00:05:48.916 you must see things through to the end. 171 00:05:49.176 --> 00:05:50.877 You have to go all the way for the patient. 172 00:05:51.318 --> 00:05:55.201 You have to put yourself in the clinician's or the patient's shoes and say, 173 00:05:55.301 --> 00:05:55.521 okay, 174 00:05:55.561 --> 00:05:58.704 what I'm developing could change the doctor's life or the patient's life, 175 00:05:59.044 --> 00:06:00.145 and how can I change it? 176 00:06:00.525 --> 00:06:02.467 It's in their hands or all the doctors now. 177 00:06:02.827 --> 00:06:03.167 Exactly, 178 00:06:03.227 --> 00:06:03.568 exactly, 179 00:06:03.648 --> 00:06:03.968 exactly. 180 00:06:04.549 --> 00:06:08.872 And we realize that there are a lot of things that have been published that never made it to the end. 181 00:06:09.252 --> 00:06:14.637 So maybe it's because people don't have that tenacity to see things through to the end in some way. 182 00:06:14.837 --> 00:06:15.878 Do you have concrete examples? 183 00:06:16.118 --> 00:06:16.458 I don't know. 184 00:06:16.699 --> 00:06:17.859 A publication where you say, 185 00:06:17.980 --> 00:06:18.300 actually, 186 00:06:18.660 --> 00:06:19.601 I could have stopped there, 187 00:06:19.661 --> 00:06:19.941 but no, 188 00:06:19.961 --> 00:06:20.522 I kept going. 189 00:06:20.702 --> 00:06:20.842 Yes, 190 00:06:20.862 --> 00:06:21.422 for example, 191 00:06:21.423 --> 00:06:22.543 the technique I invented. 192 00:06:23.004 --> 00:06:23.484 You could say, 193 00:06:23.644 --> 00:06:23.864 okay, 194 00:06:23.925 --> 00:06:24.165 great, 195 00:06:24.225 --> 00:06:25.106 I filed a patent, 196 00:06:25.126 --> 00:06:25.686 it got bought. 197 00:06:26.427 --> 00:06:26.547 No, 198 00:06:26.687 --> 00:06:28.528 the idea was to say it has to be used, 199 00:06:28.529 --> 00:06:30.190 it has to be included in the guidelines. 200 00:06:30.470 --> 00:06:31.090 The next step, 201 00:06:31.491 --> 00:06:32.331 after I had done, 202 00:06:32.392 --> 00:06:32.732 I don't know, 203 00:06:32.812 --> 00:06:33.773 four or five studies, 204 00:06:33.853 --> 00:06:34.914 go right to the guideline. 205 00:06:35.274 --> 00:06:35.454 Yes, 206 00:06:35.514 --> 00:06:35.814 that's it, 207 00:06:35.815 --> 00:06:36.475 the recognition. 208 00:06:36.495 --> 00:06:36.755 You say, 209 00:06:36.835 --> 00:06:37.015 okay, 210 00:06:37.055 --> 00:06:39.297 this is the global reference used in various contexts. 211 00:06:39.698 --> 00:06:42.520 I had done several single center studies and could have said that's enough. 212 00:06:42.580 --> 00:06:43.661 They're in my publications. 213 00:06:44.129 --> 00:06:44.449 But no, 214 00:06:44.609 --> 00:06:45.290 I told myself, 215 00:06:45.710 --> 00:06:46.930 for it to be truly recognized, 216 00:06:47.250 --> 00:06:49.031 it's essential to have a multi-center study. 217 00:06:49.411 --> 00:06:52.613 I went to see INCA when there were major calls for proposals. 218 00:06:53.073 --> 00:06:55.914 I responded to a major call for 1.5 million euros. 219 00:06:56.375 --> 00:06:59.496 I brought together 19 teams in France from cancer centers, 220 00:06:59.936 --> 00:07:01.037 university hospitals, 221 00:07:01.237 --> 00:07:01.797 and so on. 222 00:07:02.197 --> 00:07:03.818 I told them I'd invented a technique, 223 00:07:03.898 --> 00:07:06.499 but we had to prove it works beyond Gustave Roussy, 224 00:07:06.879 --> 00:07:08.380 across France with residents, 225 00:07:08.480 --> 00:07:09.020 professors, 226 00:07:09.040 --> 00:07:09.901 and department heads. 227 00:07:10.337 --> 00:07:12.818 We conducted a large study on 500 patients, 228 00:07:12.898 --> 00:07:13.878 followed for six years, 229 00:07:14.758 --> 00:07:20.160 and we demonstrated the relevance of the tool that it helped to predict the effectiveness of drugs that destroy blood vessels. 230 00:07:20.200 --> 00:07:23.601 We validated a biomarker because we correlated it with survival. 231 00:07:24.021 --> 00:07:26.802 And afterwards it was referenced in a nature review clinical oncology, 232 00:07:27.542 --> 00:07:30.043 and it's included in the international ultrasound guidelines. 233 00:07:30.723 --> 00:07:31.483 So it was really, 234 00:07:31.983 --> 00:07:32.603 this is truly, 235 00:07:32.964 --> 00:07:33.364 I search, 236 00:07:33.424 --> 00:07:33.844 I find, 237 00:07:33.864 --> 00:07:34.404 and I apply. 238 00:07:35.144 --> 00:07:37.665 So it took quite a long time to develop. 239 00:07:37.805 --> 00:07:43.190 because the original patent dates all the way back to 2005 and the multicenter publications, 240 00:07:43.490 --> 00:07:45.051 all of that came out in 2015. 241 00:07:45.472 --> 00:07:46.472 So it took 10 years. 242 00:07:46.953 --> 00:07:47.733 That's when it gets tough. 243 00:07:47.974 --> 00:07:48.794 So at that point, 244 00:07:48.834 --> 00:07:49.435 you can't give up. 245 00:07:50.176 --> 00:07:50.476 First, 246 00:07:50.496 --> 00:07:52.638 you have to demonstrate proof of concept on a phantom, 247 00:07:52.718 --> 00:07:54.199 then do small studies on mice, 248 00:07:54.619 --> 00:07:56.081 monocentric studies on patients, 249 00:07:56.101 --> 00:07:57.021 the multicenter study, 250 00:07:57.402 --> 00:07:58.543 and then convince Toshiba. 251 00:07:59.183 --> 00:08:01.285 And now on ultrasound machines all over the world, 252 00:08:01.365 --> 00:08:03.287 my technique is implemented on the devices. 253 00:08:03.407 --> 00:08:03.827 All right. 254 00:08:04.027 --> 00:08:04.388 There you go. 255 00:08:04.428 --> 00:08:05.008 You can use it. 256 00:08:05.549 --> 00:08:05.849 All right. 257 00:08:06.473 --> 00:08:07.254 And the manufacturer, 258 00:08:07.314 --> 00:08:08.354 when did they come into the picture? 259 00:08:08.774 --> 00:08:09.315 At what point, 260 00:08:09.515 --> 00:08:10.475 right from the very beginning, 261 00:08:10.656 --> 00:08:10.956 Canon? 262 00:08:11.356 --> 00:08:13.077 Canon got involved pretty quickly. 263 00:08:13.517 --> 00:08:13.637 Well, 264 00:08:13.657 --> 00:08:15.318 it was Toshiba and now it's Canon. 265 00:08:15.698 --> 00:08:17.099 But back then it was Toshiba. 266 00:08:17.319 --> 00:08:18.620 So I was with Pierre Perronneau, 267 00:08:18.680 --> 00:08:19.641 the inventor of Doppler, 268 00:08:19.781 --> 00:08:21.362 and I had told him my idea and everything. 269 00:08:21.522 --> 00:08:23.303 And I had gone to present my first work. 270 00:08:23.683 --> 00:08:24.264 And at the time, 271 00:08:24.304 --> 00:08:24.544 actually, 272 00:08:24.584 --> 00:08:26.605 we were doing quantification with Photoshop. 273 00:08:26.805 --> 00:08:28.266 I don't know if you ever experienced that. 274 00:08:28.886 --> 00:08:29.807 I think you're not too young, 275 00:08:29.847 --> 00:08:31.148 but it was, 276 00:08:31.428 --> 00:08:31.668 you know, 277 00:08:31.768 --> 00:08:33.369 quantifying images with Photoshop. 278 00:08:33.869 --> 00:08:35.550 I presented it in the United States. 279 00:08:35.551 --> 00:08:37.871 It was a great study I had done on sarcomas, 280 00:08:38.912 --> 00:08:40.192 evaluating glyphosate, 281 00:08:40.232 --> 00:08:43.074 which had just come out from Superdrug Revolutionary and all that. 282 00:08:43.954 --> 00:08:44.275 And so 283 00:08:44.975 --> 00:08:47.276 I was quantifying my contrast echoes with Photoshop. 284 00:08:47.336 --> 00:08:50.738 I presented this at RSNA and there I got myself into a mess. 285 00:08:50.898 --> 00:08:54.060 It was a radiologist from MD Anderson back in Houston, 286 00:08:54.720 --> 00:08:56.101 someone who really tore me apart. 287 00:08:56.681 --> 00:08:57.262 He said to me, 288 00:08:57.582 --> 00:08:59.843 how can you propose quantification with Photoshop? 289 00:09:00.223 --> 00:09:01.024 I was completely, 290 00:09:01.704 --> 00:09:02.565 I wanted to cry, 291 00:09:02.725 --> 00:09:03.205 basically. 292 00:09:04.053 --> 00:09:06.154 It was done on 50 patients with sarcomas. 293 00:09:06.714 --> 00:09:07.054 I thought, 294 00:09:07.554 --> 00:09:08.815 I might as well just go home. 295 00:09:09.575 --> 00:09:10.875 I went to see Pierre Perronon, 296 00:09:11.075 --> 00:09:11.555 and he said, 297 00:09:12.116 --> 00:09:12.236 OK, 298 00:09:12.396 --> 00:09:12.796 Nathalie, 299 00:09:13.296 --> 00:09:16.237 we need the raw linear data from the manufacturers. 300 00:09:16.777 --> 00:09:21.338 So then we went to see every ultrasound manufacturer like GE and Shimatsu. 301 00:09:22.259 --> 00:09:22.979 And there we said, 302 00:09:23.019 --> 00:09:23.159 look, 303 00:09:23.199 --> 00:09:26.260 we want the raw linear data because we need to do real quantification. 304 00:09:26.980 --> 00:09:28.281 We can't just use Photoshop. 305 00:09:28.741 --> 00:09:29.081 So then... 306 00:09:29.709 --> 00:09:30.850 Most of the manufacturers said, 307 00:09:30.870 --> 00:09:30.990 no, 308 00:09:31.030 --> 00:09:31.150 no, 309 00:09:31.230 --> 00:09:31.930 that's classified. 310 00:09:31.931 --> 00:09:33.151 We don't give out the raw data. 311 00:09:33.251 --> 00:09:33.871 It's secret, 312 00:09:34.471 --> 00:09:36.112 except for the Japanese Toshiba, 313 00:09:36.272 --> 00:09:36.852 who agreed. 314 00:09:37.433 --> 00:09:38.033 It was a gamble, 315 00:09:38.113 --> 00:09:39.234 so they brought me to Japan. 316 00:09:39.354 --> 00:09:40.654 I explained everything and they said, 317 00:09:40.674 --> 00:09:40.794 OK, 318 00:09:40.994 --> 00:09:41.895 let's sign the contracts. 319 00:09:42.515 --> 00:09:42.795 All right, 320 00:09:42.835 --> 00:09:43.856 we'll give you the raw data. 321 00:09:44.156 --> 00:09:44.716 And from that, 322 00:09:44.796 --> 00:09:46.257 we were able to create the patent. 323 00:09:46.840 --> 00:09:47.581 So they followed us. 324 00:09:47.901 --> 00:09:49.282 They followed me all around the world. 325 00:09:49.382 --> 00:09:50.242 They gave me access. 326 00:09:50.903 --> 00:09:51.423 And to tell you, 327 00:09:51.443 --> 00:09:52.244 it took a long time. 328 00:09:52.564 --> 00:09:53.124 For example, 329 00:09:53.225 --> 00:09:53.965 just five years ago, 330 00:09:54.525 --> 00:09:55.106 all of a sudden, 331 00:09:55.446 --> 00:09:57.467 they announced at the big Congress in Chicago, 332 00:09:58.028 --> 00:09:58.408 that's it, 333 00:09:58.728 --> 00:10:00.630 we are giving access to the raw linear data. 334 00:10:00.910 --> 00:10:02.811 That was 15 years after what we had requested. 335 00:10:03.492 --> 00:10:06.434 But so we had managed to convince the Japanese. 336 00:10:06.554 --> 00:10:06.954 And for me, 337 00:10:07.014 --> 00:10:07.675 the Japanese, 338 00:10:07.815 --> 00:10:09.336 everyone says it's hard to work with them. 339 00:10:10.016 --> 00:10:11.017 It's hard to convince them. 340 00:10:11.277 --> 00:10:12.198 But the day it's okay. 341 00:10:12.778 --> 00:10:13.579 When you sign the deal, 342 00:10:13.639 --> 00:10:15.040 they're really funny and they stick with you. 343 00:10:15.416 --> 00:10:17.237 Their loyalty is absolutely unwavering. 344 00:10:17.577 --> 00:10:18.638 So it was hard to convince them. 345 00:10:18.698 --> 00:10:19.218 But after that, 346 00:10:19.278 --> 00:10:20.199 they've always been there. 347 00:10:20.639 --> 00:10:21.799 And they're still with us today. 348 00:10:22.260 --> 00:10:22.680 I've always, 349 00:10:22.940 --> 00:10:23.260 right now, 350 00:10:23.300 --> 00:10:24.601 I'm still a reference in ultrasound. 351 00:10:24.661 --> 00:10:25.522 And so is Gustav. 352 00:10:26.162 --> 00:10:27.102 I have machines on loan. 353 00:10:27.162 --> 00:10:29.704 I have a full-time engineer who's been paid for 10 years. 354 00:10:30.044 --> 00:10:30.984 And I keep publishing a lot. 355 00:10:31.004 --> 00:10:31.605 I work a lot. 356 00:10:31.965 --> 00:10:33.046 So there you have it. 357 00:10:33.126 --> 00:10:34.586 It's a partnership that's lasted. 358 00:10:34.686 --> 00:10:35.327 It's incredible. 359 00:10:35.687 --> 00:10:40.249 It's a really beautiful story of a partnership with industrialists who are straightforward, 360 00:10:40.309 --> 00:10:40.710 reliable, 361 00:10:40.750 --> 00:10:41.170 and loyal. 362 00:10:41.890 --> 00:10:42.511 The Japanese, 363 00:10:42.671 --> 00:10:44.172 it's true that they don't play tricks. 364 00:10:44.612 --> 00:10:44.832 That's... 365 00:10:45.404 --> 00:10:46.904 That's not the case with everyone. 366 00:10:47.045 --> 00:10:52.526 I now work with other industrial partners and it's true that at first I was criticized in radiology because they didn't understand. 367 00:10:52.546 --> 00:10:53.826 They said you have to work with everyone. 368 00:10:54.647 --> 00:10:55.027 I said no, 369 00:10:55.047 --> 00:11:00.988 if you're doing research very early stage and pioneering and if you really want to do R&D, 370 00:11:01.248 --> 00:11:03.069 you can't work with all the industrial partners. 371 00:11:03.369 --> 00:11:04.389 You have to make a choice. 372 00:11:04.609 --> 00:11:06.470 Afterwards you can't pick the wrong horse. 373 00:11:06.471 --> 00:11:09.151 You have to tell yourself it's the right industrial partner and all that. 374 00:11:09.551 --> 00:11:10.651 But in this case you can't do... 375 00:11:10.971 --> 00:11:14.112 Now I do more things with other industrial partners but on different topics. 376 00:11:14.564 --> 00:11:19.427 I always make sure to have a well-defined scope so there's no overlap because otherwise you ruin everything. 377 00:11:20.147 --> 00:11:22.348 And that's also the advantage of working at Gustav, 378 00:11:22.849 --> 00:11:27.732 having the possibility to work with all the industrial partners because it's also a research accelerator. 379 00:11:28.272 --> 00:11:28.432 Yes, 380 00:11:28.452 --> 00:11:31.254 it took 10 years but without them you would have done nothing. 381 00:11:31.714 --> 00:11:32.214 Well you... 382 00:11:33.055 --> 00:11:33.255 Yes, 383 00:11:33.335 --> 00:11:36.236 I wouldn't have filed my model after that and so what, 384 00:11:36.316 --> 00:11:36.857 nothing really. 385 00:11:37.317 --> 00:11:38.498 I'd just be planting cabbages. 386 00:11:38.918 --> 00:11:39.698 So indeed it's... 387 00:11:40.059 --> 00:11:40.179 No, 388 00:11:40.339 --> 00:11:40.879 but that's it. 389 00:11:41.059 --> 00:11:42.520 So in the end in PharmaMind... 390 00:11:42.700 --> 00:11:45.222 it's essentially personalities who make up the pharma world. 391 00:11:45.482 --> 00:11:51.146 So it's inevitably people who actually see the final stages of innovations when they arrive, 392 00:11:51.266 --> 00:11:53.307 either on the market or when they're about to arrive. 393 00:11:53.948 --> 00:11:54.828 But as a result, 394 00:11:54.848 --> 00:11:57.991 it's interesting to have something that's really upstream and to say to yourself, 395 00:11:58.051 --> 00:11:58.471 sometimes, 396 00:11:58.531 --> 00:11:58.731 yes, 397 00:11:58.911 --> 00:11:59.512 it's a gamble. 398 00:11:59.912 --> 00:12:00.392 It's a gamble. 399 00:12:00.813 --> 00:12:01.313 Afterwards, 400 00:12:01.373 --> 00:12:03.855 I've had one or two things that didn't work out or that I stopped. 401 00:12:04.195 --> 00:12:04.375 Yes, 402 00:12:04.435 --> 00:12:04.855 there's that, 403 00:12:04.975 --> 00:12:06.096 which I just picked up again. 404 00:12:06.677 --> 00:12:07.117 For example, 405 00:12:07.137 --> 00:12:08.938 there's molecular imaging with ultrasound. 406 00:12:09.659 --> 00:12:11.340 I filed a patent with some Americans. 407 00:12:12.132 --> 00:12:13.552 That was in 2003. 408 00:12:13.553 --> 00:12:14.833 I realized I wouldn't be able to do it. 409 00:12:15.413 --> 00:12:16.293 It cost too much money. 410 00:12:16.433 --> 00:12:17.073 My boss told me, 411 00:12:17.474 --> 00:12:18.454 we won't be able to make it. 412 00:12:18.734 --> 00:12:20.995 Yet I had raised the money and everything from the region, 413 00:12:21.095 --> 00:12:22.115 but I just couldn't do it. 414 00:12:22.315 --> 00:12:23.955 I stopped for a moment and put it aside, 415 00:12:24.356 --> 00:12:26.396 but it was still on my mind. 416 00:12:26.576 --> 00:12:28.077 It's molecular imaging. 417 00:12:29.117 --> 00:12:33.698 And then all of a sudden we managed to convince an industry partner because I'm more visible now. 418 00:12:34.218 --> 00:12:37.619 And so now we're going to do a phase one trial with patients. 419 00:12:38.640 --> 00:12:38.980 There you go. 420 00:12:39.080 --> 00:12:39.720 It's incredible. 421 00:12:40.388 --> 00:12:43.910 with a molecular ultrasound contrast agent that targets receptors. 422 00:12:44.450 --> 00:12:45.690 But you have to know when to stop. 423 00:12:45.810 --> 00:12:46.691 My boss always told me, 424 00:12:47.051 --> 00:12:47.351 Natalie, 425 00:12:47.411 --> 00:12:49.232 because he saw that I was still pretty stubborn. 426 00:12:49.692 --> 00:12:50.192 He said to me, 427 00:12:50.212 --> 00:12:50.332 OK, 428 00:12:50.452 --> 00:12:50.872 that's good. 429 00:12:51.312 --> 00:12:52.633 But sometimes you have to tell yourself, 430 00:12:52.753 --> 00:12:52.873 OK, 431 00:12:53.113 --> 00:12:54.134 now you need to stop. 432 00:12:54.154 --> 00:12:55.094 You have to cut the branch. 433 00:12:55.294 --> 00:12:55.634 Stop. 434 00:12:55.714 --> 00:12:56.735 You're wearing yourself out. 435 00:12:56.835 --> 00:12:58.295 And then you move on to other things. 436 00:12:58.435 --> 00:12:59.956 And maybe you'll come back to it later. 437 00:13:00.676 --> 00:13:01.377 And that project, 438 00:13:01.457 --> 00:13:01.617 yeah, 439 00:13:02.057 --> 00:13:02.777 it was picked up again. 440 00:13:02.837 --> 00:13:04.898 It started up again because there were problems. 441 00:13:05.398 --> 00:13:07.039 With gadolinium contrast, 442 00:13:07.339 --> 00:13:08.679 scanners used radiation. 443 00:13:08.940 --> 00:13:09.860 Now we have ultrasound. 444 00:13:10.080 --> 00:13:10.980 It's radiation free, 445 00:13:11.340 --> 00:13:13.481 infinitely repeatable and cheaper than a PET scan. 446 00:13:13.801 --> 00:13:15.161 We finally have the technology, 447 00:13:15.421 --> 00:13:17.442 so suddenly it's the right time. 448 00:13:17.522 --> 00:13:18.222 It's always like that, 449 00:13:18.242 --> 00:13:19.383 the stars must align. 450 00:13:20.023 --> 00:13:22.083 And now everything is lining up perfectly. 451 00:13:22.283 --> 00:13:24.404 So we're just starting again to see what happens. 452 00:13:25.124 --> 00:13:26.164 It's a massive gamble. 453 00:13:26.985 --> 00:13:27.705 It has to work. 454 00:13:28.745 --> 00:13:36.107 I would be truly happy to have conducted this molecular imaging study using ultrasound because it was something I really cared about at the time, 455 00:13:36.147 --> 00:13:37.747 but it was far too innovative, 456 00:13:37.808 --> 00:13:38.248 too early, 457 00:13:38.308 --> 00:13:39.228 I didn't have enough money. 458 00:13:39.668 --> 00:13:40.749 It just wasn't possible. 459 00:13:41.710 --> 00:13:43.571 And when the industry did not follow along, 460 00:13:43.591 --> 00:13:44.652 you still need the industry. 461 00:13:44.813 --> 00:13:46.214 We can do academic research, 462 00:13:46.454 --> 00:13:47.535 but in this day and age, 463 00:13:48.236 --> 00:13:51.378 you still need the powerful drive from industry and funding from industry. 464 00:13:51.538 --> 00:13:53.100 We need all three of these elements. 465 00:13:53.480 --> 00:13:54.701 You need power behind it. 466 00:13:55.102 --> 00:13:58.825 Something must be pushing from behind or we'll be stranded in the middle of nowhere. 467 00:13:59.125 --> 00:14:00.266 That is actually interesting. 468 00:14:00.486 --> 00:14:01.647 It's about knowing how to wait. 469 00:14:01.727 --> 00:14:03.049 That's something my bosses taught me. 470 00:14:03.469 --> 00:14:04.029 Bide your time, 471 00:14:04.109 --> 00:14:04.750 know how to wait. 472 00:14:05.090 --> 00:14:06.852 Since you have young people doing research with you. 473 00:14:07.112 --> 00:14:09.373 You shouldn't set them up for failure on those topics in the end. 474 00:14:09.693 --> 00:14:10.373 You think about that? 475 00:14:10.753 --> 00:14:10.933 Well, 476 00:14:11.033 --> 00:14:12.593 I'm still responsible for my little ones. 477 00:14:13.054 --> 00:14:13.914 We do great research, 478 00:14:13.954 --> 00:14:16.415 but then they need to find jobs and have a life of passion. 479 00:14:17.015 --> 00:14:18.355 And so at that point, 480 00:14:18.755 --> 00:14:20.436 I thought I couldn't take them down that path. 481 00:14:20.476 --> 00:14:21.576 It was kind of a dead end. 482 00:14:22.576 --> 00:14:22.956 So yeah, 483 00:14:23.776 --> 00:14:23.897 well, 484 00:14:23.957 --> 00:14:24.077 yeah, 485 00:14:24.117 --> 00:14:25.737 we're still responsible for the young people. 486 00:14:26.437 --> 00:14:27.738 You can't control everything. 487 00:14:27.758 --> 00:14:28.898 You can't do it all either. 488 00:14:29.478 --> 00:14:30.658 Even if with experience, 489 00:14:30.678 --> 00:14:31.819 you can know what's going to happen. 490 00:14:32.059 --> 00:14:33.159 It has to be at the right time. 491 00:14:33.919 --> 00:14:35.060 It has to be the right people. 492 00:14:35.220 --> 00:14:36.660 You have to analyze the ecosystem. 493 00:14:36.920 --> 00:14:38.002 You need to have a kind of vision. 494 00:14:38.363 --> 00:14:40.427 We can't control everything, 495 00:14:40.587 --> 00:14:41.288 never in life. 496 00:14:41.348 --> 00:14:41.789 But still. 497 00:14:42.310 --> 00:14:43.112 What does that really mean? 498 00:14:43.886 --> 00:14:44.306 Mentoring, 499 00:14:44.366 --> 00:14:46.207 guiding and leading a research group or students? 500 00:14:46.687 --> 00:14:46.807 Well, 501 00:14:46.847 --> 00:14:47.127 for me, 502 00:14:47.548 --> 00:14:49.408 guiding always means leading a research group. 503 00:14:49.548 --> 00:14:49.668 OK, 504 00:14:49.909 --> 00:14:50.849 we have our topics. 505 00:14:51.269 --> 00:14:54.250 And right now we're working on biomarkers in oncology, 506 00:14:55.031 --> 00:14:56.151 predictive prognosis. 507 00:14:56.631 --> 00:14:58.972 So I know what could be promising for patients, 508 00:14:59.012 --> 00:14:59.713 for oncology, 509 00:14:59.793 --> 00:15:02.414 for pharmaceutical companies and for imaging in general. 510 00:15:02.914 --> 00:15:04.695 Five or even 10 years gets more complicated. 511 00:15:05.135 --> 00:15:06.996 People who project 10 years ahead, 512 00:15:07.436 --> 00:15:08.036 I think that's, 513 00:15:08.736 --> 00:15:10.757 I like to have something concrete and pragmatic. 514 00:15:10.777 --> 00:15:12.198 So 10 years is too far for me. 515 00:15:13.006 --> 00:15:15.927 While research or ministers might plan that far ahead, 516 00:15:16.107 --> 00:15:17.327 that's not how I operate. 517 00:15:18.047 --> 00:15:20.888 Since I like to achieve concrete results beyond five years, 518 00:15:20.988 --> 00:15:22.449 it's hard to see anything tangible. 519 00:15:23.489 --> 00:15:24.929 I always tell young people 520 00:15:25.349 --> 00:15:27.570 I prefer to build my castle with small bricks first. 521 00:15:28.410 --> 00:15:32.491 Then I expand and make my first room rather than trying to build the big castle first. 522 00:15:33.052 --> 00:15:34.012 You have to be realistic. 523 00:15:34.432 --> 00:15:36.012 Even if you're visionary and a pioneer, 524 00:15:36.052 --> 00:15:37.073 you have to be realistic. 525 00:15:37.533 --> 00:15:38.853 And you always have to in the end, 526 00:15:38.933 --> 00:15:39.793 because for young people, 527 00:15:39.853 --> 00:15:41.614 we're here to help them fill out their CVs. 528 00:15:42.078 --> 00:15:43.279 That's how they're going to find a job. 529 00:15:43.399 --> 00:15:45.199 What remains in the end are the publications, 530 00:15:45.499 --> 00:15:48.461 patents or publications that catch someone's eye. 531 00:15:49.241 --> 00:15:49.621 Basically, 532 00:15:49.681 --> 00:15:50.021 that's it. 533 00:15:50.341 --> 00:15:50.522 Okay, 534 00:15:50.542 --> 00:15:51.282 you have the degrees. 535 00:15:51.702 --> 00:15:52.342 But when I look, 536 00:15:52.642 --> 00:15:54.463 I pay attention to which internships they've done, 537 00:15:54.823 --> 00:15:56.644 where they've been and which team they've worked with. 538 00:15:56.964 --> 00:15:58.225 Was the person able to publish? 539 00:15:58.465 --> 00:15:59.665 Is their name listed first? 540 00:16:00.046 --> 00:16:02.026 Did they keep a good pace in a fairly short time? 541 00:16:02.126 --> 00:16:03.047 That's what I focus on. 542 00:16:04.087 --> 00:16:05.008 Then after that, 543 00:16:05.148 --> 00:16:06.228 the desire to go further, 544 00:16:06.288 --> 00:16:07.309 the passion and all that. 545 00:16:07.709 --> 00:16:09.309 So as a result of this, 546 00:16:09.389 --> 00:16:10.490 the projects I start 547 00:16:10.922 --> 00:16:12.823 are things that span over five years. 548 00:16:13.544 --> 00:16:15.926 I need to achieve a solid result within five years, 549 00:16:16.246 --> 00:16:17.086 or in one year, 550 00:16:17.206 --> 00:16:17.827 or in two years, 551 00:16:17.947 --> 00:16:18.647 or in three years, 552 00:16:19.548 --> 00:16:21.069 but you have to achieve concrete results. 553 00:16:21.669 --> 00:16:23.271 So tell me what are the topics? 554 00:16:24.331 --> 00:16:25.372 So the topics right now, 555 00:16:25.412 --> 00:16:29.015 the themes I've chosen include molecular imaging and molecular ultrasound. 556 00:16:29.475 --> 00:16:31.196 That's really the ultrasound part, 557 00:16:31.556 --> 00:16:33.277 which is my little baby until the end, 558 00:16:33.638 --> 00:16:34.458 and I'll see it through. 559 00:16:35.359 --> 00:16:38.701 And then it's everything related to biomarkers, 560 00:16:38.761 --> 00:16:39.662 it's tumor burden. 561 00:16:40.470 --> 00:16:41.851 So now we're getting into a topic. 562 00:16:42.271 --> 00:16:45.372 For more than 20 years we've been explaining to radiologists, 563 00:16:45.692 --> 00:16:46.593 so internationally, 564 00:16:47.213 --> 00:16:51.135 we have a single rule all together to make sure we're talking about the same thing. 565 00:16:51.335 --> 00:16:52.015 That is to say, 566 00:16:52.415 --> 00:16:54.917 when we evaluate a patient who has several metastases, 567 00:16:54.997 --> 00:16:56.337 we say here is the rule, 568 00:16:56.797 --> 00:17:00.679 you will only measure five lesions and that way from one scan to another, 569 00:17:00.959 --> 00:17:02.320 no matter where the scan is done, 570 00:17:02.580 --> 00:17:03.761 we will measure in the same way. 571 00:17:04.121 --> 00:17:04.881 Except that you say, 572 00:17:04.981 --> 00:17:05.181 well, 573 00:17:05.481 --> 00:17:06.562 it's extremely restrictive. 574 00:17:06.978 --> 00:17:07.478 For example, 575 00:17:07.479 --> 00:17:10.199 the patient had multiple metastases but you only measure five of them. 576 00:17:10.519 --> 00:17:11.119 Up until now, 577 00:17:11.419 --> 00:17:12.840 that hasn't been a problem for anyone. 578 00:17:13.280 --> 00:17:13.800 Except now, 579 00:17:14.040 --> 00:17:15.400 liquid biopsy has arrived. 580 00:17:15.800 --> 00:17:16.561 Liquid biopsy, 581 00:17:16.781 --> 00:17:18.281 I've been talking about it non-stop, 582 00:17:18.881 --> 00:17:21.842 but it's a new tool that's going to revolutionize oncology. 583 00:17:22.342 --> 00:17:23.062 Up until now, 584 00:17:23.122 --> 00:17:24.203 it's been 10 years, 585 00:17:24.283 --> 00:17:25.443 I think even 15 years, 586 00:17:25.483 --> 00:17:27.404 that we've been talking about precision medicine. 587 00:17:28.124 --> 00:17:30.024 Millions and millions have been invested. 588 00:17:30.865 --> 00:17:31.425 Previously, 589 00:17:32.145 --> 00:17:34.186 we'd biopsy a patient's lesion, 590 00:17:34.726 --> 00:17:35.506 take a sample, 591 00:17:36.122 --> 00:17:38.063 examine the cells and say, 592 00:17:38.623 --> 00:17:41.203 it's this type of cancer or abnormality. 593 00:17:41.984 --> 00:17:43.544 Now we've broken things down quite a bit. 594 00:17:44.044 --> 00:17:44.584 For example, 595 00:17:44.664 --> 00:17:45.725 lung and breast cancers, 596 00:17:46.025 --> 00:17:48.285 all categorized by the genome, 597 00:17:48.586 --> 00:17:50.006 except there's a gap in the system. 598 00:17:50.606 --> 00:17:54.267 We realize that if you do a biopsy somewhere else on another lesion, 599 00:17:54.847 --> 00:17:56.568 you don't necessarily get a good result. 600 00:17:56.988 --> 00:17:57.768 You think to yourself, 601 00:17:57.888 --> 00:17:58.688 all that for this? 602 00:17:58.968 --> 00:18:02.389 So we don't have a comprehensive analysis of the patient. 603 00:18:03.170 --> 00:18:04.090 What is a liquid biopsy? 604 00:18:04.862 --> 00:18:06.783 You take a blood sample directly from a patient. 605 00:18:07.224 --> 00:18:11.026 You detect in the blood the DNA from tumors that are circulating in the bloodstream. 606 00:18:11.427 --> 00:18:14.269 So the DNA from all of the patient's metastases, 607 00:18:14.729 --> 00:18:15.490 and you collect it, 608 00:18:15.770 --> 00:18:17.591 and then you analyze the modifications. 609 00:18:18.052 --> 00:18:19.693 So you are much more holistic, 610 00:18:19.793 --> 00:18:20.393 much broader. 611 00:18:20.874 --> 00:18:21.034 Well, 612 00:18:21.035 --> 00:18:21.494 you might say, 613 00:18:21.934 --> 00:18:23.055 a CT scan is the same. 614 00:18:23.115 --> 00:18:25.297 I'm not going to measure all five of my metastases. 615 00:18:25.477 --> 00:18:25.757 Actually, 616 00:18:25.817 --> 00:18:26.718 I need to measure everything, 617 00:18:27.258 --> 00:18:28.279 what we call the tumor burden. 618 00:18:28.759 --> 00:18:29.160 Except here, 619 00:18:29.200 --> 00:18:29.780 there's a problem. 620 00:18:30.240 --> 00:18:32.482 You've heard about the demographics of radiologists. 621 00:18:32.874 --> 00:18:34.795 They say there are fewer and fewer radiologists. 622 00:18:35.175 --> 00:18:35.976 A CT scan, 623 00:18:36.016 --> 00:18:36.976 when you evaluate a scan, 624 00:18:37.016 --> 00:18:38.977 it's one every 10 or 15 minutes at most. 625 00:18:40.017 --> 00:18:41.498 Each time that's 2,000 images. 626 00:18:41.978 --> 00:18:45.860 That means every 10 or 15 minutes you have 2,000 images and a report to write. 627 00:18:46.140 --> 00:18:48.482 By the end of the day your eyes hurt and your head is spinning. 628 00:18:49.362 --> 00:18:52.924 And if on top of that you ask radiologists to measure every single lesion, 629 00:18:53.484 --> 00:18:54.044 for example, 630 00:18:54.104 --> 00:18:57.626 if there are 100 lesions it's impossible to do in 10 minutes. 631 00:18:58.134 --> 00:19:00.035 So at that point you realize, 632 00:19:00.075 --> 00:19:00.375 okay, 633 00:19:00.615 --> 00:19:02.436 if we want to assess this tumor burden, 634 00:19:02.556 --> 00:19:03.756 we need to develop AI, 635 00:19:03.976 --> 00:19:05.577 artificial intelligence tools. 636 00:19:05.977 --> 00:19:06.958 That's just mathematics, 637 00:19:07.098 --> 00:19:07.998 so it's being developed. 638 00:19:08.058 --> 00:19:11.780 It's a project we've just submitted with Gerbit and we'll get the answer at the end of the week. 639 00:19:12.240 --> 00:19:13.460 Probably BPI France, 640 00:19:13.500 --> 00:19:14.941 a major source of funding. 641 00:19:15.081 --> 00:19:16.102 So what's our vision? 642 00:19:16.402 --> 00:19:25.165 So within five years we should be able to produce a platform where you upload a patient scan and automatically an algorithm detects all the patients metastases, 643 00:19:25.586 --> 00:19:26.486 segments all of them. 644 00:19:26.962 --> 00:19:30.703 and says for example this patient has 200 millimeters of tumor, 645 00:19:31.243 --> 00:19:38.545 200 cubic millimeters or 300 cubic millimeters and we know that this tumor burden before starting treatment is highly prognostic. 646 00:19:38.765 --> 00:19:42.766 You can stratify and just now we published an article in the European Journal of Cancer. 647 00:19:43.206 --> 00:19:47.267 These are patients with metastatic colon cancer who respond very well to immunotherapy. 648 00:19:47.728 --> 00:19:48.708 It's a particular status. 649 00:19:48.948 --> 00:19:52.169 These are MSI positive patients so it's a molecular abnormality. 650 00:19:52.629 --> 00:19:56.290 Half of patients respond to immunotherapy so double immunotherapy is great. 651 00:19:56.810 --> 00:19:58.451 But there's a public health issue. 652 00:19:58.792 --> 00:20:00.393 Immunotherapy is expensive, 653 00:20:00.573 --> 00:20:02.495 costing 70,000 euros each, 654 00:20:02.915 --> 00:20:05.838 so double immunotherapy is 140,000 euros. 655 00:20:06.335 --> 00:20:08.176 unlike chemotherapy at 300 euros. 656 00:20:08.536 --> 00:20:08.676 Well, 657 00:20:08.716 --> 00:20:10.837 now by looking at this significant tumor burden, 658 00:20:11.538 --> 00:20:13.259 the number of metastases and everything, 659 00:20:13.419 --> 00:20:17.621 we've shown that we are able to stratify to know for whom we should immediately give double immunotherapy. 660 00:20:18.081 --> 00:20:23.444 Those with a very poor prognosis versus those for whom a single immunotherapy was enough for them to have a... 661 00:20:24.185 --> 00:20:25.365 So that's something concrete. 662 00:20:26.026 --> 00:20:26.946 It's for the oncologist. 663 00:20:27.086 --> 00:20:27.827 It's a scoring system. 664 00:20:28.107 --> 00:20:29.828 They count the number of cubic millimeters, 665 00:20:29.868 --> 00:20:31.008 the number of metastases. 666 00:20:31.148 --> 00:20:34.250 This small scoring system assesses four parameters for stratification. 667 00:20:34.590 --> 00:20:36.011 We did this with an oncologist. 668 00:20:36.295 --> 00:20:38.196 a world-leading expert in colon cancer, 669 00:20:38.356 --> 00:20:40.516 Professor Thierry André at Saint-Antoine. 670 00:20:41.176 --> 00:20:42.577 And we conducted this study together, 671 00:20:42.757 --> 00:20:43.677 so it's really concrete. 672 00:20:43.877 --> 00:20:44.597 It's tumor research, 673 00:20:44.877 --> 00:20:45.598 and I work on this. 674 00:20:45.778 --> 00:20:46.718 These are AI tools. 675 00:20:47.058 --> 00:20:47.378 Right now, 676 00:20:47.398 --> 00:20:49.459 my radiologists are segmenting all the tumors. 677 00:20:49.819 --> 00:20:51.019 It takes time for research, 678 00:20:51.079 --> 00:20:53.460 but the goal is to have a fully automated platform. 679 00:20:53.660 --> 00:20:54.340 Once that's done, 680 00:20:54.560 --> 00:20:56.241 and it will be ready before I retire, 681 00:20:56.461 --> 00:20:57.761 I am confident it'll work. 682 00:20:58.561 --> 00:20:58.741 Okay, 683 00:20:58.901 --> 00:20:59.182 good news. 684 00:21:00.162 --> 00:21:01.182 And if it happens before, 685 00:21:01.322 --> 00:21:01.842 I'll retire. 686 00:21:02.282 --> 00:21:02.903 It's exhausting, 687 00:21:02.923 --> 00:21:04.163 but truly great research. 688 00:21:04.519 --> 00:21:06.781 We are correlating liquid biopsy with imaging, 689 00:21:07.041 --> 00:21:09.783 so we're no longer working in silos with imaging alone. 690 00:21:10.403 --> 00:21:12.024 We do liquid biopsy and imaging. 691 00:21:12.885 --> 00:21:15.107 This will benefit all pharmaceutical companies. 692 00:21:15.447 --> 00:21:16.648 Now everyone is joining. 693 00:21:17.188 --> 00:21:20.270 So how do you actually bridge the gap between liquid biopsy and AI? 694 00:21:20.871 --> 00:21:22.392 How do you integrate these fields together? 695 00:21:22.792 --> 00:21:23.713 How do you mix everything? 696 00:21:23.853 --> 00:21:24.433 At what point? 697 00:21:24.593 --> 00:21:25.394 How do you make it happen? 698 00:21:25.794 --> 00:21:29.097 It's true that this year when we presented it at the restaurant in Chicago, 699 00:21:29.297 --> 00:21:31.118 actually we are the first in the world to do this. 700 00:21:31.158 --> 00:21:32.219 We are real pioneers. 701 00:21:32.603 --> 00:21:34.725 Because we presented a study of 1,000 patients, 702 00:21:34.905 --> 00:21:37.027 we tagged 55,000 metastases. 703 00:21:37.447 --> 00:21:38.708 There was a Chinese man in the room. 704 00:21:38.909 --> 00:21:39.709 He stood up and said, 705 00:21:40.710 --> 00:21:41.431 that's impossible. 706 00:21:41.451 --> 00:21:42.151 You didn't do that. 707 00:21:42.211 --> 00:21:43.352 It's impossible to do that. 708 00:21:43.713 --> 00:21:47.196 How did you manage to get French people to tag 55,000 metastases? 709 00:21:48.117 --> 00:21:52.480 I told them it was extremely important and that I had taken young people for a year of research to do it. 710 00:21:52.640 --> 00:21:53.581 At noon after that, 711 00:21:53.641 --> 00:21:55.603 it's because for the past 25 to 30 years, 712 00:21:56.023 --> 00:21:59.867 I've always had a very close relationship with the oncologists here who are really at the forefront. 713 00:22:00.635 --> 00:22:02.596 and they educate me about what's new and everything. 714 00:22:02.996 --> 00:22:04.817 They'd alerted me about liquid biopsy. 715 00:22:05.057 --> 00:22:05.998 Benjamin Besser said, 716 00:22:06.318 --> 00:22:08.439 you'll see it's going to replace the CT scan. 717 00:22:08.639 --> 00:22:08.879 I said, 718 00:22:08.999 --> 00:22:09.139 what? 719 00:22:09.199 --> 00:22:09.919 That's not possible. 720 00:22:10.380 --> 00:22:11.360 It will be complimentary, 721 00:22:11.400 --> 00:22:12.761 but you're not going to replace the scan. 722 00:22:13.321 --> 00:22:14.201 And that's how it started, 723 00:22:14.321 --> 00:22:14.962 just by talking. 724 00:22:15.722 --> 00:22:17.883 And it's always about having those kinds of exchanges. 725 00:22:18.303 --> 00:22:18.563 Actually, 726 00:22:18.644 --> 00:22:19.444 that's what's needed, 727 00:22:19.544 --> 00:22:20.284 not staying stuck. 728 00:22:21.065 --> 00:22:22.325 Maybe it's because very early on, 729 00:22:22.345 --> 00:22:23.466 I was involved in research. 730 00:22:23.846 --> 00:22:25.107 So as a result from early on, 731 00:22:25.147 --> 00:22:25.867 I didn't stay put. 732 00:22:26.319 --> 00:22:27.640 I was a radiology intern, 733 00:22:27.720 --> 00:22:28.720 so that was my whole thing. 734 00:22:28.760 --> 00:22:30.141 And so I had to open up. 735 00:22:30.161 --> 00:22:31.941 I think it's because of that research training. 736 00:22:32.622 --> 00:22:33.062 So for me, 737 00:22:33.122 --> 00:22:34.262 whatever topic I'm given, 738 00:22:34.382 --> 00:22:34.542 well, 739 00:22:34.622 --> 00:22:34.782 yes, 740 00:22:34.822 --> 00:22:35.363 I go for it. 741 00:22:35.364 --> 00:22:35.863 I dive in. 742 00:22:36.283 --> 00:22:36.463 Okay, 743 00:22:36.543 --> 00:22:37.123 so what is it? 744 00:22:37.203 --> 00:22:37.604 The topic? 745 00:22:37.724 --> 00:22:38.264 What's the problem? 746 00:22:38.664 --> 00:22:39.985 Should we involve the stakeholders? 747 00:22:40.045 --> 00:22:40.925 Who should we include? 748 00:22:41.085 --> 00:22:45.807 It takes mental flexibility and energy to get people working together who have different professions, 749 00:22:46.127 --> 00:22:46.487 skills, 750 00:22:46.607 --> 00:22:47.307 and objectives. 751 00:22:47.508 --> 00:22:48.668 We don't have the same goals, 752 00:22:48.928 --> 00:22:54.530 so it requires a certain level of positive energy by saying you have to get people on board, 753 00:22:54.630 --> 00:22:55.811 get them on the fast train. 754 00:22:56.211 --> 00:22:58.472 But the most beautiful projects right now are those, 755 00:22:58.912 --> 00:23:02.954 the ones that break down silos with people who really have different visions, 756 00:23:03.014 --> 00:23:03.674 different minds. 757 00:23:04.074 --> 00:23:05.615 And it's always people from the hospital, 758 00:23:05.855 --> 00:23:08.356 people from research and people from industry. 759 00:23:08.876 --> 00:23:11.717 All my projects now always mix these three worlds together. 760 00:23:12.078 --> 00:23:13.818 So there's something you truly like, 761 00:23:14.299 --> 00:23:16.740 which is bringing together an industry professional, 762 00:23:16.840 --> 00:23:18.960 the hospital and research at the same time. 763 00:23:20.181 --> 00:23:24.943 Is there a specific project that you are especially proud to have successfully carried out using this exact methodology? 764 00:23:25.959 --> 00:23:26.079 Well, 765 00:23:26.119 --> 00:23:31.924 I think it will be my best publication and my greatest experience of collective intelligence, 766 00:23:31.984 --> 00:23:34.206 even though it was an artificial intelligence project. 767 00:23:34.546 --> 00:23:35.547 It was during COVID. 768 00:23:35.907 --> 00:23:40.291 You know that at that time all research labs were asked to close except those that were going to work on COVID. 769 00:23:40.691 --> 00:23:41.992 I was still a researcher at heart, 770 00:23:42.452 --> 00:23:43.353 along with another colleague. 771 00:23:44.234 --> 00:23:48.317 Here we were also busy with Gustave because we were receiving all the patients who had COVID. 772 00:23:48.477 --> 00:23:49.278 And even afterwards, 773 00:23:49.538 --> 00:23:52.521 we were asked to take in patients since the APHP was completely... 774 00:23:52.981 --> 00:23:53.141 well, 775 00:23:53.161 --> 00:23:53.802 there was a wave, 776 00:23:53.822 --> 00:23:54.062 you see, 777 00:23:54.082 --> 00:23:55.383 they no longer had enough bids 778 00:23:55.975 --> 00:23:58.356 So we were asked to open Gustav as well, 779 00:23:58.436 --> 00:24:00.297 to take in non-cancer patients who had COVID. 780 00:24:00.677 --> 00:24:01.677 We were overwhelmed, 781 00:24:01.778 --> 00:24:03.218 but I still wanted to do research. 782 00:24:03.718 --> 00:24:05.999 So a colleague and I decided to study COVID. 783 00:24:07.020 --> 00:24:09.741 This allowed us to keep working since it was essential research. 784 00:24:10.761 --> 00:24:11.822 Outside regular hours, 785 00:24:11.882 --> 00:24:14.863 since we spent our days on Doppler ultrasounds and working in the ICU, 786 00:24:15.443 --> 00:24:16.484 then we said to ourselves, 787 00:24:16.524 --> 00:24:16.644 OK, 788 00:24:16.744 --> 00:24:17.684 what do our patients need? 789 00:24:17.984 --> 00:24:18.725 Always the same thing. 790 00:24:18.805 --> 00:24:23.807 So we talked with the intensivists and the issue is a patient arrives in the emergency room. 791 00:24:24.247 --> 00:24:24.567 In fact, 792 00:24:24.587 --> 00:24:30.769 we don't know whether to let them go home or keep them if in 12 hours they'll be in the ICU dead or back at home. 793 00:24:31.710 --> 00:24:33.010 So he said, 794 00:24:33.170 --> 00:24:34.851 if you can find us a scoring system, 795 00:24:35.211 --> 00:24:39.092 something that can tell us whether we should at least keep the patient or send them home. 796 00:24:39.272 --> 00:24:39.392 OK, 797 00:24:40.512 --> 00:24:41.213 we said to ourselves, 798 00:24:41.253 --> 00:24:41.373 OK, 799 00:24:41.593 --> 00:24:43.633 since we've started working with artificial intelligence, 800 00:24:44.334 --> 00:24:45.054 we said to ourselves, 801 00:24:45.094 --> 00:24:48.095 let's use imaging because there was a lot of talk about CT scans. 802 00:24:48.506 --> 00:24:52.508 We could see the shading on the scan with the ground glass opacity and all that. 803 00:24:52.688 --> 00:24:54.810 We decided to work on AI using the CT scans. 804 00:24:55.330 --> 00:24:57.151 We decided not to stay isolated. 805 00:24:57.251 --> 00:25:02.254 We reviewed early reports from the Chinese because they published one or two months before us. 806 00:25:02.514 --> 00:25:04.475 We decided to collect all clinical, 807 00:25:04.575 --> 00:25:06.356 biological and medical history data. 808 00:25:06.536 --> 00:25:08.517 We'll collect everything to build a scoring system. 809 00:25:08.797 --> 00:25:09.298 At the time, 810 00:25:09.318 --> 00:25:12.199 we didn't have a CT scanner running constantly for COVID. 811 00:25:12.940 --> 00:25:14.801 But at the APHP Kremlin Bicêtre, 812 00:25:15.301 --> 00:25:17.022 my colleagues in the research lab you 813 00:25:17.502 --> 00:25:23.667 Franz Belen and the others actually had an entire dedicated CT scanner that was only doing COVID cases. 814 00:25:23.908 --> 00:25:24.468 So I told them, 815 00:25:24.848 --> 00:25:25.008 well, 816 00:25:25.189 --> 00:25:27.771 I would like to do a study with our patients and your patients. 817 00:25:28.231 --> 00:25:34.856 That will provide a validation cohort to try to see how we can predict when the patient arrives at the emergency room based on all these parameters, 818 00:25:35.197 --> 00:25:36.658 whether they need to be admitted or not. 819 00:25:37.038 --> 00:25:37.579 They said to me, 820 00:25:37.839 --> 00:25:37.959 OK. 821 00:25:38.800 --> 00:25:39.140 Then I said, 822 00:25:39.160 --> 00:25:39.340 well, 823 00:25:39.380 --> 00:25:40.721 I need to find the best people in AI. 824 00:25:41.722 --> 00:25:46.466 So then I went looking of the two teams I worked with. 825 00:25:47.010 --> 00:25:47.971 One was from INRIA, 826 00:25:48.411 --> 00:25:50.073 an AI lab at Centralis Superlec, 827 00:25:50.113 --> 00:25:50.733 the CVN, 828 00:25:51.133 --> 00:25:51.774 and then Kin, 829 00:25:51.994 --> 00:25:53.035 who I also worked with. 830 00:25:53.515 --> 00:25:54.256 And I told them, 831 00:25:54.736 --> 00:25:55.177 basically, 832 00:25:55.237 --> 00:25:55.737 I told them, 833 00:25:55.817 --> 00:25:55.977 well, 834 00:25:56.037 --> 00:25:56.618 here's the plan. 835 00:25:57.078 --> 00:25:59.720 We'll do a study and I'd like to use both your technologies, 836 00:25:59.901 --> 00:26:00.681 which were different. 837 00:26:01.522 --> 00:26:03.323 One was in machine learning, 838 00:26:03.523 --> 00:26:04.264 so in deep learning. 839 00:26:04.384 --> 00:26:05.205 I told them after that, 840 00:26:05.305 --> 00:26:05.705 it's a bet. 841 00:26:05.865 --> 00:26:06.586 May the best one win. 842 00:26:06.706 --> 00:26:08.367 We'll take the best technology and use it. 843 00:26:08.948 --> 00:26:09.068 OK, 844 00:26:09.668 --> 00:26:12.931 that's a challenge between getting the contract signed with APHP, 845 00:26:13.351 --> 00:26:14.112 retrieving the data, 846 00:26:14.192 --> 00:26:15.173 which was no small matter, 847 00:26:15.533 --> 00:26:16.274 and everything else. 848 00:26:16.966 --> 00:26:17.106 Well, 849 00:26:17.146 --> 00:26:17.646 let me tell you, 850 00:26:17.647 --> 00:26:18.367 in just 10 days, 851 00:26:18.427 --> 00:26:19.247 which is unimaginable, 852 00:26:19.307 --> 00:26:19.708 we did it. 853 00:26:20.028 --> 00:26:21.309 We signed the contracts with APHP, 854 00:26:21.569 --> 00:26:22.129 with Aukin, 855 00:26:22.209 --> 00:26:22.649 with INRIA, 856 00:26:22.750 --> 00:26:23.190 and so on. 857 00:26:23.690 --> 00:26:24.571 And I got everyone working. 858 00:26:24.591 --> 00:26:25.451 We were 45 people. 859 00:26:25.871 --> 00:26:27.252 They put half their lab on it for me. 860 00:26:27.592 --> 00:26:28.733 You can check in the publication, 861 00:26:28.793 --> 00:26:29.914 which was published in Nature. 862 00:26:30.354 --> 00:26:30.934 It's my longest, 863 00:26:30.974 --> 00:26:31.595 most major work. 864 00:26:32.115 --> 00:26:32.996 Half Aukin on it, 865 00:26:33.236 --> 00:26:34.036 45 total. 866 00:26:34.637 --> 00:26:36.278 There are 20 co-authors on my paper. 867 00:26:36.458 --> 00:26:37.618 And then everyone got involved. 868 00:26:37.718 --> 00:26:38.199 In the end, 869 00:26:38.219 --> 00:26:40.680 I told them I need a school for intensive care. 870 00:26:41.200 --> 00:26:43.662 So we included 1,000 patients in one month. 871 00:26:44.306 --> 00:26:45.727 Every evening at 9 p.m. 872 00:26:45.847 --> 00:26:49.348 we went to the Ambicet ward to record all patient data by hand. 873 00:26:49.788 --> 00:26:50.229 Honestly, 874 00:26:50.409 --> 00:26:51.049 it was true, 875 00:26:51.129 --> 00:26:52.450 we did everything ourselves. 876 00:26:53.030 --> 00:26:56.091 People were stressed by what was happening in the hospital and the study, 877 00:26:56.471 --> 00:26:57.852 but we finished the publication. 878 00:26:58.272 --> 00:26:59.293 We sent it to the study, 879 00:26:59.353 --> 00:27:02.614 which challenged us to compare our results to 15 others. 880 00:27:02.974 --> 00:27:05.716 And there was an amazing methodologist who is the last author. 881 00:27:06.636 --> 00:27:07.156 Michael Bloom, 882 00:27:07.157 --> 00:27:07.837 you should look him up, 883 00:27:07.877 --> 00:27:08.997 he was an incredible guy. 884 00:27:09.657 --> 00:27:10.818 And we published a great paper. 885 00:27:11.262 --> 00:27:11.823 And in the end, 886 00:27:11.824 --> 00:27:14.044 you have a kind of scoring system with a color code. 887 00:27:14.465 --> 00:27:15.566 When the patient arrives, 888 00:27:15.746 --> 00:27:16.006 you know, 889 00:27:16.046 --> 00:27:16.547 with AI, 890 00:27:16.587 --> 00:27:17.467 the scan results. 891 00:27:18.328 --> 00:27:20.710 And then it was the oxygen saturation level, 892 00:27:20.750 --> 00:27:21.771 the platelet count, 893 00:27:21.851 --> 00:27:23.933 the duration rate that no one expected, 894 00:27:24.373 --> 00:27:25.454 and then age and sex. 895 00:27:26.115 --> 00:27:26.635 And with that, 896 00:27:26.675 --> 00:27:27.035 you knew, 897 00:27:27.175 --> 00:27:29.077 depending on the color and the involvement. 898 00:27:29.738 --> 00:27:32.420 So it was a paper with explainability in AI, 899 00:27:32.520 --> 00:27:33.621 which is very important. 900 00:27:34.422 --> 00:27:35.542 This allowed a doctor to say, 901 00:27:35.683 --> 00:27:35.803 OK, 902 00:27:35.943 --> 00:27:37.024 this patient is orange-red. 903 00:27:37.824 --> 00:27:38.545 I need to keep them, 904 00:27:38.565 --> 00:27:39.586 but this one is blue-green. 905 00:27:40.394 --> 00:27:41.415 so I don't need to keep them. 906 00:27:41.895 --> 00:27:42.775 And that was incredible. 907 00:27:43.336 --> 00:27:44.616 And we did that in three months. 908 00:27:45.057 --> 00:27:46.497 But it's surreal when you say that. 909 00:27:47.038 --> 00:27:48.859 Any industry professional or any laboratory, 910 00:27:48.919 --> 00:27:49.859 like if you explain this, 911 00:27:49.899 --> 00:27:50.239 they'll say, 912 00:27:50.620 --> 00:27:51.340 how did she do that? 913 00:27:52.261 --> 00:27:52.841 I told myself, 914 00:27:52.901 --> 00:27:53.061 yeah, 915 00:27:53.481 --> 00:27:59.705 never again will I do a study with 100 meters between the hospital industry and academia each time. 916 00:27:59.885 --> 00:28:00.545 It was possible. 917 00:28:00.765 --> 00:28:01.966 It was a bit tough at the beginning, 918 00:28:02.026 --> 00:28:02.446 but well, 919 00:28:02.626 --> 00:28:03.047 in the end, 920 00:28:03.067 --> 00:28:05.548 it produces something magnificent. 921 00:28:06.468 --> 00:28:06.989 Magnificent, 922 00:28:07.049 --> 00:28:07.269 really. 923 00:28:07.689 --> 00:28:09.310 It's the best paper of my entire career. 924 00:28:09.470 --> 00:28:12.032 And it was truly an adventure in collective intelligence, 925 00:28:12.092 --> 00:28:13.513 more than artificial intelligence. 926 00:28:13.873 --> 00:28:14.974 Even though we used AI, 927 00:28:14.994 --> 00:28:16.755 it was really collective intelligence. 928 00:28:17.295 --> 00:28:18.936 Every time we made a video with everyone, 929 00:28:19.017 --> 00:28:20.037 with all the stakeholders, 930 00:28:20.097 --> 00:28:20.598 to be sure. 931 00:28:20.938 --> 00:28:21.238 All right, 932 00:28:21.278 --> 00:28:22.399 so what do you have the data, 933 00:28:22.739 --> 00:28:23.159 the stuff? 934 00:28:23.459 --> 00:28:24.040 Every evening, 935 00:28:24.041 --> 00:28:25.521 we would have a video call with everyone, 936 00:28:25.801 --> 00:28:27.702 because I had to keep motivating everyone, 937 00:28:27.703 --> 00:28:28.363 the whole team. 938 00:28:29.323 --> 00:28:30.604 Those who were locked up at home, 939 00:28:30.624 --> 00:28:31.585 but working like crazy, 940 00:28:31.765 --> 00:28:32.986 analyzing the scans as well. 941 00:28:33.806 --> 00:28:35.007 Those who were collecting the data, 942 00:28:35.107 --> 00:28:36.048 those who were on site. 943 00:28:36.428 --> 00:28:36.909 It was funny. 944 00:28:37.329 --> 00:28:37.929 It was amazing. 945 00:28:38.189 --> 00:28:38.830 Very rewarding. 946 00:28:39.202 --> 00:28:40.183 a very tough period. 947 00:28:40.523 --> 00:28:40.783 For me, 948 00:28:40.983 --> 00:28:45.767 I think it was the hardest period of my career as a doctor to lose our patients like that, 949 00:28:46.467 --> 00:28:47.948 but at the same time very rewarding. 950 00:28:48.709 --> 00:28:54.913 I think it was also a way for us to keep our heads above water and not let ourselves be overwhelmed by what was happening. 951 00:28:55.454 --> 00:28:57.035 It was really hard. 952 00:28:57.315 --> 00:28:58.556 I was not really afraid to read. 953 00:28:59.196 --> 00:28:59.316 Me, 954 00:28:59.356 --> 00:28:59.937 I was afraid. 955 00:29:00.317 --> 00:29:01.378 I was 55 years old. 956 00:29:01.498 --> 00:29:03.680 I was in the age group of people who could die quickly. 957 00:29:04.500 --> 00:29:05.501 I could catch it and die. 958 00:29:05.841 --> 00:29:06.902 In a week I could be dead. 959 00:29:07.342 --> 00:29:09.023 Those are still things that cross your mind. 960 00:29:09.783 --> 00:29:10.903 Even if we had the equipment, 961 00:29:11.083 --> 00:29:11.803 we were protected, 962 00:29:11.903 --> 00:29:13.304 dressed like little astronauts all day. 963 00:29:13.924 --> 00:29:15.985 But those are still things that cross your mind. 964 00:29:16.105 --> 00:29:16.225 Me, 965 00:29:16.505 --> 00:29:17.105 I said it. 966 00:29:17.265 --> 00:29:18.005 I was scared. 967 00:29:18.205 --> 00:29:18.665 It's true. 968 00:29:19.466 --> 00:29:19.826 Thank you. 969 00:29:20.106 --> 00:29:23.387 It was very interesting to see the innovations coming out of the research centers. 970 00:29:23.507 --> 00:29:24.627 Thank you for this exchange. 971 00:29:24.907 --> 00:29:26.147 It was a pleasure meeting you. 972 00:29:26.508 --> 00:29:26.848 Goodbye. 973 00:29:27.068 --> 00:29:28.608 If Europe excels in AI research, 974 00:29:28.708 --> 00:29:30.149 why does access remain unequal? 975 00:29:30.629 --> 00:29:31.929 Is it because of regulation, 976 00:29:32.189 --> 00:29:33.109 industrial caution, 977 00:29:33.870 --> 00:29:34.970 fragmentation of systems? 978 00:29:36.834 --> 00:29:37.716 In the next episode, 979 00:29:37.736 --> 00:29:39.380 we will address another structural gap. 980 00:29:40.142 --> 00:29:42.909 Why are there 100 new drugs already available? 981 00:29:43.370 --> 00:29:44.212 In the United States, 982 00:29:44.252 --> 00:29:45.034 but not in Europe? 983 00:29:47.581 --> 00:29:48.762 And to follow up on this video, 984 00:29:48.862 --> 00:29:50.044 I'll be waiting for your comments. 985 00:29:50.604 --> 00:29:51.205 In your country, 986 00:29:51.285 --> 00:29:53.407 what is the biggest barrier to accessing AI? 987 00:29:53.627 --> 00:29:54.769 Is it funding, 988 00:29:55.229 --> 00:29:55.790 regulation, 989 00:29:56.431 --> 00:29:58.513 or collaboration between industry and academia?