Discover how Artificial Intelligence (AI) is poised to broadly reshape medicine, improving the experiences of both clinicians and patients. Dr. Topol will discuss the key findings from a 2-year weekly effort to track and share key developments in medical AI, and cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. Several promising avenues for novel medical AI research including non-image data sources, unconventional problem formulations and human–AI collaboration will be presented. Lastly, he will discuss the consideration of serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI’s potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.

Speakers:
Eric Topol, Professor, Molecular Medicine, and Executive Vice-President of Scripps Research, and Founder and Director of its Scripps Research Translational Institute


Shownotes:
00:00 Panelist intro
1:31 Zak Kohane
5:58 Eric Topol, Scripps Research Translational Institute
7:20 ai and medicine
8:26 retina
8:55 what the machine sees in the retina
9:45 myocardial infarction from scans and personal information
10:29 seen in the ECG
10:55 ECG-based Atrial Fibrillation
11:22 elevated pulmonary capillary from ECG
12:00 Ejection fraction detection
12:46 Arrhythmic sudden death survival prediction
13:50 beta-blocker response - cluster analysis
14:42 pathology
15:45 EGFR genotype and targeted therapy
16:34 mammography and breast cancer risk
17:51 data being missed
18:28 delayed and missed care
19:21 prospective trials in clinical environments
19:56 randomized trials
21:15 speech recognition for the hearing impaired
21:29 opportunities for augmentation
22:20 new guidelines for AI clinical research
23:28 disappointments ==
23:56 COVID via Chest X-rays
25:26 Breast screening programmes
26:45 colonoscopy
27:48 AI in the ER
28:14 subtle shift in patient data
28:48 sepsis
29:12 clinical implementation
30:13 Ziad Obermeyer
30:33 Trust in AI
30:49 doctorless diagnosis
30:51 opportunities
33:55 the virtual medical assistant
33:34 ethical challenge
35:21 explainability and trust
35:53 principled AI
36:11 federated learning
36:59 swarm learning
37:51 inequities
38:24 smartphone ultrasound
38:58 AI wihh smartphone ultrasound
40:02 Topol medical selfie
40:27 telephone connections - target COVID aid
41:34 the robot will see you now
42:00 Topol Review of the NHS
43:34 natural language processing
43:51 thinking, fast and slow
44:27 the gift of time
44:52 there is no algorithm for empathy
44:56 Francis Peabody
45:53 == Q&A ==
45:55 use of stethoscope in medicine schools
46:59 entrenchment in medicine
47:37 smartphone ultrasound
48:00 inertia
48:30 Topol clinic
49:22 the need for change
50:10 disbelief among medical students
51:12 combining western and traditional medicine
51:53 the book deep medicine – progress since
52:18 going beyond accuracy
53:18 interpretation of genomes – human limitations
53:43 data donations
54:08 “All of us”
55:43 should medical practices create their own AI?
56:13 context dependent – don’t want to reinvent the wheel
57:26 is the FDA well equipped for AI?
57:30 retrospective, proprietary algorithms, frozen algorithms
58:22 thanks from the moderator
58:37 thanks from ITU

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