A gentle introduction to AI for medicine

1. Maarten van Smeden, PhD PhD Defence Henrik Olsson Feb 3, 2023 A gentle introduction into AI for medicine

2. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Terminology In medical research, “artificial intelligence” usually just means “machine learning” or “algorithm”

3. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden https://bit.ly/2CwW43A

4. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Proportion of studies indexed in Medline with the Medical Subject Heading (MeSH) term “Artificial Intelligence” divided by the total number of publications per year. Faes et al. doi: 10.3389/fdgth.2022.833912

5. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden https://bit.ly/2TOdd0F

6. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden https://bit.ly/2v2aokk

7. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Forsting, J Nuc Med, 2017, DOI: 10.2967/jnumed.117.190397

8. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Tech company business model

9. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Tech company business model https://bit.ly/2HSp8X5; https://bit.ly/2Z0Pfop; https://bit.ly/2KIcpHG; https://bit.ly/33IJhr9

10. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Other success stories https://go.nature.com/2VG2hS7; https://bbc.in/2Z1drXQ; https://bit.ly/2TAfRIP

11. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden IBM Watson winning Jeopardy! (2011) https://bbc.in/2TMvV8I

12. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden IBM Watson for oncology https://bit.ly/2LxiWGj

13. https://twitter.com/AndrewLBeam/status/1620855064033382401?s=20&t=VO9_LdFFCj_wcwIQLvKcIQ

14. Source: Ilse Kant (UMC Utrecht)

15. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden https://bit.ly/38A1ng0

16. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden “ML methods come from computer science” https://bit.ly/2zhbwPv; https://stanford.io/2TVp1xK; https://stanford.io/2ZfED0k Leo Breiman Jerome H Friedman Trevor Hastie CART, random forest Gradient boosting Elements of statistical learning Education Physics/Math Physics Statistics Job title Professor of Statistics Professor of Statistics Professor of Statistics

17. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden “ML methods for prediction, statistics for explaining” 1See further: Kreiff and Diaz Ordaz; https://bit.ly/2m1eYdK ML and causal inference, small selection1 • Superlearner (e.g. van der Laan) • High dimensional propensity scores (e.g. Schneeweiss) • The book of why (Pearl)

18. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Two cultures Breiman, Stat Sci, 2001, DOI: 10.1214/ss/1009213726

19. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Faes et al. doi: 10.3389/fdgth.2022.833912 Language

20. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Robert Tibshirani: https://stanford.io/2zqEGfr Machine learning: large grant = $1,000,000 Statistics: large grant = $50,000

21. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden ML refers to a culture, not to methods Distinguishing between statistics and machine learning • Substantial overlap methods used by both cultures • Substantial overlap analysis goals • Attempts to separate the two frequently result in disagreement Pragmatic approach: I’ll use “ML” to refer to models roughly outside of the traditional regression types of analysis: decision trees (and descendants), SVMs, neural networks (including Deep learning), boosting etc.

22. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Examples where “ML” has done well

23. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden

24. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Example: retinal disease Gulshan et al, JAMA, 2016, 10.1001/jama.2016.17216; Picture retinopathy: https://bit.ly/2kB3X2w Diabetic retinopathy Deep learning (= Neural network) • 128,000 images • Transfer learning (preinitialization) • Sensitivity and specificity > .90 • Estimated from training data

26. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Primary outcome: time to TB treatment. Time to TB treatment lowered from a median of 11 days in standard of care to 1 day with computer aided X-ray screening

28. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Examples where “ML” has done poorly

29. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Adversarial examples https://bit.ly/2N4mQFo; https://bit.ly/2W7X9rF

30. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Recidivism Algorithm Pro-publica (2016) https://bit.ly/1XMKh5R

31. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Skin cancer and rulers Esteva et al., Nature, 2016, DOI: 10.1038/nature21056; https://bit.ly/2lE0vV0

33. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Systematic review clinical prediction models Christodoulou et al. Journal of Clinical Epidemiology, 2019, doi: 10.1016/j.jclinepi.2019.02.004

34. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden But…

35. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Flexible algorithms are data hungry From slide deck Ben van Calster: https://bit.ly/38Aqmjs

36. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Flexible algorithms are energy hungry The costs of running (cloud computing) the Transformer algorithm are estimated at 1 to 3 million Dollars https://bit.ly/33Dj38X

37. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden AI assistance leads to more accurate diagnosis of liver cancer!

38. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden AI assistance leads to more accurate diagnosis of liver cancer! If AI is correct AI assistance leads to less accurate diagnosis of liver cancer! If AI is incorrect

39. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Algorithm based medicine • Algorithms are high maintenance • Developed models need repeated testing and updating to remain useful over time and place • Many new barriers: black box proprietary algorithms, computing costs • Regulation and quality control of algorithms • Algorithms need testing, preferably in experimental fashion

40. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Source: Ilse Kant (UMC Utrecht), adapted from doi: 10.1080/08956308.1997.11671126 3000 100 10 2 1 Ideas Explorations Launches well defined projects Succes From research AI model to implemented AI application, innovation is ….

41. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Pipeline of algorithmic medicine failure Van Royen et al, ERJ, doi: 10.1183/13993003.00250-2022, also credits to Laure Wynants

42. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Doi: 10.1093/eurheartj/ehac238

43. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Old statistics wine in new machine learning bottles? Lots of… • Hype • Rebranding traditional analysis as ML and AI • Methodological reinventions • Traditional issues such as low sample size, lack of adequate validation, poor reporting Also, real developments in… • Methods and architectures, allowing for modeling (unstructured) data that could previously not easily be used • Software • Computing power • Clinical trials showing benefit of AI assistance

44. Stockholm, 3 Feb 2023 Twitter: @MaartenvSmeden Email: M.vanSmeden@umcutrecht.nl Twitter: @MaartenvSmeden Slides available at: https://www.slideshare.net/MaartenvanSmeden

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