Dr. Faisal Mahmood is an Assistant Professor of Pathology at Harvard Medical School and Computational Pathology at Brigham and Women’s Hospital. Dr. Mahmood recently published an exciting new paper this year where he and his team built a deep learning model to accurately identify tumors of unknown origin on pathological slides (Lu et al., Nature 2021)
Pathology is one of the central pillars of medicine and here we really dive deep into how machine learning is pushing the boundaries of the field and our abilities to diagnose and recognize tumors. Enjoy!
Twitter: @TheMaMLPodcast
Interviewer: David JH Wu (@davidjhwu)
Producer: Aaron Schumacher (@a_schu95)
Cover Art: Saurin Kantesaria
1:20 Background in computational pathology
5:30 Interest in Pathology
10:20 Modern algorithms detecting biomarkers to better educate physicians
12:05 Using AI to identify tumors of unknown origin
17:10 Building the TOAD AI Model
21:50 Assessing the validity of the Toad Model
23:30 Determining inputs for the TOAD Model
26:30 Diversity with the TOAD Algorithm
27:40 Next steps for the project
33:15 Using AI to augment physicians' abilities
35:06 Advice for physicians interest in AI
37:00 Dream Research Project
38:40 Will AI make medical discoveries in the future?
40:38 Advice you would give to yourself in your 20's
41:55 Obtaining a Ph.D. in Japan
44:00 Closing thoughts
Paper: Lu et. al, “AI-based pathology predicts origins for cancers of unknown primary.” Nature, 2021
---
Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support
