Season 2, Episode 15

The Digital Fellow

This episode's guest:

Michael Feldman, MD, PhD

This episode of Digital Pathology Today™ our guest is Michael Feldman; Professor , Pathology & Laboratory Medicine; Vice Chair of Clinical Services; Medical Director Pathology Informatics; Director Tumor Tissue Bank

What is The State of the Practice of digital pathology as we enter 2022? Where are we in terms of integrated workflows, predictive and prognostic algorithms based on H&E morphology, and incorporating AI into workflows or diagnostic assistance?

Perhaps most pressingly, have we finally made the business case for wide scale adoption of digital pathology?

Our guest is Michael Feldman, MD, PhD, Professor of Pathology and Laboratory Medicine at the University of Pennsylvania as well as Vice Chair for Clinical Services and Director of the office of Pathology Informatics. We're going to be talking about all these things and in addition perhaps a perspective on digital pathology in an academic setting. Will digital fellows become a reality - artificial intelligence enabled machines that will organize, prepare, preview, maybe even diagnose cases and prepare the report for sign out? We’ll discuss what image analysis and computational algorithms mean for the diagnostic acumen of trainees in the next generation of pathologists.

More About Michael Feldman, MD, PhD

Michael Feldman, MD, PhD

Professor, Pathology & Laboratory Medicine

Vice Chair of Clinical Services

Medical Director Pathology Informatics

Director Tumor Tissue Bank

Michael Feldman's professional interests revolve around the development, integration and adoption of information technologies in the discipline of Pathology. One of his main areas of interest within this broad discipline has been in the field of digital imaging. He has been studying pathology imaging on several fronts including interactions between pathology/radiology (Radiopathogenomics of prostate cancer and breast carcinoma), development and utilization of computer assisted diagnostic algorithms for machine vision in prostate and breast cancer. More recently he has been developing deep learning methods for complex interrogation of pathology slides both within the cancer domain as well as in cardiovascular and renal pathology. He has also been developing methods to apply multispectral imaging for the analysis of multiplexed immunohistochemistry and immunoflourescence to tissues along with the development of a quantitative system for scoring and analyzing these studies at a cytometric level on surgical pathology slides. The efforts have been recognized by the national funding agencies of the NIH and DOD as well as industry sponsored projects.