Chair
Stanley Cohen, MD
Rutgers-New Jersey Medical School
Co-Chair
John Tomaszewski, MD
University of Buffalo
Session Description: The use of artificial intelligence has become ubiquitous in many aspects of human endeavor including medicine. In particular, Pathology is well-positioned to take advantage of this methodology both because of our heavy reliance on large amounts of laboratory data and the need for high level image interpretation. This session will begin with an overview of the underlying mechanisms of artificial intelligence and deep learning, followed by examples of the implementation of AI in solving problems such as tumor grading, interactions between tumors and the host’s immune response, with the ultimate aim of prognostic prediction. The session will conclude with progress in the fusion of multiple pathology data streams via AI-based strategies.
| Chair - Welcome and Introductions | ||
| 2:00 PM - 2:35 PM | Data Analytics Comes of Age John Tomaszewski, MD • University of Buffalo School of Medicine |
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| 2:35 PM - 3:10 PM | AI 101: What is AI and How are It’s Algorithmic Strategies Implemented? Stanley Cohen, MD • Rutgers-New Jersey Medical School |
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| 3:10 PM - 3:45 PM | Deep Learning based Pathomic Biomarkers Joel Saltz, MD • Stony Brook Medicine |
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| 3:45 PM - 4:20 PM | Reducing Intra- and Inter-Observer Variability via AI Assistance Faisal Mahmood, MD • Brigham and Women's Hospital and Harvard Medical School |
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| 4:20 PM - 4:55 PM | Deep Leaning Driven Data Fusion as Pathology’s New Frontier John Tomaszewski, MD • University of Buffalo School of Medicine |
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| Summary Stanley Cohen, MD • Rutgers-New Jersey Medical School |
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