Validating AI in Digital Pathology for Clinical Use Beyond the Algorithm
Data Science Seminar Series
April 15, 2026 | 11:00 AM – 12:00 PM
Online
Attend this data science seminar to discover how researchers are attempting to build more robust artificial intelligence (AI) models to improve digital pathology—the end-to-end process of digitizing and analyzing tissue samples—for cancer diagnostics.
Dr. Pratik Shah from the University of California, Irvine (UC Irvine), will:
- address how robust AI models in digital pathology are limited not only due to the scarcity of large-scale, annotated data sets but also due to the lack of clear framework for clinical translation.
- exemplify a deep learning model that can generate synthetic, but plausible, histological stains from unstained tissue.
- highlight how NIH exploratory grants (e.g., NIH R21) have made it possible for a feasible technology to become a clinically validated, risk-probability tool (i.e., prognostic model).
- give perspective on both emerging challenges and standards for validating and approving these complex, AI-enabled medical devices intended for clinical use.
About the Speaker
Pratik Shah, Ph.D.
Dr. Shah is a faculty member at UC Irvine with joint appointments in the School of Medicine and the Henry Samueli School of Engineering. He serves as an expert reviewer for grant panels on emerging deep learning technologies for clinical applications, and he contributes to national science policy as an advisor on regulatory science for generative AI in medical devices.
About the Data Science Seminar Series
CBIIT’s Data Science Seminar Series is dedicating its 2026 events to spotlighting the use of AI in cancer research and care. Brought to you by CBIIT and NCI’s Division of Cancer Treatment and Diagnosis AI working group, the upcoming webinars will explore a variety of questions, such as the following:
- How can AI be used for diagnosis, treatment, or omics research?
- What are the related laws and ethical considerations for AI?
- How can we empower an AI-ready cancer research community through workforce development, collaborations, and funding?
To view upcoming speakers or recordings of past presentations, visit the Data Science Seminar Series page.