From Tissue to Mechanism to Decision: Building AI for Computational Oncology

About this episode

The episode discusses the challenges and advancements in biomedical AI, particularly in computational oncology, featuring insights from Arvind Rao.

In this episode of Data in Biotech, host Ross Katz sits down with Arvind Rao, Professor of Computational Medicine and Bioinformatics at the University of Michigan, for a discussion on the gap between what biomedical AI can do and what it can reliably be trusted to do in clinical practice. Arvind's research sits at the intersection of computational oncology and AI governance and his lab works across H&E histopathology, multiplex immunofluorescence, spatial transcriptomics, and single-cell RNA sequencing, not just to build predictive models, but to understand the full lifecycle from data to model to inference, and to ask where that lifecycle can be trusted and where it can't. The conversation moves through two of his recent papers on SPIFEE, a graph-based framework that replaces scalar interaction scores in the tumor microenvironment with spatially resolved functional representations, and a multimodal framework that traces a path from stained tissue slides to nominated drug targets via morphological pattern discovery and spatial transcriptomic mapping. What you’ll learn in this episode: >> Why the field's central failure is not algorithmic but translational and the gap…

People in this episode

Host: Ross Katz

Guest: Arvind Rao

Topics covered

• computational oncology
• biomedical AI
• AI governance
• predictive models
• tumor microenvironment
• spatial transcriptomics

Keywords

• AI
• computational medicine
• histopathology
• drug targets
• morphological pattern discovery
• single-cell RNA sequencing

Mentioned in this episode

Organizations: University of Michigan

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