EPISODE · Jan 29, 2026 · 39 MIN
From Data Chaos to Clinical Clarity: How Pharma Turns Messy Clinical & Omics Data into Decisions
from ByteSight · host PAICON
In this ByteSight episode, Dr. Manasi A-Ratnaparkhe speaks with Dr. Paul Agapow about why AI in biomedicine often gets the „right answer for the wrong reason.” Using the famous „wolf-husky problem,” they explore hidden correlations in medical data, why context matters as much as performance, and what it takes to move AI from pilot to real clinical impact. The conversation also dives into clinical trials as the „eye of the needle” for drug development, the cultural barriers to adoption, and why trust and explainability are essential for AI that truly helps patients.
What this episode covers
In this ByteSight episode, Dr. Manasi A-Ratnaparkhe speaks with Dr. Paul Agapow about why AI in biomedicine often gets the „right answer for the wrong reason.” Using the famous „wolf-husky problem,” they explore hidden correlations in medical data, why context matters as much as performance, and what it takes to move AI from pilot to real clinical impact. The conversation also dives into clinical trials as the „eye of the needle” for drug development, the cultural barriers to adoption, and why trust and explainability are essential for AI that truly helps patients.
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From Data Chaos to Clinical Clarity: How Pharma Turns Messy Clinical & Omics Data into Decisions
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