Picture this: A lung cancer drug candidate was headed for abandonment until AI-powered pathology analysis identified which patients would benefit from it. Using machine learning and digital pathology, the two sponsors backing the candidate built an algorithm capable of distinguishing responders from non-responders to the compound they were developing. “Without that AI model and digital biomarker, they were considering retiring the drug,” explained Nathan Buchbinder, co-founder of the…
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https://www.drugdiscoverytrends.com/how-proscia-is-using-ai-embeddings-to-transform-drug-discovery-in-pathology/
