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nigam shah
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Nigam Shah: Which LLM is best for real health care needs?

When a large language model first passed the United States Medical Licensing Exam in 2023, it was a big deal. But two years later, what was once a notable milestone in artificial intelligence progress is more of a bare minimum.

“It’s not enough for a large language model to simply answer medical test questions accurately,” said Nigam H. Shah, MBBS, PhD, chief data scientist at Stanford Health Care. “That type of evaluation doesn’t tell us anything about what matters.”

Read more: https://scopeblog.stanford.edu/2025/04/08/ai-artificial-intelligence-evaluation-algorithm/

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Nima Agheepour in Nature Medicine: AI can help doctors give intravenous nutrition to preemies, Stanford Medicine study finds

An algorithm that learned from tens of thousands of nutrition prescriptions for premature babies could reduce medical errors and better identify what nutrients the smallest patients need.

You may remember that Nima presented this work at the C&C in January.
Read it here: https://med.stanford.edu/news/all-news/2025/03/prematurity-nutrition0.html

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Gevaert group published in Nature Machine Intelligence: Towards a more inductive world for drug repurposing approaches

Nature Machine Intelligence:
In collaboration with Dr. Mikel Hernaez at CIMA, the Gevaert lab has benchmarked inductive and transductive methods for drug target interaction discovery. We show that transductive methods don’t generalize and lead to inflated performance when traditionally evaluated, making them unsuitable for drug repurposing. We propose a biologically driven inductive strategy for negative-edge subsampling.
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