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Hospitals struggle to validate AI-generated clinical summaries Click here to read AI health care companies say they’ll keep humans in the loop. But what does that actually mean? “‘Human in the loop’ is a stop-gap solution to make progress and punt questions around trust, liability, cross population performance, and a full accounting of the consequences of algorithm-guided work to a later date,” Stanford Health Care’s chief data scientist Nigam Shah told STAT. “It’s basically a get out of jail free card in my view.” Click here to read
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Nigam Shah cited in two STAT articles

Hospitals struggle to validate AI-generated clinical summaries
Click here to read

AI health care companies say they’ll keep humans in the loop. But what does that actually mean?

“‘Human in the loop’ is a stop-gap solution to make progress and punt questions around trust, liability, cross population performance, and a full accounting of the consequences of algorithm-guided work to a later date,” Stanford Health Care’s chief data scientist Nigam Shah told STAT. “It’s basically a get out of jail free card in my view.”
Click here to read

A new $5 million grant from the Warren Alpert Foundation was recently awarded to the Department of Biomedical Data Science (DBDS) at the Stanford School of Medicine. The grant will fund the training of 15 graduate scholars over the next five years to enhance training and retention of scholars in computational biology/artificial intelligence (CBAI).
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$5 Million Warren Alpert Foundation Grant To Fund 15 Department of Biomedical Data Science Computational Biology/AI Scholars

A new $5 million grant from the Warren Alpert Foundation was recently awarded to the Department of Biomedical Data Science (DBDS) at the Stanford School of Medicine. The grant will fund the training of 15 graduate scholars over the next five years to enhance training and retention of scholars in computational biology/artificial intelligence (CBAI).

Read the story here: https://dbds.stanford.edu/five-million-warren-alpert-foundation-to-fund-15-computational-biology-ai-scholars/

Akshay Chaudhari's group publishes new paper in Nature Medicine on adapting open-source and closed-source large language models for clinical text summarization tasks. We present a framework for quantitative and qualitative evaluation of language models, showing that the best adapted models can even outperform medical experts.
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Akshay Chaudhari’s group publishes new paper in Nature Medicine on adapting large language models for clinical text summarization tasks

Akshay Chaudhari’s group publishes new paper in Nature Medicine on adapting open-source and closed-source large language models for clinical text summarization tasks. The paper presents a framework for quantitative and qualitative evaluation of language models, showing that the best adapted models can even outperform medical experts.
Read it here: https://www.nature.com/articles/s41591-024-02855-5

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