The DBDS Education Committee undertook a “Curriculum Review Project” that culminated in a published review about graduate curriculum in Biomedical Data Science. Read it ahead of print here: https://www.annualreviews.org/content/journals/10.1146/annurev-biodatasci-090624-022951. The full publication will be available in August. Authors: Christine Y. Yeh, Dennis P. Wall, Karen Matthys, Chiara Sabatti, and Julia A. Palacios.
In an rousing event that connected industry leaders from biopharma, consulting, tech, and start-up companies with Stanford faculty, postdocs, and students from the Department of Biomedical Data Science (DBDS), the future for research in the AI sphere felt limitless.
Read more here: https://dbds.stanford.edu/ai-frontiers-of-healthcare-and-medicine-summit/
More than three-quarters of the AI software cleared by the Food and Drug Administration for medical use is designed to support radiology practice, says Curtis Langlotz, a radiology professor at Stanford University and president of the Radiological Society of North America’s board of directors.
“AI is not a better kind of intelligence, it’s a different kind of intelligence,” Langlotz says. “A human plus a machine is better than either one alone. I would say that has been true since I began studying AI in the 1980’s, and it continues to be true today.”
Read more here: https://wapo.st/4jk7cmB
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/