• Generative AI analyzes medical data faster than human research teams In an early real world test of artificial intelligence in health research, scientists at UC San Francisco and Wayne State University discovered that generative AI could process enormous medical datasets far faster than traditional computer science teams – and in some cases produce even stronger results. • Human experts had spent months carefully analyzing the same information. • To compare performance directly, researchers assigned identical tasks to different groups. • Some teams relied entirely on human expertise, while others used scientists working with AI tools. • The challenge was to predict preterm birth using data from more than 1,000 pregnant women. • Even a junior research pair made up of a UCSF master’s student, Reuben Sarwal, and a high school student, Victor Tarca, successfully developed prediction models with AI support.
Article Summaries:
- Researchers at UCSF and Wayne State University tested generative AI on a large preterm‑birth dataset, comparing its performance to traditional human research teams. In several cases, the AI matched or surpassed models built by experts who had spent months on the task. By generating analytical code from precise prompts, the AI reduced pipeline development from days or weeks to minutes, enabling a junior research pair to produce a publishable model in a few months. Only half of the tested chatbots produced usable code, but those that did required no large specialist teams. The study, published in Cell Reports Medicine, suggests AI could accelerate data‑driven medical discoveries.
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