Generative AI Accelerates Prediction of Preterm Births, Outperforming Traditional Research Timelines

By Web Desk :

United States Researchers from UC San Francisco and Wayne State University have demonstrated the potential of generative AI to dramatically accelerate biomedical research, successfully analyzing complex pregnancy datasets in minutes and in some cases surpassing human research teams.

In a recent study, AI tools processed large medical datasets to predict preterm births, a leading cause of newborn mortality in the United States, where approximately 1,000 premature births occur daily. To understand risk factors, researchers compiled microbiome data from roughly 1,200 pregnant women across nine separate studies.

Professor Marina Sirota of UCSF highlighted the transformative potential of AI in health research. “These AI tools could relieve one of the biggest bottlenecks in data science: building our analysis pipelines,” she noted, emphasizing that AI can reduce the time and technical expertise required for large-scale data analysis.

In the experiment, eight AI chatbots were tasked with generating analytical code for datasets previously analyzed during a global DREAM challenge. Four of the chatbots produced usable models, and some matched or exceeded the performance of human teams. Notably, the AI-driven project took six months, compared to nearly two years for conventional research to consolidate results.

With AI assistance, even a small team including a master’s student and a high school student was able to construct working prediction models in minutes. Tasks that typically require days of programming by experienced coders were completed rapidly through well-crafted prompts.

Professor Adi L. Tarca of Wayne State University said generative AI allows researchers to focus more on scientific questions rather than coding, accelerating discoveries and enabling broader exploration of complex biomedical datasets.

The study underscores the growing role of AI in biomedical data science, highlighting its potential to transform research efficiency, accuracy, and accessibility in predicting and understanding critical health outcomes like preterm birth.

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