techend
By Sabeeh Zanair :
A collaborative team from UC San Francisco and Wayne State University has shown that generative AI can rapidly analyze complex pregnancy datasets, providing accurate predictions of preterm births while significantly reducing research timelines.
Preterm birth is the leading cause of newborn mortality in the U.S., with around 1,000 premature births occurring daily. To better identify risk factors, researchers compiled microbiome data from 1,200 pregnant women across nine studies.
Professor Marina Sirota, UCSF, emphasized the breakthrough potential: “Generative AI tools could remove one of the biggest bottlenecks in data science — building analysis pipelines — allowing researchers to focus on scientific questions rather than coding.”
In the study, eight AI chatbots were asked to generate analytical code for the datasets. Four successfully produced usable models, some matching or outperforming human research teams. The AI-driven process took six months, compared with nearly two years using traditional methods.
Remarkably, a small team, including a master’s student and a high school student, was able to build functional predictive models in minutes. Tasks that normally require extensive programming were completed rapidly with AI guidance through detailed prompts.
Professor Adi L. Tarca of Wayne State University noted that generative AI enables researchers to focus on scientific insights rather than technical coding, opening doors for faster discoveries in biomedical research.
This study highlights how AI is reshaping health data analysis, demonstrating its potential to accelerate research, improve predictive accuracy, and enhance understanding of critical medical conditions such as preterm birth.
About The Author
