Can Generative AI Analyze Medical Data Faster Than Humans?

Artificial intelligence (AI) has revolutionized many industries, but could it now rival human experts in medical research? A new study suggests that generative AI can analyze complex medical datasets faster and with similar accuracy to teams of trained researchers.

That potential breakthrough could transform how scientists handle massive health data and accelerate discoveries in critical areas such as preterm birth prediction.

What Researchers Tested With Generative AI

In a collaboration between the University of California, San Francisco and Wayne State University, scientists evaluated whether generative AI could perform complex data analysis tasks traditionally tackled by experienced computational teams. They used datasets involving more than 1,000 pregnant women to build predictive models for preterm birth outcomes.

Human research teams had spent months developing these models through standard statistical and machine learning workflows. By contrast, generative AI was supplied with detailed prompts and tasked with generating analytical code to build prediction models.

Results: Speed and Performance of AI vs Human Teams

The key finding was that AI systems significantly reduced analysis timelines:

• Junior researchers using AI were able to generate usable code in minutes, where traditional programming might take hours or days.
• Out of eight different generative AI models tested, four produced analytical output comparable to human teams.
• In some cases, the AI-generated models performed on par with, or better than, human-constructed models.
• The entire process, from initial prompts to publication submission, took about six months — a fraction of the usual research timeline.

These results suggest that generative AI can help researchers overcome major bottlenecks in health data analysis.

What This Means for Predictive Health Research

The implications are significant. Preterm birth remains a leading cause of newborn mortality and long-term health challenges. Better predictive models could inform earlier interventions and improve care outcomes.

By reducing the time required to process large datasets, generative AI could enable research teams to focus more on interpretation, hypothesis refinement, and clinical application, rather than debugging code or building analysis pipelines.

Limitations and Human Oversight Needed

Despite promising results, the study highlights limitations:

• Only half of the generative AI tools evaluated produced usable output.
• AI can generate misleading or incorrect results if not carefully monitored.
• Domain expertise is still crucial to craft precise prompts and validate outcomes.

Experts emphasize that generative AI should supplement, not replace, human expertise in medical research.

Looking Ahead: AI in Medical Analytics

Generative AI’s ability to write analytical code and handle complex datasets quickly could redefine research workflows. This efficiency may prove critical in areas where rapid insights directly affect patient outcomes.

The study’s authors envision a future where AI accelerates hypothesis testing, supports smaller labs lacking extensive computational resources, and democratizes access to advanced analytics.

Fast Facts: Generative AI in Medical Data Analysis Explained

How was generative AI tested against human researchers in medical data analysis?

Researchers from the University of California, San Francisco and Wayne State University evaluated generative AI models using clinical datasets from more than 1,000 pregnant women. The AI systems were prompted to generate analytical code and predictive models for preterm birth. These outputs were then compared to models developed by experienced human research teams. The study measured accuracy, model performance, and development time to determine whether generative AI could match traditional biomedical data workflows.

How accurate was generative AI compared to traditional research teams?

In the study, eight generative AI models were evaluated. Four produced analytical outputs comparable to those created by human teams. In certain cases, the AI-generated predictive models performed on par with or slightly better than those built manually. However, results varied depending on the specific AI system used. The researchers emphasized that while generative AI showed strong potential, not every model consistently met professional research standards without expert review and validation.

What are the limitations of generative AI in medical research?

Although generative AI can accelerate medical data analysis, not all models succeed, and outputs can be misleading without expert oversight. Human researchers are still essential for interpretation and validation.