AI Chatbots Decode Big Data with Precise Prompts

Researchers find AI tools can perform data analysis tasks faster and sometimes better than human teams.

Published on Feb. 20, 2026

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Researchers at UC San Francisco and Wayne State University found that generative AI tools could perform data analysis tasks orders of magnitude faster than computer science teams that had spent months poring over the data. Even a junior research duo was able to generate working computer code in minutes with AI assistance, a task that would have taken experienced programmers at least a few hours and up to a few days. The AI's strength came from its ability to write code to analyze health data based on a short but highly specialized prompt.

Why it matters

A faster path from data to discovery could lead to more reliable diagnostic testing for preterm birth, the leading cause of newborn death and a leading cause of long-term motor and cognitive impairment in children. Scientists still don't know much about what leads to preterm birth, so the ability to quickly parse massive amounts of data could free up researchers to focus on answering the right biomedical questions.

The details

The researchers instructed eight AI tools to build algorithms to make pregnancy assessments using the same data from three DREAM challenges, but with no human input. The AI chatbots were given natural language prompts to accomplish this, similar to how ChatGPT works, but the prompts were carefully phrased to guide the AI to assess the human health data the way the DREAM teams had. Only 4 of the 8 AI tools produced prediction models that performed as well as those made by the DREAM teams, but sometimes the AI outperformed the human teams. And the entire generative AI project — from inception to submission of a paper — took just six months.

  • The data used in this study was generated in part with support from the Pregnancy Research Branch of the NICHD.
  • The paper was published on February 17, 2026.

The players

UC San Francisco

The University of California, San Francisco (UCSF) is exclusively focused on the health sciences and is dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and excellence in patient care.

Wayne State University

A public research university in Detroit, Michigan that is home to the Center for Molecular Medicine and Genetics.

March of Dimes

A nonprofit organization that works to improve the health of mothers and babies.

Marina Sirota

A professor of Pediatrics who is the interim director of the Bakar Computational Health Sciences Institute (BCHSI) at UCSF and the principal investigator of the March of Dimes Prematurity Research Center at UCSF.

Tomiko T. Oskotsky

The co-director of the March of Dimes Preterm Birth Data Repository, associate professor in UCSF BCHSI, and co-author of the paper.

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What they’re saying

“These AI tools could relieve one of the biggest bottlenecks in data science: building our analysis pipelines. The speed-up couldn't come sooner for patients who need help now.”

— Marina Sirota, Professor of Pediatrics, interim director of the Bakar Computational Health Sciences Institute (BCHSI) at UCSF, and principal investigator of the March of Dimes Prematurity Research Center at UCSF (Cell Reports Medicine)

“This kind of work is only possible with open data sharing, pooling the experiences of many women and the expertise of many researchers.”

— Tomiko T. Oskotsky, Co-director of the March of Dimes Preterm Birth Data Repository, associate professor in UCSF BCHSI, and co-author of the paper (Cell Reports Medicine)

“Thanks to generative AI, researchers with a limited background in data science won't always need to form wide collaborations or spend hours debugging code. They can focus on answering the right biomedical questions.”

— Adi L. Tarca, Co-senior author and professor in the Center for Molecular Medicine and Genetics at Wayne State University (Cell Reports Medicine)

What’s next

Scientists still need to be on guard for misleading results from the AI tools and step in when the AI fails. The technology is no replacement for human expertise, but its power could enable researchers to quickly parse massive amounts of data and free them up to focus on answering the right biomedical questions.

The takeaway

This study demonstrates the potential for generative AI tools to accelerate data analysis in biomedical research, enabling scientists with limited data science backgrounds to quickly generate working models and focus on the core scientific questions. However, human oversight and expertise remain crucial to ensure the validity of the results and to guide the AI towards meaningful insights.