AI in healthcare: New research shows promise and limitations of physicians working with GPT-4 for decision making
- Date:
- October 28, 2024
- Source:
- University of Minnesota Medical School
- Summary:
- Researchers have studied how well doctors used GPT-4 -- an artificial intelligence (AI) large language model system -- for diagnosing patients.
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Published in JAMA Network Open, a collaborative team of researchers from the University of Minnesota Medical School, Stanford University, Beth Israel Deaconess Medical Center and the University of Virginia studied how well doctors used GPT-4 -- an artificial intelligence (AI) large language model system -- for diagnosing patients.
The study was conducted with 50 U.S.-licensed physicians in family medicine, internal medicine and emergency medicine. The research team found that the availability of GPT-4 to physicians as a diagnostic aid did not significantly improve clinical reasoning compared to conventional resources. Other key findings include:
- GPT-4 alone demonstrated significantly better scores in diagnostic performance, surpassing the performance of clinicians using conventional diagnostic online resources and clinicians assisted by GPT-4.
- There was no significant enhancement in diagnostic performance with the addition of GPT-4 when assessing clinicians using GPT-4 against clinicians using conventional diagnostic resources.
"The field of AI is expanding rapidly and impacting our lives inside and outside of medicine. It is important that we study these tools and understand how we best use them to improve the care we provide as well as the experience of providing it," said Andrew Olson, MD, a professor at the U of M Medical School and hospitalist with M Health Fairview. "This study suggests that there are opportunities for further improvement in physician-AI collaboration in clinical practice."
These results underline the complexity of integrating AI into clinical practice. While GPT-4 alone showed promising results, the integration of GPT-4 as a diagnostic aid alongside clinicians did not significantly outperform the use of conventional diagnostic resources. This suggests a nuanced potential for AI in healthcare, emphasizing the importance of further exploration into how AI can best support clinical practice. Further, more studies are needed to understand how clinicians should be trained to use these tools.
The four collaborating institutions have launched a bi-coastal AI evaluation network -- known as ARiSE -- to further evaluate GenAI outputs in healthcare.
Funding for this research was provided by the Gordon and Betty Moore Foundation.
Story Source:
Materials provided by University of Minnesota Medical School. Original written by Alex Smith. Note: Content may be edited for style and length.
Journal Reference:
- Ethan Goh, Robert Gallo, Jason Hom, Eric Strong, Yingjie Weng, Hannah Kerman, Joséphine A. Cool, Zahir Kanjee, Andrew S. Parsons, Neera Ahuja, Eric Horvitz, Daniel Yang, Arnold Milstein, Andrew P. J. Olson, Adam Rodman, Jonathan H. Chen. Large Language Model Influence on Diagnostic Reasoning. JAMA Network Open, 2024; 7 (10): e2440969 DOI: 10.1001/jamanetworkopen.2024.40969
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