Attitudes Toward Large Language Models in Health Care and Preferences for Their Adoption and Oversight Among Health Care Professionals: Cross-Sectional Survey - Summary - MDSpire
To explore health care professionals' views on the integration and regulation of large language models (LLMs) in clinical settings.
Approach:
Survey Analysis: Conducted a cross-sectional survey to gather insights from health care professionals regarding their experiences and perspectives on LLMs.
Key Findings:
Physician uptake of AI tools increased from 38% in 2023 to 66% in 2024.
LLMs have significant limitations, particularly in high-stakes clinical environments, including the production of false or hallucinated information.
Ethical concerns regarding algorithmic bias and transparency in training datasets are prevalent.
Regulatory frameworks for LLMs in health care are fragmented and poorly aligned with the rapid development of these technologies.
Interpretation:
Health care professionals face uncertainty regarding the adoption and integration of LLMs due to their limitations and the lack of robust regulatory frameworks.
Limitations:
The survey may not capture the full range of health care professionals' perspectives.
Responses may be influenced by individual experiences and biases.
Conclusion:
The integration of LLMs in health care requires careful consideration of their limitations and the establishment of comprehensive regulatory frameworks.
by Arya Rao, Chinemerem Nwokemodo-Ihejirika, John W R Kincaid, Marharyta Krylova, Kaiz P Esmail, Dan Nguyen, Christian Rivera, Erica Koranteng, Marc D Succi
Operative-time–adjusted findings favored the ipsilateral technique, while absolute fluoroscopy time and radiation exposure remained similar between approaches.
