Attitudes Toward Large Language Models in Health Care and Preferences for Their Adoption and Oversight Among Health Care Professionals: Cross-Sectional Survey - Scorecard - MDSpire
Clinical Scorecard: Health Care Professionals' Perspectives on the Integration and Regulation of Large Language Models: A Cross-Sectional Survey Analysis
At a Glance
Category
Detail
Condition
Large Language Models in Healthcare
Key Mechanisms
Clinical and operational applications including diagnosis, treatment, patient communication, clinical documentation, and medical education.
Target Population
Healthcare professionals utilizing AI tools.
Care Setting
Clinical and operational healthcare environments.
Key Highlights
LLM adoption among physicians increased from 38% in 2023 to 66% in 2024.
LLMs can produce false or hallucinated information, particularly in high-stakes settings.
Ethical issues include biases in machine learning and lack of transparency in training datasets.
Regulation of LLMs is fragmented with no standardized predeployment validation requirements.
Healthcare professionals face uncertainty in integrating LLMs into clinical workflows.
Guideline-Based Recommendations
Diagnosis
Management
Monitoring & Follow-up
Risks
Potential for false or hallucinated information from LLMs.
Algorithmic bias that may exacerbate health disparities.
Patient & Prescribing Data
Not specified.
Integration of LLMs in clinical decision support and patient communication.
Clinical Best Practices
Recognize limitations of LLMs in clinical settings.
Address ethical considerations and biases in AI tools.
Establish clear regulatory frameworks for LLM deployment.
by Arya Rao, Chinemerem Nwokemodo-Ihejirika, John W R Kincaid, Marharyta Krylova, Kaiz P Esmail, Dan Nguyen, Christian Rivera, Erica Koranteng, Marc D Succi
