A Survey of Clinicians' Views of the Utility of Large Language Models
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View abstract on PubMed
Summary
This summary is machine-generated.Healthcare providers are open to using large language models (LLMs) in clinical settings, particularly for assistive tasks. However, concerns remain regarding potential inaccuracies and bias in LLM-generated content.
Area Of Science
- Artificial Intelligence in Healthcare
- Clinical Informatics
- Natural Language Processing
Background
- Large language models (LLMs) demonstrate human-like text generation capabilities.
- While experts have explored clinical applications, clinician perspectives on LLM readiness remain largely unassessed.
Purpose Of The Study
- To survey practicing clinicians on their comfort levels with LLMs across clinical, research, and educational tasks.
- To identify specific tasks where clinicians perceive LLMs as suitable for healthcare.
Main Methods
- A validated mixed-methods survey was administered to practicing clinicians.
- Survey content focused on LLM applications identified from existing literature.
Main Results
- 16 out of 23 tasks received positive ratings from over 50% of the 30 clinician respondents.
- Clinicians recognized LLMs' strengths in synthesis and efficiency but worried about factual errors and data bias.
- Respondents showed highest comfort with LLMs in assistive roles, akin to physician extenders.
Conclusions
- Clinicians expressed optimism about integrating LLMs into healthcare for various tasks, especially in supportive capacities.
- There is a clear need for human-centered development in LLMs and artificial intelligence within the medical field.
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