Evaluating interactions of patients with large language models for medical information
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Summary
This summary is machine-generated.Patients found large language models (LLMs) useful for medical information, rating them highly for understandability and user-friendliness. While urologists were preferred for complex questions, LLMs showed potential in clinical settings for patient education.
Area Of Science
- Medical Informatics
- Artificial Intelligence in Healthcare
- Patient Engagement
Background
- Large language models (LLMs) are increasingly explored for their potential in healthcare.
- Understanding patient interaction with LLM-powered chatbots in clinical settings is crucial for adoption.
- Assessing the quality and utility of medical information provided by LLMs is an emerging research area.
Purpose Of The Study
- To investigate real-world patient interactions with an LLM-powered chatbot for urological counseling.
- To evaluate the perceived utility, completeness, understandability, and user-friendliness of LLM-generated medical information.
- To compare patient preferences for information sources: LLMs, urologists, or search engines.
Main Methods
- 300 patients seeking urological advice interacted with a GPT-4 chatbot.
- Post-interaction surveys assessed perceived information quality and user-friendliness.
- Patients compared LLMs, urologists, and search engines for answering their questions.
Main Results
- Most of the 292 completers found the chatbot useful, complete, understandable, and user-friendly.
- Human urologists were rated higher for understandability than LLMs.
- 53% of patients preferred LLMs over search engines for answering questions.
Conclusions
- LLMs show significant potential for enhancing patient education and communication in clinical environments.
- The user-friendliness and comprehensiveness of LLMs are valued by patients for medical information.
- LLMs may help alleviate healthcare provider time constraints by addressing preliminary patient queries.
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