Mapping the susceptibility of large language models to medical misinformation across clinical notes and social media: a cross-sectional benchmarking analysis

Key Points

• This analysis aims to evaluate how susceptible large language models are to medical misinformation found in clinical notes and social media.
• Cross-sectional study design
• Evaluation of large language models
• Comparison of responses to clinical notes and social media content
• Benchmarking for assessment
• Identified significant vulnerabilities in language models to misinformation
• Differences in susceptibility found between clinical notes and social media content
• Highlighted the need for improved training methods