Clinical Accuracy, Relevance, Clarity, and Emotional Sensitivity of Large Language Models to Surgical Patient Questions: Cross-Sectional Study
Mert Marcel Dagli, Felix C. Oettl, Jaskeerat Gujral, Kashish Malhotra, Yohannes Ghenbot, Jang W. Yoon, Ali K. Ozturk, William C. Welch
Abstract
This cross-sectional study evaluates the clinical accuracy, relevance, clarity, and emotional sensitivity of responses to inquiries from patients undergoing surgery provided by large language models (LLMs), highlighting their potential as adjunct tools in patient communication and education. Our findings demonstrated high performance of LLMs across accuracy, relevance, clarity, and emotional sensitivity, with Anthropic's Claude 2 outperforming OpenAI's ChatGPT and Google's Bard, suggesting LLMs' potential to serve as complementary tools for enhanced information delivery and patient-surgeon interaction.
Topics & Concepts
CLARITYCross-sectional studyRelevance (law)Clinical significancePsychologyMedicinePathologyPolitical scienceLawChemistryBiochemistryArtificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic SkillsPatient-Provider Communication in Healthcare