Accuracy and Reliability of Chatbot Responses to Physician Questions

Abstract

ImportanceNatural language processing tools, such as ChatGPT (generative pretrained transformer, hereafter referred to as chatbot), have the potential to radically enhance the accessibility of medical information for health professionals and patients. Assessing the safety and efficacy of these tools in answering physician-generated questions is critical to determining their suitability in clinical settings, facilitating complex decision-making, and optimizing health care efficiency.ObjectiveTo assess the accuracy and comprehensiveness of chatbot-generated responses to physician-developed medical queries, highlighting the reliability and limitations of artificial intelligence–generated medical information.Design, Setting, and ParticipantsThirty-three physicians across 17 specialties generated 284 medical questions that they subjectively classified as easy, medium, or hard with either binary (yes or no) or descriptive answers. The physicians then graded the chatbot-generated answers to these questions for accuracy (6-point Likert scale with 1 being completely incorrect and 6 being completely correct) and completeness (3-point Likert scale, with 1 being incomplete and 3 being complete plus additional context). Scores were summarized with descriptive statistics and compared using the Mann-Whitney U test or the Kruskal-Wallis test. The study (including data analysis) was conducted from January to May 2023.Main Outcomes and MeasuresAccuracy, completeness, and consistency over time and between 2 different versions (GPT-3.5 and GPT-4) of chatbot-generated medical responses.ResultsAcross all questions (n = 284) generated by 33 physicians (31 faculty members and 2 recent graduates from residency or fellowship programs) across 17 specialties, the median accuracy score was 5.5 (IQR, 4.0-6.0) (between almost completely and complete correct) with a mean (SD) score of 4.8 (1.6) (between mostly and almost completely correct). The median completeness score was 3.0 (IQR, 2.0-3.0) (complete and comprehensive) with a mean (SD) score of 2.5 (0.7). For questions rated easy, medium, and hard, the median accuracy scores were 6.0 (IQR, 5.0-6.0), 5.5 (IQR, 5.0-6.0), and 5.0 (IQR, 4.0-6.0), respectively (mean [SD] scores were 5.0 [1.5], 4.7 [1.7], and 4.6 [1.6], respectively; P = .05). Accuracy scores for binary and descriptive questions were similar (median score, 6.0 [IQR, 4.0-6.0] vs 5.0 [IQR, 3.4-6.0]; mean [SD] score, 4.9 [1.6] vs 4.7 [1.6]; P = .07). Of 36 questions with scores of 1.0 to 2.0, 34 were requeried or regraded 8 to 17 days later with substantial improvement (median score 2.0 [IQR, 1.0-3.0] vs 4.0 [IQR, 2.0-5.3]; P < .01). A subset of questions, regardless of initial scores (version 3.5), were regenerated and rescored using version 4 with improvement (mean accuracy [SD] score, 5.2 [1.5] vs 5.7 [0.8]; median score, 6.0 [IQR, 5.0-6.0] for original and 6.0 [IQR, 6.0-6.0] for rescored; P = .002).Conclusions and RelevanceIn this cross-sectional study, chatbot generated largely accurate information to diverse medical queries as judged by academic physician specialists with improvement over time, although it had important limitations. Further research and model development are needed to correct inaccuracies and for validation.

Performance of Multimodal GPT-4V on USMLE with Image: Potential for Imaging Diagnostic Support with Explanations

“…The influence of ChatGPT is attributed to its conversational prowess and its performance, which approaches or matches human-level competence in cognitive tasks, spanning various domains including medicine. 16 ChatGPT has achieved commendable results in the United States Medical Licensing Examinations, leading to discussions about the readiness of LLM applications for integration into clinical [17][18][19] , educational [20][21][22] , and research 23 environments.…”

Assessment of a Large Language Model’s Responses to Questions and Cases About Glaucoma and Retina Management

“…We compared answers to clinical questions and case management generated by GPT-4 and fellowship-trained retina and glaucoma specialists. We compared the accuracy and completeness of answers, evaluated using a Likert scale, which aligns with a validated approach . Secondary end points explored rating differences between trainees and attendings to assess whether the level of training influenced the perception of the LLM’s responses.…”

“…While these studies showcase the potential of LLM chatbots in specific domains, a broader evaluation of their accuracy, including in comparison with attending-level ophthalmologists, is warranted, particularly for addressing real-life clinical case scenarios . In this study, we compared an LLM chatbot’s responses with those of fellowship-trained glaucoma and retina specialists to explore the potential of LLMs in clinical ophthalmology.…”

Appropriateness of Answers to Common Preanesthesia Patient Questions Composed by the Large Language Model GPT-4 Compared to Human Authors

“…The use of large language models such as GPT-4, which are “generative artificial intelligence” tools capable of creating natural, human-sounding prose in response to a plain-language query, and their incorporation into search engines, promise to make it easier for patients to directly ask questions related to preanesthetic preparation. The accuracy of large language models in answering medical questions has generally been impressive 1–3 but has not been evaluated for preanesthetic queries. We evaluated the ability of the widely accessible model GPT-4 to provide reasonable responses to common preanesthetic patient questions, compared to online published resources.…”

“…Two sessions with ChatGPT were used because the software regenerates new responses when reprompted, and the responses may differ in quality. 1 Survey participants were preoperative anesthesia experts known to the investigators, and other similar experts suggested by this cohort, nearly all of whom were academicians involved with preoperative assessment (total solicited N = 210). The survey instructions asked raters to “evaluate answers to questions about anesthesia care that patients may ask.…”