Performance of AI-powered chatbots in diagnosing acute pulmonary thromboembolism from given clinical vignettes.

Abstract

Background: Chatbots hold great potential to serve as support tool in diagnosis and clinical decision process. In this study, we aimed to evaluate the accuracy of chatbots in diagnosing pulmonary embolism (PE). Furthermore, we assessed their performance in determining the PE severity.

Method: 65 case reports meeting our inclusion criteria were selected for this study. Two emergency medicine (EM) physicians crafted clinical vignettes and introduced them to the Bard, Bing, and ChatGPT-3.5 with asking the top 10 diagnoses. After obtaining all differential diagnoses lists, vignettes enriched with supplemental data redirected to the chatbots with asking the severity of PE.

Results: ChatGPT-3.5, Bing, and Bard listed PE within the top 10 diagnoses list with accuracy rates of 92.3%, 92.3%, and 87.6%, respectively. For the top 3 diagnoses, Bard achieved 75.4% accuracy, while ChatGPT and Bing both had 67.7%. As the top diagnosis, Bard, ChatGPT-3.5, and Bing were accurate in 56.9%, 47.7% and 30.8% cases, respectively. Significant differences between Bard and both Bing (p=0.000) and ChatGPT (p=0.007) were noted in this group. Massive PEs were correctly identified with over 85% success rate. Overclassification rates for Bard, ChatGPT-3.5 and Bing at 38.5%, 23.3% and 20%, respectively. Misclassification rates were highest in submassive group.

Conclusion: Although chatbots aren't intended for diagnosis, their high level of diagnostic accuracy and success rate in identifying massive PE underscore the promising potential of chatbots as clinical decision support tool. However, further research with larger patient datasets is required to validate and refine their performance in real-world clinical settings.