Abstract
Abstract
Background
Large language models (LLMs) have revolutionized the way plastic surgeons and their patients can access and leverage artificial intelligence (AI).
Objectives
The present study aims to compare the performance of 2 current publicly available and patient-accessible LLMs in the potential application of AI as postoperative medical support chatbots in an aesthetic surgeon's practice.
Methods
Twenty-two simulated postoperative patient presentations following aesthetic breast plastic surgery were devised and expert-validated. Complications varied in their latency within the postoperative period, as well as urgency of required medical attention. In response to each patient-reported presentation, Open AI's ChatGPT and Google's Bard, in their unmodified and freely available versions, were objectively assessed for their comparative accuracy in generating an appropriate differential diagnosis, most-likely diagnosis, suggested medical disposition, treatments or interventions to begin from home, and/or red flag signs/symptoms indicating deterioration.
Results
ChatGPT cumulatively and significantly outperformed Bard across all objective assessment metrics examined (66% vs 55%, respectively; P < .05). Accuracy in generating an appropriate differential diagnosis was 61% for ChatGPT vs 57% for Bard (P = .45). ChatGPT asked an average of 9.2 questions on history vs Bard’s 6.8 questions (P < .001), with accuracies of 91% vs 68% reporting the most-likely diagnosis, respectively (P < .01). Appropriate medical dispositions were suggested with accuracies of 50% by ChatGPT vs 41% by Bard (P = .40); appropriate home interventions/treatments with accuracies of 59% vs 55% (P = .94), and red flag signs/symptoms with accuracies of 79% vs 54% (P < .01), respectively. Detailed and comparative performance breakdowns according to complication latency and urgency are presented.
Conclusions
ChatGPT represents the superior LLM for the potential application of AI technology in postoperative medical support chatbots. Imperfect performance and limitations discussed may guide the necessary refinement to facilitate adoption.
Publisher
Oxford University Press (OUP)
Cited by
3 articles.
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