Migliaro Stefano, Celotto Roberto, Teliti Romina, Mariani Simona, Altamura Luca, Tomai Fabrizio
Department of Cardiovascular Sciences, European Hospital and Aurelia Hospital, 00165 Rome, Italy.
J Clin Med. 2025 Jun 23;14(13):4452. doi: 10.3390/jcm14134452.
: Multivessel coronary artery disease (CAD) remains a challenging condition requiring multidisciplinary decision-making, particularly when determining between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Recent advancements in artificial intelligence (AI), particularly generative language models like ChatGPT, present an opportunity to assist in the decision-making process. However, their ability to replicate human clinical judgment in complex scenarios, such as multivessel CAD, remains untested. : The aim of this study was to evaluate the concordance between recommendations from AI (ChatGPT) and those from heart team (HT) in the management of multivessel CAD, with a focus on comparing treatment strategies such as PCI and CABG. A retrospective observational study was conducted on 137 patients with multivessel CAD, discussed at multidisciplinary HT meetings in 2024. Standardized clinical vignettes, including clinical and anatomical data, were presented to ChatGPT for treatment recommendations. The AI's responses were compared with the HT's decisions regarding PCI or CABG. Statistical analysis was performed to assess the level of agreement and predictive value of ChatGPT's recommendations. : ChatGPT achieved an overall accuracy of 65% in its recommendations. The agreement rate was higher for CABG (82.4%) than for PCI (44.4%). Discordance was identified in 48 patients, with a notable bias towards recommending CABG. Factors such as age, diabetes, and chronic kidney disease were predictors of discordance, although no significant factors emerged for the PCI or CABG subgroups. : AI, particularly ChatGPT, demonstrated modest concordance with HT decisions in the management of multivessel CAD, especially favoring CABG. While AI offers potential as a decision-support tool, its current limitations highlight the continued need for human clinical judgment in complex cases. Further research is required to optimize AI integration into clinical decision-making frameworks.
多支冠状动脉疾病（CAD）仍然是一种具有挑战性的病症，需要多学科决策，尤其是在决定进行经皮冠状动脉介入治疗（PCI）和冠状动脉旁路移植术（CABG）之间做出选择时。人工智能（AI）的最新进展，特别是像ChatGPT这样的生成式语言模型，为协助决策过程提供了一个机会。然而，它们在复杂场景（如多支CAD）中复制人类临床判断的能力仍未得到检验。
本研究的目的是评估人工智能（ChatGPT）的建议与心脏团队（HT）在多支CAD管理方面的建议之间的一致性，重点是比较PCI和CABG等治疗策略。对2024年在多学科HT会议上讨论的137例多支CAD患者进行了一项回顾性观察研究。向ChatGPT提供标准化的临床病例摘要，包括临床和解剖学数据，以获取治疗建议。将人工智能的回答与HT关于PCI或CABG的决定进行比较。进行统计分析以评估ChatGPT建议的一致程度和预测价值。
ChatGPT的建议总体准确率为65%。CABG的一致率（82.4%）高于PCI（44.4%）。在48例患者中发现了不一致情况，明显倾向于推荐CABG。年龄、糖尿病和慢性肾脏病等因素是不一致的预测因素，尽管在PCI或CABG亚组中没有出现显著因素。
人工智能，特别是ChatGPT，在多支CAD管理方面与HT的决定表现出适度的一致性，尤其倾向于CABG。虽然人工智能作为一种决策支持工具具有潜力，但其目前的局限性凸显了在复杂病例中持续需要人类临床判断。需要进一步研究以优化人工智能在临床决策框架中的整合。
