Artificial Intelligence in Cardiac Treatment Decision-Making: An Evaluation of the Performance of ChatGPT Versus the Heart Team in Coronary Revascularization.

BACKGROUND

This study aimed to investigate the performance of two versions of ChatGPT (o1 and 4o) in making decisions about coronary revascularization and to compare the recommendations of these versions with those of a multidisciplinary Heart Team. Moreover, the study aimed to assess whether the decisions generated by ChatGPT, based on the internal knowledge base of the system and clinical guidelines, align with expert recommendations in real-world coronary artery disease management. Given the increasing prevalence and processing capabilities of large language models, such as ChatGPT, this comparison offers insights into the potential applicability of these systems in complex clinical decision-making.

METHODS

We conducted a retrospective study at a single center, which included 128 patients who underwent coronary angiography between August and September 2024. The demographics, medical history, current medications, echocardiographic findings, and angiographic findings for each patient were provided to the two ChatGPT versions. The two models were then asked to choose one of three treatment options: coronary artery bypass grafting (CABG), percutaneous coronary intervention (PCI), or medical therapy, and to justify their choice. Performance was assessed using metrics such as accuracy, sensitivity, specificity, precision, F1 score, Cohen's kappa, and Shannon's entropy.

RESULTS

The Heart Team recommended CABG for 78.1% of the patients, PCI for 12.5%, and medical therapy for 9.4%. ChatGPT o1 demonstrated higher sensitivity in identifying patients who needed CABG (82%) but lower sensitivity for PCI (43.7%), whereas ChatGPT 4o performed better in recognizing PCI candidates (68.7%) but was less accurate for CABG cases (43%). Both models struggled to identify patients suitable for medical therapy, with no correct predictions in this category. Agreement with the Heart Team was low (Cohen's kappa: 0.17 for o1 and 0.03 for 4o). Notably, these errors were often attributed to the limited understanding of the model in a clinical context and the inability to analyze angiographic images directly.

CONCLUSION

While ChatGPT-based artificial intelligence (AI) models show promise in assisting with cardiac care decisions, the current limitations of these models emphasize the need for further development. Incorporating imaging data and enhancing comprehension of clinical context is essential to improve the reliability of these AI models in real-world medical settings.