Background: Artificial intelligence (AI) has shown promise in the interpretation of electrocardiograms (ECGs) using signal-based deep learning models. In parallel, large language models (LLMs) have gained increasing visibility in clinical practice, including exploratory applications in ECG analysis. Whether a general-purpose LLM can meaningfully discriminate cardiovascular disease from athlete ECGs during PPS remains unknown. We aimed to evaluate the diagnostic performance of a general-purpose LLM for this task. Methods: In this multicentre diagnostic accuracy study, we evaluated a commercially available LLM (ChatGPT, version 5) in 2950 competitive athletes undergoing PPS. All athletes underwent resting 12-lead ECG, with second- and third-line investigations performed when clinically indicated. The reference outcome was confirmed cardiovascular disease after full diagnostic work-up (n = 450, 15.3%). For each ECG, the LLM generated a numeric score (0–100) representing the inferred likelihood of underlying disease using a standardized prompt and without task-specific fine-tuning. Discriminative performance was assessed using receiver operating characteristic (ROC) analysis. Misclassification patterns were analysed according to International ECG Criteria. Results: GPT-derived scores demonstrated a marked floor effect, with a median value of 0 (IQR 0–2) in both diseased and non-diseased athletes and substantial overlap between groups. The area under the ROC curve was 0.52 (95% CI 0.49–0.55), indicating performance close to random classification. At the Youden-derived threshold, 79% of athletes with confirmed disease were incorrectly classified as negative. False-negative cases were predominantly characterized by borderline ECG patterns (82%), and a substantial number of red-flag ECG abnormalities were also missed. Conclusions: In this PPS cohort, a general-purpose LLM used in a naïve configuration showed no clinically meaningful ability to discriminate between cardiovascular disease and athlete ECGs. Without task-specific training or domain adaptation, such models should not be used for diagnostic triage in athlete screening.

Antiplatelet and anticoagulation therapy are commonly used in the general population and sometimes in athletes experiencing cardiovascular disorders. In these cases, the treatment has to be tailored according to the individual bleeding and thrombotic risk profile, also considering the intrinsic risk of sports activities when advising athletes for eligibility for competitive sports. In athletes, it is necessary to pre-assess the individual bleeding risk, considering not only the personal bleeding risk (usually low in athletes) but also the type of sport the athlete would like to practice, with careful consideration in sports where traumatic collisions are highly likely. Additionally, non-steroidal anti-inflammatory drugs are commonly used among athletes, and antiplatelet therapy may further increase the bleeding risk. Therefore, in selected competitive athletes, the default approach for antithrombotic therapy could be personalized. This review discusses the clinical management challenges of competitive athletes under antithrombotic or antiplatelet therapy, focusing on the intrinsic risks of sports practice and the indications for sports eligibility and disqualification.