Hoppe John Michael, Kellnar Antonia, Esser David, Diegruber Kathrin, Stremmel Christopher
Department of Medicine IV, Ludwig-Maximilians-Universität München, Munich, Germany.
Department of Medicine I, Ludwig-Maximilians-Universität München, Munich, Germany.
Open Heart. 2025 Jul 20;12(2):e003316. doi: 10.1136/openhrt-2025-003316.
The integration of artificial intelligence (AI) into medical diagnostics has significantly impacted cardiology by enhancing diagnostic precision and therapeutic strategies. Coronary artery disease continues to be a leading cause of global morbidity and mortality, with coronary angiography being the diagnostic gold standard. However, the subjective nature of angiographic interpretation can lead to inconsistent assessment. AI aims to provide automated, objective assessments to mitigate these challenges.
This study evaluated ChatGPT with Generative Pre-trained Transformer (GPT)-4o (OpenAI, USA), for automated coronary angiogram interpretation. Due to its inability to process video data, we extracted maximum contrast frames from diagnostic angiogram views. These anonymised images were analysed by GPT-4o. Its diagnostic findings and stent recommendations were compared with expert cardiologist assessments.
We included 100 patients who underwent coronary interventions between January and April 2024. GPT-4o accurately identified coronary vessels in 98% of images. The overall sensitivity for detecting lesions requiring intervention was 71.6%, with a specificity of 57.2% (F1 score 0.652). Performance varied by vessel with best results for left anterior descending artery (sensitivity 81.0%; specificity 69.3%) and right coronary artery (sensitivity 86.5%; specificity 61.4%). Identification of the target vessel based solely on imaging was 47%, which improved to 87% with additional clinical information.
GPT-4o shows potential as a supportive tool in coronary angiography interpretation. Its diagnostic performance improves significantly when contextual clinical information is included. However, its accuracy based on static images alone remains below the threshold required for reliable diagnostic and therapeutic support. The lack of cine-loop data as an essential element in real-world angiographic interpretation is a key limitation. Future developments should focus on enhancing AI capabilities for analysing complex anatomical structures and integrating dynamic imaging data to augment clinical utility.
将人工智能（AI）整合到医学诊断中，通过提高诊断精度和治疗策略，对心脏病学产生了重大影响。冠状动脉疾病仍然是全球发病和死亡的主要原因，冠状动脉造影是诊断的金标准。然而，血管造影解释的主观性可能导致评估不一致。人工智能旨在提供自动化、客观的评估，以应对这些挑战。
本研究评估了具有生成式预训练变换器（GPT）-4o（美国OpenAI公司）的ChatGPT用于冠状动脉造影自动解释的情况。由于其无法处理视频数据，我们从诊断性血管造影视图中提取了最大对比度帧。这些匿名图像由GPT-4o进行分析。将其诊断结果和支架建议与心脏病专家的评估进行比较。
我们纳入了2024年1月至4月期间接受冠状动脉介入治疗的100例患者。GPT-4o在98%的图像中准确识别出冠状动脉血管。检测需要干预的病变的总体敏感性为71.6%，特异性为57.2%（F1分数为0.652）。不同血管的表现有所不同，左前降支动脉（敏感性81.0%；特异性69.3%）和右冠状动脉（敏感性86.5%；特异性61.4%）的结果最佳。仅基于影像学识别目标血管的比例为47%，增加临床信息后提高到87%。
GPT-4o在冠状动脉造影解释中显示出作为辅助工具的潜力。当纳入背景临床信息时，其诊断性能显著提高。然而，仅基于静态图像的准确性仍低于可靠诊断和治疗支持所需的阈值。缺乏电影环数据作为现实世界血管造影解释中的关键要素是一个主要限制。未来的发展应侧重于增强人工智能分析复杂解剖结构的能力，并整合动态成像数据以提高临床实用性。
