Sengupta Partho P, Dey Damini, Davies Rhodri H, Duchateau Nicolas, Yanamala Naveena
Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Lancet Digit Health. 2024 Oct;6(10):e739-e748. doi: 10.1016/S2589-7500(24)00142-0. Epub 2024 Aug 29.
Artificial Intelligence (AI), through deep learning, has brought automation and predictive capabilities to cardiac imaging. However, despite considerable investment, tangible health-care cost reductions remain unproven. Although AI holds promise, there has been insufficient time for both methodological development and prospective clinical trials to establish its advantage over human interpretations in terms of its effect on patient outcomes. Challenges such as data scarcity, privacy issues, and ethical concerns impede optimal AI training. Furthermore, the absence of a unified model for the complex structure and function of the heart and evolving domain knowledge can introduce heuristic biases and influence underlying assumptions in model development. Integrating AI into diverse institutional picture archiving and communication systems and devices also presents a clinical hurdle. This hurdle is further compounded by an absence of high-quality labelled data, difficulty sharing data between institutions, and non-uniform and inadequate gold standards for external validations and comparisons of model performance in real-world settings. Nevertheless, there is a strong push in industry and academia for AI solutions in medical imaging. This Series paper reviews key studies and identifies challenges that require a pragmatic change in the approach for using AI for cardiac imaging, whereby AI is viewed as augmented intelligence to complement, not replace, human judgement. The focus should shift from isolated measurements to integrating non-linear and complex data towards identifying disease phenotypes-emphasising pattern recognition where AI excels. Algorithms should enhance imaging reports, enriching patients' understanding, communication between patients and clinicians, and shared decision making. The emergence of professional standards and guidelines is essential to address these developments and ensure the safe and effective integration of AI in cardiac imaging.
人工智能（AI）通过深度学习为心脏成像带来了自动化和预测能力。然而，尽管投入了大量资金，但能否切实降低医疗成本仍未得到证实。虽然人工智能前景广阔，但在方法学发展和前瞻性临床试验方面，仍没有足够的时间来证明其在对患者预后的影响方面优于人类解读。数据稀缺、隐私问题和伦理担忧等挑战阻碍了人工智能的优化训练。此外，对于心脏复杂结构和功能缺乏统一模型以及领域知识不断演变，可能会在模型开发中引入启发式偏差并影响基本假设。将人工智能集成到不同机构的图像存档与通信系统及设备中也存在临床障碍。高质量标注数据的缺失、机构间数据共享的困难以及在现实环境中进行外部验证和模型性能比较时缺乏统一且充分的金标准，进一步加剧了这一障碍。尽管如此，行业和学术界仍在大力推动医学成像领域的人工智能解决方案。本系列文章回顾了关键研究，并确定了在心脏成像中使用人工智能的方法需要务实变革的挑战，即应将人工智能视为增强智能，以补充而非取代人类判断。重点应从孤立的测量转向整合非线性和复杂数据，以识别疾病表型——强调人工智能擅长的模式识别。算法应改进成像报告，增进患者理解、患者与临床医生之间的沟通以及共同决策。专业标准和指南的出现对于应对这些发展并确保人工智能在心脏成像中的安全有效整合至关重要。
