在心脏成像中增强智能的挑战。

IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Lancet Digital Health Pub Date : 2024-08-29 DOI:10.1016/S2589-7500(24)00142-0
Prof Partho P Sengupta MD , Prof Damini Dey PhD , Rhodri H Davies PhD , Nicolas Duchateau PhD , Naveena Yanamala PhD
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引用次数: 0

摘要

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,但切实的医疗成本降低仍未得到证实。虽然人工智能大有可为,但还没有足够的时间来进行方法开发和前瞻性临床试验,以确定其在对患者预后的影响方面相对于人工解读的优势。数据稀缺、隐私问题和伦理问题等挑战阻碍了最佳的人工智能培训。此外,由于缺乏针对心脏复杂结构和功能的统一模型以及不断发展的领域知识,在模型开发过程中可能会出现启发式偏差并影响基本假设。将人工智能整合到不同的机构图片存档和通信系统及设备中也是一个临床障碍。缺乏高质量的标注数据、机构间难以共享数据,以及用于外部验证和比较真实世界环境中模型性能的黄金标准不统一和不充分,都进一步加剧了这一障碍。尽管如此,业界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。这篇系列论文回顾了主要研究,并指出了在将人工智能用于心脏成像时需要务实改变方法的挑战,即把人工智能视为补充而非取代人类判断的增强智能。重点应从孤立的测量转向整合非线性和复杂的数据,以确定疾病表型--强调人工智能擅长的模式识别。算法应强化成像报告,丰富患者的理解、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安全有效地融入心脏成像至关重要。
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Challenges for augmenting intelligence in cardiac imaging
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.
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来源期刊
CiteScore
41.20
自引率
1.60%
发文量
232
审稿时长
13 weeks
期刊介绍: The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health. The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health. We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.
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