Chew Boon-How, Ngiam Kee Yuan
Department of Biomedical Informatics, Yong Loo Lin School of Medicine, National University of Singapore C/O NUHS Tower Block, Level 8, 1E Kent Ridge Road, Singapore, 119228, Singapore.
Department of Family Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, 43400, Malaysia.
BMC Med. 2025 Apr 24;23(1):244. doi: 10.1186/s12916-025-04076-0.
Digital medicine and smart healthcare will not be realised without the cognizant participation of clinicians. Artificial intelligence (AI) today primarily involves computers or machines designed to simulate aspects of human intelligence using mathematically designed neural networks, although early AI systems relied on a variety of non-neural network techniques. With the increased complexity of the neural layers, deep machine learning (ML) can self-learn and augment many human tasks that require decision-making on the basis of multiple sources of data. Clinicians are important stakeholders in the use of AI and ML tools. The review questions are as follows: What is the typical process of AI tool development in the full cycle? What are the important concepts and technical aspects of each step? This review synthesises a targeted literature review and reports and summarises online structured materials to present a succinct explanation of the whole development process of AI tools. The development of AI tools in healthcare involves a series of cyclical processes: (1) identifying clinical problems suitable for AI solutions, (2) forming project teams or collaborating with experts, (3) organising and curating relevant data, (4) establishing robust physical and virtual infrastructure, and computer systems' architecture that support subsequent stages, (5) exploring AI neural networks on open access platforms before making a new decision, (6) validating AI/ML models, (7) registration, (8) clinical deployment and continuous performance monitoring and (9) improving the AI ecosystem ensures its adaptability to evolving clinical needs. A sound understanding of this would help clinicians appreciate the development of AI tools and engage in codesigning, evaluating and monitoring the tools. This would facilitate broader use and closer regulation of AI/ML tools in healthcare settings.
没有临床医生的切实参与，数字医学和智能医疗就无法实现。如今，人工智能（AI）主要涉及使用数学设计的神经网络来模拟人类智能各方面的计算机或机器，不过早期的人工智能系统依赖于多种非神经网络技术。随着神经层复杂性的增加，深度机器学习（ML）能够自我学习并增强许多需要基于多源数据进行决策的人类任务。临床医生是使用人工智能和机器学习工具的重要利益相关者。综述问题如下：人工智能工具全周期开发的典型流程是什么？每个步骤的重要概念和技术方面有哪些？本综述综合了有针对性的文献综述以及报告，并总结了在线结构化材料，以简要解释人工智能工具的整个开发过程。医疗保健领域人工智能工具的开发涉及一系列循环过程：（1）识别适合人工智能解决方案的临床问题，（2）组建项目团队或与专家合作，（3）组织和整理相关数据，（4）建立强大的物理和虚拟基础设施以及支持后续阶段的计算机系统架构，（5）在做出新决策之前在开放获取平台上探索人工智能神经网络，（6）验证人工智能/机器学习模型，（7）注册，（8）临床部署以及持续性能监测，（9）改善人工智能生态系统以确保其适应不断变化的临床需求。对此有充分的理解将有助于临床医生了解人工智能工具的开发，并参与工具的联合设计、评估和监测。这将促进人工智能/机器学习工具在医疗保健环境中的更广泛使用和更严格监管。
