PDF(Pubmed)
Abstract：
Clinical Decision Support Systems (CDSS) are essential tools in contemporary healthcare, enhancing clinicians\' decisions and patient outcomes. The integration of artificial intelligence (AI) is now revolutionizing CDSS even further. This review delves into AI technologies transforming CDSS, their applications in healthcare decision-making, associated challenges, and the potential trajectory toward fully realizing AI-CDSS\'s potential. The review begins by laying the groundwork with a definition of CDSS and its function within the healthcare field. It then highlights the increasingly significant role that AI is playing in enhancing CDSS effectiveness and efficiency, underlining its evolving prominence in shaping healthcare practices. It examines the integration of AI technologies into CDSS, including machine learning algorithms like neural networks and decision trees, natural language processing, and deep learning. It also addresses the challenges associated with AI integration, such as interpretability and bias. We then shift to AI applications within CDSS, with real-life examples of AI-driven diagnostics, personalized treatment recommendations, risk prediction, early intervention, and AI-assisted clinical documentation. The review emphasizes user-centered design in AI-CDSS integration, addressing usability, trust, workflow, and ethical and legal considerations. It acknowledges prevailing obstacles and suggests strategies for successful AI-CDSS adoption, highlighting the need for workflow alignment and interdisciplinary collaboration. The review concludes by summarizing key findings, underscoring AI\'s transformative potential in CDSS, and advocating for continued research and innovation. It emphasizes the need for collaborative efforts to realize a future where AI-powered CDSS optimizes healthcare delivery and improves patient outcomes.
摘要：
临床决策支持系统（CDSS）是当代医疗保健中必不可少的工具，提高临床医生的决策和患者的预后。人工智能（AI）的集成现在正在进一步彻底改变CDSS。这篇综述深入探讨了人工智能技术转变CDSS，它们在医疗保健决策中的应用，相关挑战,以及充分发挥AI-CDSS潜力的潜在轨迹。审查首先为CDSS的定义及其在医疗保健领域的功能奠定了基础。然后强调了人工智能在提高CDSS有效性和效率方面发挥的日益重要的作用，强调其在塑造医疗保健实践方面不断发展的突出地位。它研究了将AI技术集成到CDSS中，包括神经网络和决策树等机器学习算法，自然语言处理，和深度学习。它还解决了与AI集成相关的挑战，比如可解释性和偏见。然后，我们转向CDSS中的AI应用程序，通过人工智能驱动诊断的真实例子，个性化治疗建议，风险预测，早期干预,和AI辅助的临床文档。该评论强调在AI-CDSS集成中以用户为中心的设计，解决可用性，信任,工作流,以及道德和法律方面的考虑。它承认普遍存在的障碍，并提出了成功采用AI-CDSS的策略，强调工作流程调整和跨学科协作的必要性。审查最后总结了主要发现，强调AI在CDSS中的变革潜力，并倡导继续研究和创新。它强调需要共同努力，以实现未来的AI驱动的CDSS优化医疗保健服务并改善患者预后。
