Hanna Matthew G, Pantanowitz Liron, Dash Rajesh, Harrison James H, Deebajah Mustafa, Pantanowitz Joshua, Rashidi Hooman H
Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence, University of Pittsburgh, Pittsburgh, Pennsylvania.
Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence, University of Pittsburgh, Pittsburgh, Pennsylvania.
Mod Pathol. 2025 Apr;38(4):100705. doi: 10.1016/j.modpat.2025.100705. Epub 2025 Jan 4.
Artificial intelligence (AI) and machine learning (ML) are transforming the field of medicine. Health care organizations are now starting to establish management strategies for integrating such platforms (AI-ML toolsets) that leverage the computational power of advanced algorithms to analyze data and to provide better insights that ultimately translate to enhanced clinical decision-making and improved patient outcomes. Emerging AI-ML platforms and trends in pathology and medicine are reshaping the field by offering innovative solutions to enhance diagnostic accuracy, operational workflows, clinical decision support, and clinical outcomes. These tools are also increasingly valuable in pathology research in which they contribute to automated image analysis, biomarker discovery, drug development, clinical trials, and productive analytics. Other related trends include the adoption of ML operations for managing models in clinical settings, the application of multimodal and multiagent AI to utilize diverse data sources, expedited translational research, and virtualized education for training and simulation. As the final chapter of our AI educational series, this review article delves into the current adoption, future directions, and transformative potential of AI-ML platforms in pathology and medicine, discussing their applications, benefits, challenges, and future perspectives.
人工智能（AI）和机器学习（ML）正在改变医学领域。医疗保健组织现在开始制定管理策略，以整合此类平台（AI-ML工具集），这些平台利用先进算法的计算能力来分析数据，并提供更好的见解，最终转化为增强的临床决策和改善的患者治疗效果。病理学和医学中新兴的AI-ML平台及趋势正在重塑该领域，通过提供创新解决方案来提高诊断准确性、优化操作流程、提供临床决策支持并改善临床结果。这些工具在病理学研究中也越来越有价值，它们有助于自动图像分析、生物标志物发现、药物开发、临床试验和高效分析。其他相关趋势包括在临床环境中采用ML操作来管理模型、应用多模态和多智能体AI以利用多样化数据源、加速转化研究以及用于培训和模拟的虚拟化教育。作为我们AI教育系列的最后一章，这篇综述文章深入探讨了AI-ML平台在病理学和医学中的当前应用、未来方向及变革潜力，讨论了它们的应用、益处、挑战和未来展望。
