Future of Artificial Intelligence (AI) - Machine Learning (ML) Trends in Pathology and Medicine

病理 人工智能 数字化病理学 医学 计算机科学 医学物理学
作者
Matthew G. Hanna,Liron Pantanowitz,Rajesh Dash,James H. Harrison,Mustafa Deebajah,Joshua Pantanowitz,Hooman H. Rashidi
出处
期刊:Modern Pathology [Springer Nature]
卷期号:38 (4): 100705-100705 被引量:1
标识
DOI:10.1016/j.modpat.2025.100705
摘要

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.

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