Generative Artificial Intelligence in Anatomic Pathology

被引:3
作者
Brodsky, Victor [1 ,14 ]
Ullah, Ehsan [2 ]
Bychkov, Andrey [3 ,4 ]
Song, Andrew H. [5 ]
Walk, Eric E. [6 ]
Louis, Peter [7 ]
Rasool, Ghulam [8 ,9 ,10 ]
Singh, Rajendra S. [12 ]
Mahmood, Faisal [5 ]
Bui, Marilyn M. [11 ]
V. Parwani, Anil [13 ]
机构
[1] Washington Univ, Dept Pathol & Immunol, Sch Med St Louis, St. Louis, MO USA
[2] Hlth New Zealand, Dept Surg, Cty Manukau, New Zealand
[3] Kameda Med Ctr, Dept Pathol, Kamogawa City, Chiba, Japan
[4] Nagasaki Univ, Dept Pathol, Nagasaki, Japan
[5] Brigham & Womens Hosp, Dept Pathol, Boston, MA USA
[6] PathAI, Boston, MA USA
[7] Rutgers Robert Wood Johnson Med Sch, Dept Pathol & Lab Med, Lab Med, New Brunswick, NJ USA
[8] Univ South Florida Hlth, Morsani Coll Med, Dept Oncol Sci, Tampa, FL USA
[9] Univ S Florida, Dept Elect Engn, Tampa, Romania
[10] H Lee Moffitt Canc Ctr & Res Inst, Dept Machine Learning Rasool, Neurooncol, Tampa, FL USA
[11] H Lee Moffitt Canc Ctr & Res Inst, Dept Pathol, Tampa, FL USA
[12] Summit Hlth, Dermatopathol & Digital Pathol, Berkley Hts, NJ 07922 USA
[13] Ohio State Univ, Dept Pathol, Columbus, OH USA
[14] Washington Univ, BJC Inst Hlth, Dept Pathol & Immunol, Sch Med St Louis, 425 S Euclid Ave, St Louis, MO 63110 USA
关键词
FOUNDATION MODEL; AI; MEDICINE; CANCER; CLASSIFICATION; PERFORMANCE; PREDICTION; IMAGES;
D O I
10.5858/arpa.2024-0215-RA
中图分类号
R446 [实验室诊断]; R-33 [实验医学、医学实验];
学科分类号
1001 ;
摘要
Context.-Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities. Objective.-To explore the applications, benefits, and challenges of generative AI in anatomic pathology, with a focus on its impact on diagnostic processes, workflow efficiency, education, and research. Data Sources.-A comprehensive review of current literature and recent advancements in the application of generative AI within anatomic pathology, categorized into unimodal and multimodal applications, and evaluated for clinical utility, ethical considerations, and future potential. Conclusions.-Generative AI demonstrates significant promise in various domains of anatomic pathology, including diagnostic accuracy enhanced through AI-driven image analysis, virtual staining, and synthetic data generation; workflow efficiency, with potential for improvement by automating routine tasks, quality control, and reflex testing; education and research, facilitated by AI-generated educational content, synthetic histology images, and advanced data analysis methods; and clinical integration, with preliminary surveys indicating cautious optimism for nondiagnostic AI tasks and growing engagement in academic settings. Ethical and practical challenges require rigorous validation, prompt engineering, federated learning, and synthetic data generation to help ensure trustworthy, reliable, and unbiased AI applications. Generative AI can potentially revolutionize anatomic pathology, enhancing diagnostic accuracy, improving workflow efficiency, and advancing education and research. Successful integration into clinical practice will require continued interdisciplinary collaboration, careful validation, and adherence to ethical standards to ensure the benefits of AI are realized while maintaining the highest standards of patient care.
引用
收藏
页码:298 / 318
页数:21
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