Generative Artificial Intelligence in Anatomic Pathology.

Victor Brodsky, Ehsan Ullah, Andrey Bychkov, Andrew H Song, Eric E Walk, Peter Louis, Ghulam Rasool, Rajendra S Singh, Faisal Mahmood, Marilyn M Bui, Anil V Parwani
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Abstract

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 being addressed by 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.

生成式人工智能在解剖病理学中的应用。
上下文。生成式人工智能(AI)已成为包括解剖病理学在内的各个领域的变革力量,在这些领域,它提供了显著提高诊断准确性、工作流程效率和研究能力的潜力。-:探索生成式人工智能在解剖病理学中的应用、好处和挑战,重点关注其对诊断过程、工作流程效率、教育和研究的影响。数据源。-:对生成式人工智能在解剖病理学中应用的当前文献和最新进展进行了全面回顾,分为单模态和多模态应用,并对临床效用、伦理考虑和未来潜力进行了评估。-:生成式人工智能在解剖病理学的各个领域显示出巨大的前景,包括通过人工智能驱动的图像分析、虚拟染色和合成数据生成提高诊断准确性;工作流程效率,有可能通过自动化日常任务、质量控制和反射测试来改进;通过人工智能生成的教育内容、合成的组织学图像和先进的数据分析方法,促进教育和研究;初步调查显示,人们对非诊断人工智能任务持谨慎乐观态度,并越来越多地参与到学术环境中。伦理和实践挑战需要通过严格的验证、快速的工程、联合学习和合成数据生成来解决,以帮助确保值得信赖、可靠和公正的人工智能应用。生成式人工智能可能会彻底改变解剖病理学,提高诊断准确性,提高工作流程效率,并推进教育和研究。成功地融入临床实践需要持续的跨学科合作、仔细的验证和遵守道德标准,以确保实现人工智能的好处,同时保持最高标准的患者护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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