Giansanti Daniele
Centro TISP, Istituto Superiore di Sanità, Via Regina Elena 299, 00161 Rome, Italy.
J Clin Med. 2024 Nov 9;13(22):6745. doi: 10.3390/jcm13226745.
The integration of artificial intelligence (AI) in cytopathology is an emerging field with transformative potential, aiming to enhance diagnostic precision and operational efficiency. This umbrella review seeks to identify prevailing themes, opportunities, challenges, and recommendations related to AI in cytopathology. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements and future implications of AI technologies in this domain. Twenty-one review studies were selected through a systematic process. AI has demonstrated promise in automating and refining diagnostic processes, potentially reducing errors and improving patient outcomes. However, several critical challenges need to be addressed to realize the benefits of AI fully. This review underscores the necessity for rigorous validation, ongoing empirical data on diagnostic accuracy, standardized protocols, and effective integration with existing clinical workflows. Ethical issues, including data privacy and algorithmic bias, must be managed to ensure responsible AI applications. Additionally, high costs and substantial training requirements present barriers to widespread AI adoption. Future directions highlight the importance of applying successful integration strategies from histopathology and radiology to cytopathology. Continuous research is needed to improve model interpretability, validation, and standardization. Developing effective strategies for incorporating AI into clinical practice and establishing comprehensive ethical and regulatory frameworks will be crucial for overcoming these challenges. In conclusion, while AI holds significant promise for advancing cytopathology, its full potential can only be achieved by addressing challenges related to validation, cost, and ethics. This review provides an overview of current advancements, identifies ongoing challenges, and offers a roadmap for the successful integration of AI into diagnostic cytopathology, informed by insights from related fields.
人工智能（AI）在细胞病理学中的整合是一个具有变革潜力的新兴领域，旨在提高诊断准确性和操作效率。本综述旨在确定与细胞病理学中人工智能相关的主要主题、机遇、挑战和建议。利用标准化清单和质量控制程序，本综述考察了人工智能技术在该领域的最新进展和未来影响。通过系统流程筛选出21项综述研究。人工智能已在自动化和优化诊断流程方面展现出前景，有可能减少错误并改善患者预后。然而，要充分实现人工智能的益处，还需应对若干关键挑战。本综述强调了严格验证、关于诊断准确性的持续实证数据、标准化方案以及与现有临床工作流程有效整合的必要性。必须管理包括数据隐私和算法偏差在内的伦理问题，以确保人工智能的负责任应用。此外，高成本和大量培训要求是人工智能广泛应用的障碍。未来方向突出了将组织病理学和放射学的成功整合策略应用于细胞病理学的重要性。需要持续研究以提高模型的可解释性、验证和标准化。制定将人工智能纳入临床实践的有效策略并建立全面的伦理和监管框架对于克服这些挑战至关重要。总之，虽然人工智能在推进细胞病理学方面具有巨大潜力，但只有应对与验证、成本和伦理相关的挑战，才能充分发挥其潜力。本综述概述了当前进展，确定了持续存在的挑战，并提供了将人工智能成功整合到诊断细胞病理学中的路线图，该路线图参考了相关领域的见解。
