Whole slide images classification code.

The integration of Artificial Intelligence (AI) algorithms into pathology practice presents both opportunities and challenges. Although it can improve accuracy and inter-rater reliability, it is not infallible and can produce erroneous diagnoses, hence the need for pathologists to always check predictions. This critical judgment is particularly important when algorithm errors could lead to high-impact negative clinical outcomes, such as missing an invasive carcinoma. However, the influence of AI tools on pathologists’ decision-making is not well explored. This study aims to evaluate the impact of a previously developed AI tool on the diagnostic accuracy and inter-rater reliability among pathologists, while assessing whether pathologists maintain independent judgment of AI predictions. Eight pathologists from different hospitals and with varying levels of experience, participated in the study. Each of them reviewed 115 slides of laryngeal biopsies, including benign epithelium, low-grade and high-grade dysplasia, and invasive squamous carcinomas. The study compared diagnostic outcomes with and without AI assistance. The reference labels were established by an expert’s double-blind review. Results show that assisted pathologists had a higher accuracy for high-grade dysplasia, invasive carcinoma and improved inter-rater reliability. However, cases of over-reliance on AI have been observed, resulting in the omission of correctly diagnosed invasive carcinomas during the unassisted examination. The false predictions on these carcinoma slides were labeled with a low confidence score, which was not considered by the less experienced pathologists, showing the risk that they would follow the AI prediction without enough critical judgment or expertise. Our study emphasizes the potential over-reliance of pathologists on AI models and the potential harmful consequences, even with the advancement of powerful algorithms. The integration of confidence scores and the education of pathologists to use this tool could help to optimize the safe integration of AI into pathology practice.

人工智能（Artificial Intelligence, AI）算法融入病理诊疗实践，既带来了机遇，也伴随诸多挑战。尽管该技术可提升诊断准确性与阅片者间一致性，但它并非绝对可靠，亦可能产生错误诊断，因此病理医师需始终对AI预测结果进行核查。当算法错误可能引发具有重大临床负面影响的后果（例如漏诊浸润性癌）时，此类审慎判断尤为关键。然而，目前针对AI工具对病理医师决策的影响仍缺乏充分研究。本研究旨在评估一款此前开发的AI工具对病理医师诊断准确性及阅片者间一致性的影响，同时考察病理医师是否会对AI预测结果保持独立判断。本研究共招募了来自不同医院、临床经验水平各异的8名病理医师参与。每位参与者均审阅了115张喉活检玻片，样本涵盖良性上皮组织、低级别异型增生、高级别异型增生及浸润性鳞状细胞癌。本研究对比了病理医师在有AI辅助与无AI辅助两种情况下的诊断结果。金标准标签由一名专家采用双盲法审阅确定。结果显示，获得AI辅助的病理医师对高级别异型增生、浸润性癌的诊断准确性更高，且阅片者间一致性亦得到改善。但研究同时观察到了过度依赖AI的情况：部分病例在无AI辅助时，原本可正确诊断的浸润性癌被漏诊。此类癌性玻片上的AI错误预测均被标记为低置信度评分，但经验不足的病理医师并未关注该评分，这表明他们可能因缺乏足够的审慎判断能力与专业经验，而盲目跟随AI预测结果。本研究强调了病理医师可能会过度依赖AI模型，即便算法性能强劲，亦可能带来潜在的有害后果。将置信度评分纳入考量，并对病理医师开展该工具的使用培训，或有助于优化AI在病理诊疗实践中的安全集成应用。