Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are transferable to flow cytometry and have the potential to improve efficiency and prioritization of cases, reduce errors, and highlight fundamental, previously unrecognized associations with underlying biological processes. As a multidisciplinary group of stakeholders, we review a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine. In particular, we provide practical guidance for the development, implementation, and suggestions for potential regulation of AI‐based methods in the clinical flow cytometry laboratory. We expect these recommendations to be a helpful initial framework of reference, which will also require additional updates as the field matures.

中文翻译：

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在临床流式细胞术中使用人工智能的建议

流式细胞术是诊断许多血液恶性肿瘤的关键临床工具，传统上需要具有专业领域知识的血液病理学家仔细检查数字数据。人工智能 (AI) 的进步可转移到流式细胞术，并有可能提高效率和病例优先级、减少错误，并突出与潜在生物过程的基本的、以前未被识别的关联。作为一个多学科的利益相关者小组，我们审查了将人工智能适当应用于临床流式细胞术的一系列关键考虑因素，包括用例识别、低风险和高风险用例、验证、再验证、计算考虑因素以及当前围绕人工智能的监管框架。临床医学。特别是，我们为临床流式细胞术实验室中基于人工智能的方法的潜在监管提供实用指导和建议。我们希望这些建议成为有用的初始参考框架，随着该领域的成熟，也需要额外的更新。