Graf Erin, Soliman Amr, Marouf Mohamed, Parwani Anil V, Pancholi Preeti
Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Phoenix, AZ, US.
Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, US.
Am J Clin Pathol. 2025 Feb 12;163(2):162-168. doi: 10.1093/ajcp/aqae107.
This review summarizes the current and potential uses of artificial intelligence (AI) in the current state of clinical microbiology with a focus on replacement of labor-intensive tasks.
A search was conducted on PubMed using the key terms clinical microbiology and artificial intelligence. Studies were reviewed for relevance to clinical microbiology, current diagnostic techniques, and potential advantages of AI in routine microbiology workflows.
Numerous studies highlight potential labor, as well as diagnostic accuracy, benefits to the implementation of AI for slide-based and macroscopic digital image analyses. These range from Gram stain interpretation to categorization and quantitation of culture growth.
Artificial intelligence applications in clinical microbiology significantly enhance diagnostic accuracy and efficiency, offering promising solutions to labor-intensive tasks and staffing shortages. More research efforts and US Food and Drug Administration clearance are still required to fully incorporate these AI applications into routine clinical laboratory practices.
本综述总结了人工智能(AI)在临床微生物学当前状态下的当前和潜在用途,重点是替代劳动密集型任务。
在PubMed上使用关键词“临床微生物学”和“人工智能”进行搜索。对与临床微生物学、当前诊断技术以及人工智能在常规微生物学工作流程中的潜在优势相关的研究进行了综述。
众多研究强调了人工智能在基于玻片和宏观数字图像分析中的应用在潜在劳动力以及诊断准确性方面的益处。这些应用范围从革兰氏染色解读到培养物生长的分类和定量。
人工智能在临床微生物学中的应用显著提高了诊断准确性和效率,为劳动密集型任务和人员短缺提供了有前景的解决方案。仍需要更多的研究努力和美国食品药品监督管理局的批准,才能将这些人工智能应用全面纳入常规临床实验室实践。