Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review

被引:48
作者
Herman, Daniel S. [1 ]
Rhoads, Daniel D. [2 ,3 ]
Schulz, Wade L. [4 ]
Durant, Thomas J. S. [4 ]
机构
[1] Univ Penn, Dept Pathol & Lab Med, Philadelphia, PA 19104 USA
[2] Cleveland Clin, Dept Lab Med, Cleveland, OH 44106 USA
[3] Case Western Reserve Univ, Cleveland Clin, Lerner Coll Med, Dept Pathol, Cleveland, OH 44106 USA
[4] Yale Univ, Dept Lab Med, New Haven, CT USA
关键词
artificial intelligence; machine learning; laboratory medicine; supervised machine learning; MACHINE LEARNING APPROACH; IMAGE-ANALYSIS; CLINICAL MICROBIOLOGY; REFERENCE INTERVALS; FLOW-CYTOMETRY; CLASSIFICATION; DIAGNOSIS; HEALTH; VALIDATION; ALGORITHM;
D O I
10.1093/clinchem/hvab165
中图分类号
R446 [实验室诊断]; R-33 [实验医学、医学实验];
学科分类号
1001 ;
摘要
BACKGROUND: Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine. CONTENT: In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine. SUMMARY: AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.
引用
收藏
页码:1466 / 1482
页数:17
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