Jason M. Baron, MD, Editor
For example, clinical laboratory directors must make decisions about which tests to include on their laboratory’s send-out test menus; in recent times, this responsibility has grown to include decisions about multianalyte assays with algorithmic analyses (MAAAs). MAAAs are often based on machine-learning algorithms, a subset of AI. Likewise, anatomic pathologists may be called upon to evaluate whether new digital histopathology image analysis systems and accompanying AI-based algorithms are well-suited to use in their clinical practices. While effectively evaluating these technologies may not require a detailed understanding of the machine learning algorithms underlying the MAAA or the image analysis system, having at least a basic understanding of machine learning and particularly well-known pitfalls may be vital. Moreover, pathologists and laboratorians can serve as invaluable collaborators and domain experts in developing and implementing new AI-based technologies; having at least a basic understanding of AI and the process of algorithm development will enable them to collaborate much more effectively.
A key goal of this issue is to provide pathologists, laboratorians, and others within health care with a background in AI to enable them to (i) strategically evaluate and adopt new technologies; (ii) effectively collaborate in algorithm development initiatives; and (iii) have a strong foundation for more in-depth study of the topic. While this issue alone will not maximally achieve all these objectives, I hope that it will offer a very useful starting point.
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Published online: December 13, 2022
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© 2022 Published by Elsevier Inc.
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