Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening.

ABSTRACT: Novel screening and diagnostic tests based on artificial intelligence (AI) image recognition algorithms are proliferating. Some initial reports claim outstanding accuracy followed by disappointing lack of confirmation, including our own early work on cervical screening. This is a presentation of lessons learned, organized as a conceptual step-by-step approach to bridge the gap between the creation of an AI algorithm and clinical efficacy. The first fundamental principle is specifying rigorously what the algorithm is designed to identify and what the test is intended to measure (eg, screening, diagnostic, or prognostic). Second, designing the AI algorithm to minimize the most clinically important errors. For example, many equivocal cervical images cannot yet be labeled because the borderlin

SUBMITTER: Egemen D 

PROVIDER: S-EPMC10777665 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature