Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare.

Autor: Feng J; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. jean.feng@ucsf.edu.; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA. jean.feng@ucsf.edu., Phillips RV; Department of Biostatistics, University of California, Berkeley, CA, USA., Malenica I; Department of Biostatistics, University of California, Berkeley, CA, USA., Bishara A; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.; Department of Anesthesia, University of California, San Francisco, CA, USA., Hubbard AE; Department of Biostatistics, University of California, Berkeley, CA, USA., Celi LA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Department of Medicine, Beth Israel Deaconess Medical Center; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA., Pirracchio R; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.; Department of Anesthesia, University of California, San Francisco, CA, USA.
Jazyk: angličtina
Zdroj: NPJ digital medicine [NPJ Digit Med] 2022 May 31; Vol. 5 (1), pp. 66. Date of Electronic Publication: 2022 May 31.
DOI: 10.1038/s41746-022-00611-y
Abstrakt: Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness. To bring AI into maturity in clinical care, we advocate for the creation of hospital units responsible for quality assurance and improvement of these algorithms, which we refer to as "AI-QI" units. We discuss how tools that have long been used in hospital quality assurance and quality improvement can be adapted to monitor static ML algorithms. On the other hand, procedures for continual model updating are still nascent. We highlight key considerations when choosing between existing methods and opportunities for methodological innovation.
(© 2022. The Author(s).)
Databáze: MEDLINE
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