Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician

Background: Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection. Objectives: We summarise recent and potential future applications of AI and its relevance to clinical infection practice.Methods: 1617 PubMed results were screened, with priority given to clinical trials, systematic reviews and meta-analyses. This narrative review focusses on studies using prospectively collected real-world data with clinical validation, and on research with translational potential, such as novel drug discovery and micro-biome-based interventions. Results: There is some evidence of clinical utility of AI applied to laboratory diagnostics (e.g. digital culture plate reading, malaria diagnosis, antimicrobial resistance profiling), clinical imaging analysis (e.g. pul-monary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial pre-scribing) and public health outbreak management (e.g. COVID-19). Most studies to date lack any real-world validation or clinical utility metrics. Significant heterogeneity in study design and reporting limits com-parability. Many practical and ethical issues exist, including algorithm transparency and risk of bias.Conclusions: Interest in and development of AI-based tools for infection research and management are undoubtedly gaining pace, although the real-world clinical utility to date appears much more modest.& COPY; 2023 The Author(s). Published by Elsevier Ltd on behalf of The British Infection Association. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).