Journal of Infection / 2023

Artificial Intelligence, Machine Learning and Deep Learning: Potential Resources for the Infection Clinician.

A. Theodosiou, R. Read

Computer VisionFoundation ModelsReinforcement Learning

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 1,617 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 microbiome-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. pulmonary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial prescribing) 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 comparability. 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.

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Title: Artificial Intelligence, Machine Learning and Deep Learning: Potential Resources for the Infection Clinician.
Authors: A. Theodosiou, R. Read
Year: 2023
Venue: Journal of Infection
Categories: Computer Vision, Foundation Models, Reinforcement Learning
Citations: 173
Paper URL: https://www.semanticscholar.org/paper/c83df91176d8cef858af7173496fdfc274f46574
Open PDF: Not available

Abstract:
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
1,617 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 microbiome-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. pulmonary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial prescribing) 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 comparability. 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.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Artificial%20Intelligence%2C%20Machine%20Learning%20and%20Deep%20Learning%3A%20Potential%20Resources%20for%20the%20Infection%20Clinician.)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Artificial%20Intelligence%2C%20Machine%20Learning%20and%20Deep%20Learning%3A%20Potential%20Resources%20for%20the%20Infection%20Clinician.)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/c83df91176d8cef858af7173496fdfc274f46574)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Artificial%20Intelligence%2C%20Machine%20Learning%20and%20Deep%20Learning%3A%20Potential%20Resources%20for%20the%20Infection%20Clinician.+paper+explained)