Artificial intelligence and infectious disease diagnostics: state of the art and future perspectives

Affiliations

¹ Centre for Antimicrobial Optimisation Network, Department of Infectious Disease, Imperial College London, London, UK.
² Centre for Antimicrobial Optimisation Network, Department of Infectious Disease, Imperial College London, London, UK; Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; The David Price Evans Global Health & Infectious Diseases Group, The University of Liverpool, Liverpool, UK; The Fleming Initiative, Imperial College London and Imperial College Healthcare NHS Trust, London, UK.
³ The African Centre of Excellence in Bioinformatics and Data Intensive Sciences, Kampala, Uganda; The Infectious Diseases Institute, Makerere University, Kampala, Uganda.
⁴ Bristol Veterinary School, University of Bristol, Bristol, UK.
⁵ Centre for Antimicrobial Optimisation Network, Department of Infectious Disease, Imperial College London, London, UK; The Fleming Initiative, Imperial College London and Imperial College Healthcare NHS Trust, London, UK.
⁶ MinoHealth AI Labs, Ashongman Estates, Ghana.
⁷ FIND, Geneva, Switzerland.
⁸ Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.
⁹ Rare and Imported Pathogens Laboratory, UK Health Security Agency, Wiltshire, UK.
¹⁰ Infectious Diseases Department, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Hospital Clínic de Barcelona, Barcelona, Spain.
¹¹ Centre for Antimicrobial Optimisation Network, Department of Infectious Disease, Imperial College London, London, UK; Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; The David Price Evans Global Health & Infectious Diseases Group, The University of Liverpool, Liverpool, UK; The Fleming Initiative, Imperial College London and Imperial College Healthcare NHS Trust, London, UK; ProtonDx, Translation & Innovation Hub, Imperial College London, London, UK. Electronic address: j.rodriguez-manzano@imperial.ac.uk.

PMID: 40972627
DOI: 10.1016/S1473-3099(25)00354-8

Review

Artificial intelligence and infectious disease diagnostics: state of the art and future perspectives

Luca Miglietta et al. Lancet Infect Dis. 2026 Mar.

. 2026 Mar;26(3):e168-e180.

doi: 10.1016/S1473-3099(25)00354-8. Epub 2025 Sep 16.

Authors

Affiliations

¹ Centre for Antimicrobial Optimisation Network, Department of Infectious Disease, Imperial College London, London, UK.
² Centre for Antimicrobial Optimisation Network, Department of Infectious Disease, Imperial College London, London, UK; Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; The David Price Evans Global Health & Infectious Diseases Group, The University of Liverpool, Liverpool, UK; The Fleming Initiative, Imperial College London and Imperial College Healthcare NHS Trust, London, UK.
³ The African Centre of Excellence in Bioinformatics and Data Intensive Sciences, Kampala, Uganda; The Infectious Diseases Institute, Makerere University, Kampala, Uganda.
⁴ Bristol Veterinary School, University of Bristol, Bristol, UK.
⁵ Centre for Antimicrobial Optimisation Network, Department of Infectious Disease, Imperial College London, London, UK; The Fleming Initiative, Imperial College London and Imperial College Healthcare NHS Trust, London, UK.
⁶ MinoHealth AI Labs, Ashongman Estates, Ghana.
⁷ FIND, Geneva, Switzerland.
⁸ Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.
⁹ Rare and Imported Pathogens Laboratory, UK Health Security Agency, Wiltshire, UK.
¹⁰ Infectious Diseases Department, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Hospital Clínic de Barcelona, Barcelona, Spain.
¹¹ Centre for Antimicrobial Optimisation Network, Department of Infectious Disease, Imperial College London, London, UK; Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; The David Price Evans Global Health & Infectious Diseases Group, The University of Liverpool, Liverpool, UK; The Fleming Initiative, Imperial College London and Imperial College Healthcare NHS Trust, London, UK; ProtonDx, Translation & Innovation Hub, Imperial College London, London, UK. Electronic address: j.rodriguez-manzano@imperial.ac.uk.

PMID: 40972627
DOI: 10.1016/S1473-3099(25)00354-8

Abstract

Artificial intelligence (AI) is reshaping infectious disease diagnostics by supporting clinical decision making, optimising laboratory and clinical workflows, and enabling real-time disease surveillance. AI approaches improve pathogen detection, antimicrobial stewardship, and treatment monitoring, enhancing diagnostic accuracy, efficiency, and scalability. The role of AI in combating antimicrobial resistance is particularly significant, enabling rapid pathogen identification and personalised treatment. Despite progress over the past two decades, widespread AI adoption in infectious disease diagnostics faces challenges. In high-income countries, fragmented data ecosystems, incomplete datasets, and algorithmic bias hinder clinical integration. Meanwhile, low-income and middle-income countries contend with limited digital infrastructure, unstandardised data, and financial constraints, exacerbating disparities in diagnostic access. Further barriers include concerns over interoperability, data privacy, cybersecurity, and the regulation of AI implementation. This paper examines the role of AI in infectious disease diagnostics, highlighting both opportunities and limitations. It underscores the need for coordinated investments in digital infrastructure, harmonised data-sharing frameworks, and clinician engagement to support equitable, sustainable adoption. Addressing these challenges will enable health-care systems to harness the potential of AI to improve infectious disease detection, prevention, and management of infectious diseases, thereby strengthening global health resilience.

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Conflict of interest statement

Declaration of interests AHH is a senior investigator for the National Institute for Health Research. TMR has received honoraria from Sandoz (2020), BioMerieux (2021–2024), Pfizer (2024), and Roche Diagnostics (2021). JR-M is cofounder, chief scientific officer, and shareholder in ProtonDx. DA is founder, chief executive officer, and shareholder in minoHealth AI Labs. This paper does not directly relate to, endorse, or promote the products, services, or technologies of either company. EOA is a named inventor on patent application number WO2021/161050 A1: Detection of cognitive impairment in human brains from images. This patent is unrelated to any product mentioned in the review. All other authors declare no competing interests.

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Artificial intelligence and infectious disease diagnostics: state of the art and future perspectives

Affiliations

Artificial intelligence and infectious disease diagnostics: state of the art and future perspectives

Authors

Affiliations

Abstract

Conflict of interest statement

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources

Medical

Miscellaneous