Autonomous reporting of 'normal' chest X-rays by artificial intelligence in the United Kingdom; can we take the human out of the loop?
Authors
Affiliations (20)
Affiliations (20)
- Oxford Clinical Artificial Intelligence Research (OxCAIR), Oxford University Hospitals, Oxford, OX3 9DU, United Kingdom.
- Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, OX3 9DU, United Kingdom.
- Radiology Department, Oxford University Hospital, Oxford, OX3 9DU, United Kingdom.
- Department of Radiology, Sheffield Teaching Hospitals FT Trust, Sheffield, S5 7AU, United Kingdom.
- NIHR Sheffield Biomedical Research Centre, Sheffield, S10 2JF, United Kingdom.
- School of Medicine & Population Health, University of Sheffield, Sheffield, S10 2TN, United Kingdom.
- Imaging Department, UCLH, NW1 2BU, United Kingdom.
- Lungs for Living Research Centre, University College London, London, WC1E 6JF, United Kingdom.
- College of Medicine and Health, University of Birmingham, Birmingham, B15 2TT, United Kingdom.
- National Institute for Health and Care Research Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, B15 2TH, United Kingdom.
- Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, B15 2TH, United Kingdom.
- Warwick Medical School, University of Warwick, Coventry, CV4 7AL, United Kingdom.
- Radiology Department, University Hospitals Coventry and Warwickshire, Coventry, CV2 2DX, United Kingdom.
- Radiology Department, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom.
- Greater Manchester Cancer Alliance, Manchester, M20 4BX, United Kingdom.
- Radiology Department, Queen Elizabeth University Hospital, Govan, Glasgow, G51 4TF, United Kingdom.
- Digital Health Validation Lab, University of Glasgow, Glasgow, G51 4TF, United Kingdom.
- Emergency Department, Queen Elizabeth University Hospital, Glasgow, G51 4TF, United Kingdom.
- Emergency Medicine Research Oxford (EMROx), Oxford University Hospitals, NHS Foundation Trust, Oxford, OX3 9DU, United Kingdom.
- Emergency Department, John Radcliffe Hospital, Headley Way, OX3 9DU, United Kingdom.
Abstract
Chest X-rays (CXRs) are the most commonly performed imaging investigation. In the UK, many centers experience reporting delays due to radiologist workforce shortages. Artificial intelligence (AI) tools capable of distinguishing "normal" from "abnormal" CXRs have emerged as a potential solution. If "normal" CXRs could be safely identified and reported without human input, a substantial portion of radiology workload could be reduced. This article examines the feasibility and implications of autonomous AI reporting of "normal" CXRs, using the United Kingdom as an example setting. Key issues include defining "normal," ensuring generalizability across populations, and managing the sensitivity-specificity trade-off. It also addresses legal and regulatory challenges, such as compliance with IR(ME)R and GDPR, and the lack of accountability frameworks for errors. Further considerations include the impact on radiologists practice, the need for robust post-market surveillance, and incorporation of patient perspectives. While the benefits are clear, adoption must be cautious, with strong governance, legal clarity, and rigorous clinical validation to ensure safe and sustainable use.