Authors' objectives: Artificial intelligence algorithms have been developed to support clinicians in diagnosing fractures, with the intention to improve the diagnostic accuracy of clinicians reviewing X-rays. The purpose of this rapid early value assessment was to identify the existing evidence base for the technology and to assess whether there was a prima facie case for the technology to represent positive outcomes for patients and a value-for-money investment for people in the National Health Service. Plain film radiography or X-ray is the most common medical imaging approach used to detect fractures in urgent care settings, including the emergency department, urgent treatment centre, and minor injuries units. X-rays are typically read in urgent care settings by healthcare professionals who are not radiology specialists or are inexperienced at interpreting X-rays, which may increase the likelihood of errors in decision-making, particularly in busy healthcare centres when staff are under significant pressure. Reduced staff numbers, such as outside normal working hours, may also influence the risk of errors in diagnosis. A definitive diagnosis of the injury will be produced by a consultant radiologist or reporting radiographer, although there may be a delay before this is available, meaning that this may arrive after people have been treated and/or discharged from urgent care. Delays vary across settings, and may be longer for children due to availability of specialist in paediatrics. Artificial intelligence (AI) algorithms have been developed to support clinicians in diagnosing fractures, with the intention to improve the diagnostic accuracy of clinicians reviewing X-rays. Improving diagnostic accuracy means reducing the number of missed fractures (false-negative diagnoses) and the number of people treated for a fracture who do not have one (false-positive diagnoses). The purpose of this rapid early value assessment (EVA) was to identify the existing evidence base for the technology and to assess whether there was a prima facie case for the technology to represent a value-for-money investment for people in the NHS. A rapid evidence review was conducted followed by ‘light touch’ early economic modelling to explore whether a plausible case could be made for cost-effectiveness at the prices charged by the companies. The approach was not suitable for a definitive assessment of the cost-effectiveness of one AI-algorithm against another, but rather to inform whether or not the NHS should consider adopting the technology while further evidence is collected.

Authors' results and conclusions: Sixteen studies identified evaluated the diagnostic accuracy of the technology. None of the included studies were conducted in the United Kingdom and all were associated with limitations. While the studies were not considered to be able to provide reliable estimates of diagnostic accuracy, there was a trend for the technology to improve sensitivity for detecting fractures. The technology had no discernible impact on the rate of false-positive diagnoses. Overall, most of the evaluated technologies were associated with a positive incremental net health benefit at willingness-to-pay thresholds of £20,000 and £30,000 per quality-adjusted life-year gained. Due to data limitations, it was not possible to compare technologies against each other. The results were mostly robust to scenario analyses.

Authors' methods: This early value assessment assessed the potential value of the use of artificial intelligence to aid clinician diagnosis of fractures in emergency care settings as compared to clinician-diagnosis alone. A rapid evidence review was conducted followed by ‘light touch’ early economic modelling to explore whether a plausible case could be made for cost-effectiveness at the prices charged by the companies. Evidence searches were conducted in June and July 2024 to identify clinical, diagnostic and service outcomes associated with the technology. A simple decision model incorporating prevalence, sensitivity, specificity and cost per scan for each of the technologies was developed to evaluate plausible cost-effectiveness for detecting ankle and foot, wrist and hand, and hip fractures, selected based on the availability of evidence and their downstream costs and consequences. There are significant limitations in the available evidence leading to uncertainties about the diagnostic accuracy of the technology within NHS settings. Due to the pragmatic nature of the early value assessment and the available evidence base, the economic analysis included many gross assumptions and was unable to produce a definitive estimate of cost-effectiveness.

Project Status: Completed

URL for project: https://www.journalslibrary.nihr.ac.uk/programmes/hta/NIHR136024

Year Published: 2026

URL for published report: https://www.journalslibrary.nihr.ac.uk/hta/GJCF0822

URL for additional information: English

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: England, United Kingdom

DOI: 10.3310/GJCF0822

Organisation Name: NIHR Health Technology Assessment programme

Contact Address: NIHR Journals Library, National Institute for Health and Care Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK

Contact Name: journals.library@nihr.ac.uk

Contact Email: journals.library@nihr.ac.uk