Artificial intelligence technologies to help detect fractures on X-rays in urgent care: early value assessment

Clinical benefit: Clinical evidence suggests that the AI technologies may improve fracture detection on X-rays in urgent care without increasing the risk of incorrect diagnoses. This could help reduce the number of fractures that are missed in urgent care, which would reduce the risk of further injury or harm to people during the time between the initial interpretation and treatment decision in urgent care and the radiology review.

System benefit: AI technologies may help reduce variation in standard care by providing a consistent baseline for X-ray interpretation unaffected by differences in staff experience or resources between centres. AI technologies would also be unaffected by factors such as staff fatigue, distractions, or working outside normal hours.

Resources: Reducing the number of fractures that are missed at initial interpretation would also reduce the number of people that reattend urgent care after discharge or are recalled to hospital after radiology review. Early results from the exploratory economic modelling show that the AI technologies could be cost effective.

Equality: AI technologies have the potential to reduce geographical inequalities in X-ray interpretation and fracture detection, because they may improve fracture detection in smaller centres with fewer and less-experienced staff.

Clinical risk: Using AI technologies to help detect fractures on X-rays in urgent care is considered to have a low clinical risk because they are used in addition to standard care in which healthcare professionals make treatment decisions. Additionally, AI technologies do not replace the definitive radiology review. The available evidence suggests that the AI technologies may improve the accuracy of fracture detection.

Implementation guidance: Clear local protocols will need to be in place when using AI technologies. This is to ensure that healthcare professionals are confident about what action to take when there is disagreement between the healthcare professional and AI technology.

Costs: There is uncertainty around the cost of some software and the true cost of implementation and ongoing post-market surveillance. Costs were estimated at £1 per scan in the exploratory economic modelling. Centres implementing AI to help fracture detection should ensure the cost per scan is similar to the estimated cost. This guidance will be reviewed within 3 years and the recommendations may change. Centres should take this into account when negotiating contract durations and licence costs.

Impact on workforce: If using AI technologies to help fracture detection becomes more widespread and part of the standard diagnostic pathway, there is a risk of over-reliance on the technologies. This could potentially lead to deskilling of the healthcare professionals who interpret the X-rays. It may also reduce the level of scrutiny for non-fracture-related conditions. This risk could be mitigated if healthcare professionals interpret X-rays before viewing the AI results.

Resources: There is a low risk that the AI technologies may increase fracture clinic referrals and requests for further imaging such as CT or MRI. These should be monitored during evidence generation to inform local fracture detection protocols.

Limitations of AI for subgroups: The AI technologies may not be suitable for use in certain groups, for example, children and young people, people with conditions that affect bone health, people with myeloma or people with metastatic bone disease. Centres should ensure that the AI technologies are used within their indications and any limitations are acknowledged and clearly explained to patients.

Equality: There is a risk that the AI technologies may have reduced diagnostic accuracy in different populations, such as people from ethnic minority backgrounds or people with low socioeconomic status. Healthcare professionals should take this into account when interpreting X-rays of people in these groups.