AI Scanning Technology to Detect Fractures Rolled Out in NHS
AI scanning technology works by analyzing thousands of bone-scan images to learn to recognize fracture patterns.
The National Health Service (NHS) is set to implement artificial intelligence (AI) scanning technology to improve the detection of hard-to-spot fractures in emergency departments.
The move aims to reduce undiagnosed bone breaks, which are among the most common errors in accident and emergency (A&E) settings, according to the National Institute for Health and Care Excellence (NICE).
Addressing Diagnostic Challenges in A&E
Chronic shortages of radiologists and radiographers have contributed to missed fractures in emergency departments.
The NHS estimates that over £1 million is spent annually on compensation for patients whose fractures were overlooked.
To tackle this, NICE has approved four AI programs—TechCare Alert, Rayvolve, BoneView, and RBfracture—designed to enhance fracture detection accuracy by approximately 15%.
Doctors will continue to assess scans, but the AI programs will support their efforts by identifying smaller, less visible breaks.
Experts suggest this dual approach could prevent further injuries and accelerate recovery times for patients.
Improving Patient Outcomes with AI
Every year, approximately 200,000 people in the UK are admitted to hospitals with fractures, with hip, ankle, and hand fractures being the most common.
Patients with osteoporosis, a condition that weakens bones, are particularly vulnerable.
AI scanning technology works by analyzing thousands of bone-scan images to learn to recognize fracture patterns. Similar AI applications are already being used to detect early signs of cancer.
Mark Chapman, NICE’s Director of Health Technology, said, “These AI technologies are safe to use and could spot fractures which humans might miss. Using AI in urgent-care centers has the potential to speed up diagnosis and reduce the need for follow-up appointments caused by missed fractures.”
The rollout of AI scanning tools represents a significant step toward improving diagnostic accuracy and patient care in the NHS.
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