AI Cuts Bradford Skin Cancer Waiting Times by Up to 31% and Boosts NHS Capacity

Introduction

Skin cancer referrals are soaring across the UK, with Bradford alone receiving ~5,000 suspected cases each year. Yet only 8 % (about 400 patients) are confirmed malignant. Long waiting lists have strained dermatology services, prompting the search for faster, more accurate triage tools.

The AI Technology: Skin Analytics DERM

The system adopted by Bradford Teaching Hospitals is Skin Analytics’ DERM, an autonomous AI medical device that analyses dermoscopic images in seconds. It highlights high‑risk lesions for immediate biopsy while safely discharging low‑risk cases without a clinician review.

Implementation at St Luke’s Hospital

• Start date: April (year not specified)
• Location: Dermatology department, St Luke’s Hospital, Bradford
• Team: Dermatologists, consultant plastic surgeon Zakir Shariff, and trust General Manager Tom White.

How it works

1. Rapid scanning – up to 32 patients per session (vs. 24 pre‑AI).
2. AI risk scoring – lesions flagged for urgent biopsy are sent straight to pathology.
3. Autonomous discharge – benign lesions are cleared at the first appointment.

Measurable Impact on Waiting Times

| Metric | Before AI | After AI | % Change | |--------|-----------|----------|----------| | Patients per session | 24 | 32 | +33 % | | Average waiting time (overall) | Baseline | Reduced by 24‑31 % | — | | Urgent referrals needing dermatologist review | 100 % | ↓ to ~69 % | 31 % fewer reviews | | Clinician capacity saved | — | 2,851 hrs | 62 % increase | | Additional face‑to‑face slots possible | — | 8,500+ per year | — |

“It will significantly reduce the number of urgent referrals that require review by dermatologists, reducing waiting times and allowing us to concentrate on the most urgent cases,” – Mr Zakir Shariff, consultant plastic surgeon.

Capacity Gains and Clinician Time

The NHS study from Chelsea & Westminster Hospital showed DERM discharged 31 % and 25 % of patients at two Trust hospitals without any clinician input. Tele‑dermatologists then discharged another 24‑25 %. Combined, this saved 2,851 hours of clinician time – a 62 % boost in clinical capacity, translating to over 8,500 extra face‑to‑face appointments annually.

Patient Story: Faster Diagnosis Saves a Life

A local patient’s melanoma was flagged by the AI during the first scan. Within two weeks the lesion was biopsied and surgically removed. “The process was seamless – I had an early diagnosis and treatment, which gave me peace of mind,” she said.

National Rollout and Future Outlook

• NICE recommendation: AI DERM is approved for NHS use for the next three years while evidence continues to be collected.
• Adoption: Already deployed in 40 NHS trusts; Bradford Teaching Hospitals was the first in Yorkshire.
• Tom White (General Manager, Dermatology) calls the early evidence a “game changer” for specialist capacity.

Conclusion

The AI‑driven DERM system is reshaping skin‑cancer pathways in Bradford by:

• Cutting waiting times by up to 31 %;


• Increasing patient throughput by 33 % per session;


• Freeing 62 % of clinician time for high‑risk cases.

If the rollout continues at this pace, the NHS could see thousands of unnecessary appointments eliminated, faster cancer diagnoses, and better outcomes for patients across the country.

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Sources: BBC News, Yahoo! Science, Bradford Hospitals communications, Skin‑Analytics NHS study, TikTok video, Facebook round‑up.