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New Skin Cancer Detection Technology to be Widely Available

New Skin Cancer Detection Technology to be Widely Available

New Skin Cancer Detection Technology to be Widely Available

A new technological advancement called DERM is set to be implemented on a larger scale, enabling medical professionals to quickly identify skin cancer in patients. This innovative technology can identify 11 types of lesions, including common skin cancers, within seconds.

In addition to speeding up cancer diagnoses, DERM is expected to save dermatologists a significant amount of time. This will allow them to attend to patients with other skin conditions, such as psoriasis, vitiligo, eczema, and lupus, who may have been given lower priority due to cancer taking precedence.

DERM functions by analyzing photos of a patient’s skin, taken by a nurse, photographer, or healthcare assistant using a specialized lens. This reduces the strain on dermatologists’ schedules and enables more efficient use of their time.

In a recent clinical trial conducted at several hospitals, DERM successfully identified over 3,500 cancer cases. It also helped avoid around 10,000 unnecessary face-to-face appointments. As a result of its success, the technology is now scheduled to be rolled out across the National Health Service (NHS) in the United Kingdom.

Consultant dermatologist Dr. Lucy Thomas emphasized that AI technology like DERM is unlikely to replace doctors. However, by freeing up dermatologists’ time, all patients can benefit from faster assessment and access to treatment.

The implementation of DERM comes at a critical time, as there is a dermatology crisis in hospitals, with three-quarters of potential cancer cases not being seen within the recommended 14-day timeframe. Furthermore, 18,000 individuals with non-life-threatening conditions have been waiting for over a year to receive care.

With DERM’s widespread availability, the hope is that it will alleviate some of the strain on dermatology services and improve the overall efficiency of skin cancer detection and treatment.

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