Skin Analytics Raises £4M Towards to Expand AI Skin Cancer Screening

06-10-2020 |   |  By Robin Mitchell

Skin Analytics, a UK company specialising in AI diagnosis, have raised £4 million in funding to help improve their technology as well as target other markets including the US. What is skin cancer, how can AI help with diagnosis, and will AI slowly replace doctors for diagnosis?

What is skin cancer?

As the name suggests, skin cancer is a cancer of the skin, whereby skin cells become cancerous and begin to grow uncontrollably. Skin cancer falls under two major types; basal/squamous cell carcinoma and melanoma. Basal/squamous cell carcinoma cancers are rarely life-threatening which grows slowly, rarely spread beyond the skin, easily found, and easily cured. Melanoma, however, is a more aggressive cancer that can be life-threatening and can spread into the bloodstream whereby it can then grow around the body including the bones and brain. Most skin cancers are visible on the epidermis, and usually take the form of a mole which can have patterns and discolourations. As a result, skin cancer is highly survivable it detected early, and detection can be easily done with a simple examination followed by a biopsy.

Why is skin cancer unique for emerging AI systems?

While AI systems can in theory be made to work with any sensory information, they are heavily used in visual systems including facial recognition and object detection. Therefore, visual applications that deploy AI solutions are often highly effective, and the same can be applied to skin cancer. Since skin cancer detection is a highly visual task, visual AI systems are highly suitable for detecting such cancers. On top of that, skin cancer is a highly documented condition with millions of skin cancer mole photos available for AI to learn from. 

Skin Analytics Raises £4M in Funding

Skin Analytics is a company that specialises in AI for detecting skin cancer on patients. The system developed by Skin Analytics helps to improve detection rates while simultaneously removing unnecessary biopsies (60% reduction in referrals). The system, which has been medically tested, allow for doctors to perform quick tests on patients and has been awarded the “Breakthrough Device Designation” by the FCA. To help push the AI diagnostic tool into the US and UK markets, Skin Analytics recently secured £4 million in a Series A funding round

In conjunction with the new funding, Skin Analytics will continue to work in conjunction with the NHS to launch the worlds first AI-powered clinical diagnostic system at University Hospital Birmingham. According to Skin Analytics, the ability to reduce the number of patients who see dermatologists not only saves money on unnecessary appointments, it also increases the ratio of important cases that dermatologists examine. To aid in the operation, Skin Analytics also offers a CE marked device that is able to recognise skin cancers, pre-cancerous, and benign lesions.

What role will AI play in the future?

While AI is still in its infancy, it has untold amounts of potential in almost any field it is applied to, including medical sciences. One of the key concepts behind medical diagnosis and research is the ability to identify patterns, and this is something that AI excels at. AI systems are also immune to medical bias and assumptions, something which doctors can often be guilty of, and an AI diagnoses system will most likely have far higher detection rates for diseases and cancers. However, for such systems to be integrated, large amounts of personal patient information is required, and trust in computation systems needs to be established; some patients may not be comfortable with a machine making decisions for them.

However, if a doctor misdiagnoses a disease, they are held liable to the patient, but who would be responsible for a misdiagnose as a result of faulty software? Such a conundrum may see AI systems be restricted to tools that can aid the decisions made by doctors, and the full power of AI systems may not be properly utilised. Either way, the introduction of AI systems into medical diagnosis will undoubtedly increase the number of detections while lowering the number of false positives.

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By Robin Mitchell

Robin Mitchell is an electronic engineer who has been involved in electronics since the age of 13. After completing a BEng at the University of Warwick, Robin moved into the field of online content creation developing articles, news pieces, and projects aimed at professionals and makers alike. Currently, Robin runs a small electronics business, MitchElectronics, which produces educational kits and resources.

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