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Interpreting skin conditions with AI

Illustration of a woman in a red shirt sitting at a computer with a bubble that says AI next to it.

Globally, skin conditions affect about 2 billion people. Diagnosing and treating these skin conditions is a complex process that involves specialized training. Due to a shortage of dermatologists and long wait times to see one, most patients first seek care from non-specialists.

Typically, a clinician examines the affected areas and the patient's medical history before arriving at a list of potential diagnoses, sometimes known as a “differential diagnosis”. They then use this information to decide on the next step such as a test, observation or treatment. 

To see if artificial intelligence (AI) could improve the process, we conducted a randomized retrospective study that was published today in JAMA Network Open. The study examined if a research tool we developed could help non-specialists clinicians — such as primary care physicians and nurse practitioners — more accurately interpret skin conditions. The tool uses Google’s deep learning system (that you can learn more about in Nature Medicine) to interpret de-identified images and medical history and provide a list of matching skin conditions.

In the study, 40 non-specialist clinicians interpreted de-identified images of patients’ skin conditions from a telemedicine dermatology service, identified the condition, and made recommendations such as biopsy or referral to a dermatologist. Each clinician examined over 1,000 cases — clinicians used the AI-powered tool for half of the cases and didn’t have access to the assistive AI tool in the other half.

Main takeaways of study: AI-assisted clinicians were better able to interpret skin conditions.

Main takeaways of study: AI-assisted clinicians were better able to interpret skin conditions and more often arrive at the same diagnosis as dermatologists. 

Clinicians with AI assistance were significantly more likely to arrive at the same diagnosis as dermatologists, compared to clinicians reviewing cases without AI assistance. The chances of identifying the correct top condition improved by more than 20% on a relative basis, though the degree of improvement varied by the individual.

We believe AI must be designed to improve care for everyone. In the study, clinicians' performance was consistently higher with AI assistance across a broad range of skin types — from pale skin that does not tan to brown skin that rarely burns. In addition to improving diagnostic ability, the AI assistance helped clinicians in the study feel more confident about their assessment and reassuringly did not increase their likelihood to recommend biopsies or referrals to dermatologists as the next appropriate step.

These research study results are promising and show that AI-based assistive tools could help non-specialist clinicians assess skin conditions. AI has shown great potential to improve health care outcomes; the next challenge is to demonstrate how AI can be applied in the real world. At Google Health, we’re committed to working with clinicians, patients and others to harness advances in research and ultimately bring about better and more accessible care. 

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