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.
