This multicenter clinical trial compared AI-powered mobile phone diagnostic tools with human medical experts in diagnosing pigmented skin cancer. The study found that the AI system was equivalent to specialists in accuracy and superior to novices, showcasing mobile technology's potential for accessible and accurate skin cancer diagnosis.
By Dr. Priyom Bose, Ph.D.Sep 28 2023Reviewed by Benedette Cuffari, M.Sc. A recent Lancet Digital Health study assesses whether mobile phone-powered artificial intelligence could support the diagnosis and management of pigmented skin cancer.
The accuracy of AI-based diagnostic systems depends on multiple factors. In most cases, diagnosis by human experts and AI-assisted diagnostic systems have been compared in simulated settings, which might not truly reflect real clinical settings. However, these limitations do not apply to certain clinical situations where direct physical connection with a patient is not required, like radiology.
About the study The current multicentered, diagnostic, and prospective clinical trial compared the decisions of the AI diagnostic algorithm of the ISIC 2018 Challenge with medical experts’ diagnoses in clinical settings. Subsequently, a new algorithm was developed based on data from current healthcare professionals.
Adult patients between the ages of 18 and 99 years were recruited. Participants selected for this clinical trial underwent routine excision or biopsy of suspicious pigmented skin lesions bigger than three millimeters in the longest diameter. These patients had a modified Fitzpatrick I-III skin type.
Reference tests for lesions were associated with histopathological examination. The lesions that remained unchanged on total-body photographs were considered benign, while the changed lesions underwent digital dermoscopy monitoring. The newly developed seven-class mobile phone-powered AI, equipped with a simple dermoscopy phone attachment, provided data for creating the algorithm, which aided in the superior diagnosis of skin cancer compared to novices. For diagnostics, the seven-class AI algorithm was equivalent to the specialists’ decision, while it remained superior to the novices’ diagnosis.
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