Patient and dermatologists' perspectives on augmented intelligence for melanoma screening: A prospective study

Background: Artificial intelligence (AI) shows promising potential to enhance human decision‐making as synergistic augmented intelligence (AuI), but requires critical evaluation for skin cancer screening in a real‐world setting. Objectives: To investigate the perspectives of patients and dermatologists after skin cancer screening by human, artificial and augmented intelligence.MethodsA prospective comparative cohort study conducted at the University Hospital Basel included 205 patients (at high‐risk of developing melanoma, with resected or advanced disease) and 8 dermatologists. Patients underwent skin cancer screening by a dermatologist with subsequent 2D and 3D total‐body photography (TBP). Any suspicious and all melanocytic skin lesions ≥3 mm were imaged with digital dermoscopes and classified by corresponding convolutional neural networks (CNNs). Excisions were performed based on dermatologist's melanoma suspicion, study‐defined elevated CNN risk‐scores and/or melanoma suspicion by AuI. Subsequently, all patients and dermatologists were surveyed about their experience using questionnaires, including quantification of patient's safety sense following different examinations (subjective safety score (SSS): 0–10). Results: Most patients believed AI could improve diagnostic performance (95.5%, n = 192/201). In total, 83.4% preferred AuI‐based skin cancer screening compared to examination by AI or dermatologist alone (3D‐TBP: 61.3%; 2D‐TBP: 22.1%, n = 199). Regarding SSS, AuI induced a significantly higher feeling of safety than AI (mean‐SSS (mSSS): 9.5 vs. 7.7, p < 0.0001) or dermatologist screening alone (mSSS: 9.5 vs. 9.1, p = 0.001). Most dermatologists expressed high trust in AI examination results (3D‐TBP: 90.2%; 2D‐TBP: 96.1%, n = 205). In 68.3% of the examinations, dermatologists felt that diagnostic accuracy improved through additional AI‐assessment (n = 140/205). Especially beginners (<2 years' dermoscopic experience; 61.8%, n = 94/152) felt AI facilitated their clinical work compared to experts (>5 years' dermoscopic experience; 20.9%, n = 9/43). Contrarily, in divergent risk assessments, only 1.5% of dermatologists trusted a benign CNN‐classification more than personal malignancy suspicion (n = 3/205). Conclusions: While patients already prefer AuI with 3D‐TBP for melanoma recognition, dermatologists continue to rely largely on their own decision‐making despite high confidence in AI‐results. Trial RegistrationClinicalTrials.gov (NCT04605822).