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. Methods: A 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 Registration: ClinicalTrials.gov (NCT04605822). [ABSTRACT FROM AUTHOR]