Artificial intelligence-based smartphone application for skin cancer detection: a prospective diagnostic accuracy study

Clinical Snapshot

Key Findings

• P Value: P < 0.001 for specificity improvement with teledermatology

• Effect Size: CNN sensitivity 82.5%, specificity 76.8%; Combined CNN+teledermatology sensitivity 75.3%, specificity 86.8%

• Primary Outcome: Diagnostic accuracy for skin cancer detection

• Nnt Or Sensitivity: Sensitivity 82.5% (CNN alone), 75.3% (CNN+teledermatology); Specificity 76.8% (CNN alone), 86.8% (CNN+teledermatology)

• Confidence Interval: Not reported in abstract

Clinical Application

Limited by high technical failure rates (16.6% under optimal conditions, 71% with user capture) and smartphone model variability Could support GP triage and teledermatology initiatives, particularly relevant for rural/remote access. TGA approval would be required for therapeutic claims. Cost-effectiveness assessment needed for PBS consideration. Patients with skin lesions of concern suitable for smartphone photography in dermatology triage settings

Abstract

BACKGROUND: Smartphone applications ('apps') that use artificial intelligence (AI) for skin cancer diagnosis may help with early detection. However, prospective studies assessing real-world performance are scarce. OBJECTIVES: To evaluate in an independent prospective study the diagnostic accuracy of a widely used skin cancer detection app and the effect of photographic conditions in a user-representative cohort of patients consulting for lesions of concern. METHODS: Between 1 February 2021 and 30 June 2023, participants presenting at an early-access consultation with a lesion of concern were consecutively enrolled. Following dermatological assessment, each lesion was photographed using the app. The app's convolutional neural network (CNN) generates a binary risk output and advice. A teledermatology review was performed in a subset of cases. The diagnostic accuracy of the CNN combined with teledermatology review was compared with final clinical or histopathological diagnosis. Performance was tested under different photographic conditions (angle, lighting, user) and with different smartphone models. The trial was registered with ClinicalTrials.gov (NCT05246163). RESULTS: A total of 1458 participants with 1904 lesions of concern were included. Of these, 185 (9.7%) were skin cancers, with 32 melanomas. Image capture was unsuccessful in 16.6% of lesions (n = 317/1904), despite optimal conditions. For successfully captured lesions, the CNN achieved a sensitivity of 82.5% and specificity of 76.8% for skin cancer detection. In-app teledermatology review was available for 65.7% (n = 1042/1587) of images. Combined CNN and teledermatology review resulted in increased specificity (86.8%; P < 0.001), with a sensitivity of 75.3%. Overall sensitivity in melanocytic lesions was lower than in nonmelanocytic lesions (71.0% vs. 76.3%; P < 0.001), whereas specificity was higher (90.0% vs. 84.9%; P < 0.001). A substudy demonstrated low image-capture success in user hands (28.9%) and variation in diagnostic performance between smartphone models. CONCLUSIONS: This independent, prospective study evaluated diagnostic performance of a widely used skin cancer detection app in a user-representative cohort. The findings highlight the importance of independent clinical validation of AI-based healthcare tools in real-world settings. Skin cancer is one of the most common cancers that affects people worldwide. Finding cancer early is important, especially for melanoma, a serious form of skin cancer. There is potential for smartphone apps to use AI to help people identify skin spots that may need medical attention. In this study, we evaluated a widely used app called SkinVision. People presenting with a worrisome spot at a dermatology appointment were invited to take part in this study. Altogether, 1458 people were included. The skin area of concern was assessed with the app. Using AI, the app produced a risk score. It also gave a final score. The final score was the AI risk score that may be combined with dermatology advice delivered remotely (‘teledermatology’). We compared the app scores with the doctor’s final diagnosis. We also studied the impact of things such as photo angle, lighting, type of smartphone and the person who took the photo. We found that over 16% of worrisome skin areas could not be captured by the app under ideal conditions. If participants were asked to take the photo themselves, this increased to over 70%. The app correctly identified skin cancers in 83% of cases but gave false alarms in 23%. The in-app teledermatology review reduced the number of false alarms, but this increased the number of missed skin cancers. Finally, we found important differences between different smartphones. Our findings highlight the importance of trialling apps like this with real patients in real-life setting before they are fully introduced to the public.

References

1. 1.Kips, J., Papeleu, J., Shen, A., Mylle, S., Genouw, E., Hoorens, I., Verhaeghe, E., & Brochez, L. (2026). Artificial intelligence-based smartphone application for skin cancer detection: a prospective diagnostic accuracy study. British Journal of Dermatology. https://doi.org/10.1093/bjd/ljag057