Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality

Patient-led skin cancer teledermatology without dermoscopy during the COVID-19 pandemic: important lessons for the development of future patient-facing teledermatology and artificial intelligence-assisted -self-diagnosis

MySkinSelfie is a mobile phone application for skin self-monitoring, enabling secure sharing of patient-captured images with healthcare providers. This retrospective study assessed MySkinSelfie's role in remote skin cancer assessment at two centres for urgent (melanoma and squamous cell carcinoma) and nonurgent skin cancer referrals, investigating the feasibility of using patient-captured images without dermoscopy for remote diagnosis. The total number of lesions using MySkinSelfie was 814, with a mean patient age of 63 years. Remote consultations reduced face-to-face appointments by 90% for basal cell carcinoma and by 63% for referrals on a 2-week waiting list. Diagnostic concordance (consultant vs. histological diagnosis) rates of 72% and 83% were observed for basal cell carcinoma (n = 107) and urgent skin cancers (n = 704), respectively. Challenges included image quality, workflow integration and lack of dermoscopy. Higher sensitivities were observed in recent artificial intelligence algorithms employing dermoscopy. While patient-captured images proved useful during the COVID-19 pandemic, further research is needed to explore the feasibility of widespread patient-led dermoscopy to enable direct patient-to-artificial intelligence diagnostic assessment.

FaceMotionPreserve: a generative approach for facial de-identification and medical information preservation

Telemedicine and video-based diagnosis have raised significant concerns regarding the protection of facial privacy. Effective de-identification methods require the preservation of diagnostic information related to normal and pathological facial movements, which play a crucial role in the diagnosis of various movement, neurological, and psychiatric disorders. In this work, we have developed FaceMotionPreserve , a deep generative model-based approach that transforms patients' facial identities while preserving facial dynamics with a novel face dynamic similarity module to enhance facial landmark consistency. We collected test videos from patients with Parkinson's disease recruited via telemedicine for evaluation of model performance and clinical applicability. The performance of FaceMotionPreserve was quantitatively evaluated based on neurologist diagnostic consistency, critical facial behavior fidelity, and correlation of general facial dynamics. In addition, we further validated the robustness and advancements of our model in preserving medical information with clinical examination videos from a different cohort of patients. FaceMotionPreserve is applicable to real-time integration, safeguarding facial privacy while retaining crucial medical information associated with facial movements to address concerns in telemedicine, and facilitating safer and more collaborative medical data sharing.

Review of Teledermatology: Lessons Learned from the COVID-19 Pandemic

Utilization of telemedicine for dermatology has greatly expanded since the start of the COVID-19 pandemic, with over 500 new teledermatology studies published since 2020. An updated review on teledermatology is necessary to incorporate new findings and perspectives, and educate dermatologists on effective utilization. We discuss teledermatology in terms of diagnostic accuracy and clinical outcomes, patient and physician satisfaction, considerations for special patient populations, published practice guidelines, cost effectiveness and efficiency, as well as administrative regulations and policies. Our findings emphasize the need for dermatologist education, prioritization of reliable reimbursement systems, and technological innovations to support the continued development of teledermatology in the post-pandemic era.