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Benefits and Limitations of Teledermatology in German Correctional Facilities: Cross-Sectional Analysis

Benefits and Limitations of Teledermatology in German Correctional Facilities: Cross-Sectional Analysis

information of skin disease; patients’ demographic data (age and gender); preliminary diagnosis and questions from the in-house medical team of the prison regarding the case (eg, special issues such as occupational or infectious matters, quarantine, or isolation); documentation of the consultation, including details of onset and clinical appearance of the skin disease, and number and quality of photos sent with the request; and further anamnestic information retrieved from the virtual presentation of the case and dermatological

Brigitte Stephan, Kathrin Gehrdau, Christina Sorbe, Matthias Augustin, Martin Scherer, Anne Kis

JMIR Med Inform 2025;13:e58712

The Comparative Sufficiency of ChatGPT, Google Bard, and Bing AI in Answering Diagnosis, Treatment, and Prognosis Questions About Common Dermatological Diagnoses

The Comparative Sufficiency of ChatGPT, Google Bard, and Bing AI in Answering Diagnosis, Treatment, and Prognosis Questions About Common Dermatological Diagnoses

This study compared the clinical utility of the unpaid versions of Chat GPT 3.5, Google Bard, and Bing AI in generating patient-facing responses to questions about 5 common dermatological diagnoses (atopic dermatitis, acne vulgaris, actinic keratosis, cyst, and rosacea) [3]. For each condition, 2 diagnosis, 2 treatment, and 1 prognosis questions were devised.

Courtney A Chau, Hao Feng, Gabriela Cobos, Joyce Park

JMIR Dermatol 2025;8:e60827

Dermatology in Student-Run Clinics in the United States: Scoping Review

Dermatology in Student-Run Clinics in the United States: Scoping Review

In October 2022, the American Dermatological Association proposed measures to address the downstream inequities for patients with skin disease arising from unequal access, including opportunities for trainees in underserved areas [49]. SRCs, with a 2014 nationwide census of 140,000 patients and support among 75% of accredited medical schools, may help close the gap in patient access [2].

Samir Kamat, Aneesh Agarwal, Leore Lavin, Hannah Verma, Lily Martin, Jules B Lipoff

JMIR Dermatol 2024;7:e59368

Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis

Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis

Traditionally, dermatological diagnoses are established by integrating the patient’s medical history, clinical examination, and, in some instances, dermoscopic and histopathologic analyses [1]. As it is predominantly a morphological feature–dependent specialty, dermatology is a field best suited for incorporating artificial intelligence (AI) image detection and recognition capabilities for aided diagnosis [2-5].

Shiva Shankar Marri, Warood Albadri, Mohammed Salman Hyder, Ajit B Janagond, Arun C Inamadar

JMIR Dermatol 2024;7:e48811

Training Family Medicine Residents in Dermoscopy Using an e-Learning Course: Pilot Interventional Study

Training Family Medicine Residents in Dermoscopy Using an e-Learning Course: Pilot Interventional Study

Participants were asked to complete a prestudy survey: sex, year of family residency program, dermatology clinical rotation experience, locums in family medicine, dermatology training during medical school or family medicine residency, quality of dermatology training during family medicine residency, interest in dermatology, frequency of dermatological consultations, self-evaluation of dermatological skills and ability to use a dermatoscope.

Pauline Friche, Lionel Moulis, Aurélie Du Thanh, Olivier Dereure, Claire Duflos, Francois Carbonnel

JMIR Form Res 2024;8:e56005