ArticleDiagnostics (Basel, Switzerland)2024
Can Artificial Intelligence "Hold" a Dermoscope?-The Evaluation of an Artificial Intelligence Chatbot to Translate the Dermoscopic Language.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Perspectives on Artificial Intelligence in Dermatology: An International Cross-Sectional Study.Medicina (Kaunas, Lithuania) · 2026Article
- Dermatology "AI Babylon": Cross-Language Evaluation of AI-Crafted Dermatology Descriptions.Medicina (Kaunas, Lithuania) · 2026Article
- Concordance in Basal Cell Carcinoma Diagnosis. Building a Proper Standard Reference to Train Artificial Intelligence Tools.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2025Article
- Article
- Deep learning in assisting dermatologists in classifying basal cell carcinoma from seborrheic keratosis.Frontiers in oncology · 2025Article
- Exploring the Role of Large Language Models in Melanoma: A Systematic Review.Journal of clinical medicine · 2024Review
- Exploring Pediatric Dermatology in Skin of Color: Focus on Dermoscopy.Life (Basel, Switzerland) · 2024Review
- Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study.JMIR medical informatics · 2024Article
- Review
- Clinical and Dermoscopic Patterns of Basal Cell Carcinoma and Its Mimickers in Skin of Color: A Practical Summary.Medicina (Kaunas, Lithuania) · 2024Review
- Laser-Induced Koebner-Related Skin Reactions: A Clinical Overview.Medicina (Kaunas, Lithuania) · 2024Review
- The Role of Artificial Intelligence in Early Diagnosis and Molecular Classification of Head and Neck Skin Cancers: A Multidisciplinary Approach.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This survey represents the first endeavor to assess the clarity of the dermoscopic language by a chatbot, unveiling insights into the interplay between dermatologists and AI systems within the complexity of the dermoscopic language. Given the complex, descriptive, and metaphorical aspects of the dermoscopic language, subjective interpretations often emerge. The survey evaluated the completeness and diagnostic efficacy of chatbot-generated reports, focusing on their role in facilitating accurate diagnoses and educational opportunities for novice dermatologists. A total of 30 participants were presented with hypothetical dermoscopic descriptions of skin lesions, including dermoscopic descriptions of skin cancers such as BCC, SCC, and melanoma, skin cancer mimickers such as actinic and seborrheic keratosis, dermatofibroma, and atypical nevus, and inflammatory dermatosis such as psoriasis and alopecia areata. Each description was accompanied by specific clinical information, and the participants were tasked with assessing the differential diagnosis list generated by the AI chatbot in its initial response. In each scenario, the chatbot generated an extensive list of potential differential diagnoses, exhibiting lower performance in cases of SCC and inflammatory dermatoses, albeit without statistical significance, suggesting that the participants were equally satisfied with the responses provided. Scores decreased notably when practical descriptions of dermoscopic signs were provided. Answers to BCC scenario scores in the diagnosis category (2.9 ± 0.4) were higher than those with SCC (2.6 ± 0.66,
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