ArticleDermatology and therapy2022
Artificial Intelligence in Dermatology: Challenges and Perspectives.
Article in Dermatology and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026Review
- Physicians' perspectives on artificial intelligence in electrocardiography in clinical practice: a qualitative study.BMC medical ethics · 2026Article
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Artificial Intelligence in Inherited Epidermolysis Bullosa: Current Evidence, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Artifact-Aware Fungal Detection in Dermatophytosis: A Transformer-Based Approach for KOH Microscopy.Bioengineering (Basel, Switzerland) · 2026Article
- Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future Directions-A Narrative Review.Journal of clinical medicine · 2026Review
- Patient Perspectives on the Use of Artificial Intelligence in Malignant Melanoma Diagnosis.Dermatology practical & conceptual · 2026Article
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- DermIDS: Dermatology imaging data structure for scalable and interoperable AI systems.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- From Optical to Molecular Imaging on Human Skin: A Review.Chemical & biomedical imaging · 2026Review
- Comparison of artificial intelligence and rheumatologists in nailfold capillaroscopic evaluation.Revista da Associacao Medica Brasileira (1992) · 2026Article
- Multi-task AI models in dermatology: Overcoming critical clinical translation challenges for enhanced skin lesion diagnosis.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Article
- Review
- The Current State and Future Prospects for Artificial Intelligence in Dermatology.Dermatologic clinics · 2025Review
- Patient Perceptions of Artificial Intelligence and Telemedicine in Dermatology: Narrative Review.JMIR dermatology · 2025Review
- A dual-stream deep learning framework for skin cancer classification using histopathological-inherited and vision-based feature extraction.Scientific reports · 2025Article
- Transforming Skin Quality Evaluation With AI: From Subjective Grading to Data-Driven Precision.Journal of cosmetic dermatology · 2025Review
- AI in Neurology: Everything, Everywhere, all at Once PART 2: Speech, Sentience, Scruples, and Service.Annals of neurology · 2025Review
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians' diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.