ReviewJID innovations : skin science from molecules to population health2023
Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations.
Review in JID innovations : skin science from molecules to population health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 4 of them syntheses 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
59 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis.The British journal of dermatology · 2025Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Cosmetogenomics unveiled: a systematic review of AI, genomics, and the future of personalized skincare.Frontiers in artificial intelligence · 2025Pooled it
- Analysis of the use of digital technologies in the preliminary diagnosis of dermatological diseases: a systematic review.Archives of dermatological research · 2024Pooled it
- Artificial intelligence in dermatology: A literature review of current evidence and clinical implementation.JID innovations : skin science from molecules to population health · 2026Review
- Automating Diagnosis of Skin Neglected Tropical Diseases via Patient Metadata through Machine Learning Model with Adaptive Balancing and Dual Cross-Validation: Retrospective Diagnostic Accuracy Study.Online journal of public health informatics · 2026Article
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Artificial Intelligence-Based Identification of Common Canine Skin Lesions From Clinical Images.Veterinary dermatology · 2026Article
- Automated Measurement of Depigmentation Extent with a New AI Tool Applied to the Example of Vitiligo.Dermatology and therapy · 2026Article
- Engineering skin microphysiological systems for transdermal drug screening based on strategic model selection and quantitative prediction roadmaps.Materials today. Bio · 2026Review
- Multimodal Deep Learning with Attention-Based Fusion for Skin Cancer Diagnosis.Bioengineering (Basel, Switzerland) · 2026Article
- Harnessing Digital Pathology Tools to Distinguish Hand Eczema From Palmar Psoriasis: A Quantitative Approach.Journal of cutaneous pathology · 2026Article
- SoleFusion-Net: an explainable multimodal deep learning framework for diabetic foot syndrome classification in type II diabetes mellitus.Scientific reports · 2026Article
- Integrating Generative AI Into Patient-Centered Clinical Decision Support: Viewpoint on Research and Practice Considerations.Journal of medical Internet research · 2026Article
- Recent Advances in AI and GenAI for Health Informatics.Healthcare (Basel, Switzerland) · 2026Review
- Predicting outcomes of port-wine stains treated with hematoporphyrin monomethyl ether photodynamic therapy (HMME-PDT): An observational study using multimodal deep learning.JAAD international · 2026Article
- Integrative Machine Learning of Genetic and Lifestyle Factors for Personalized Skin Health.IEEE journal of translational engineering in health and medicine · 2026Article
- When Jack of All Trades Is a Master of None: Comparing the Performance of GPT-4 Omni against Specialised Neural Networks in Identifying Malignant Dermatological Lesions from Smartphone Images and Structured Clinical Data.Dermatology (Basel, Switzerland) · 2026Article
- Automated triage of cancer-suspicious skin lesions with 3D total-body photography.NPJ digital medicine · 2025Article
- Deep Learning Image Processing Models in Dermatopathology.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has recently made great advances in image classification and malignancy prediction in the field of dermatology. However, understanding the applicability of AI in clinical dermatology practice remains challenging owing to the variability of models, image data, database characteristics, and variable outcome metrics. This systematic review aims to provide a comprehensive overview of dermatology literature using convolutional neural networks. Furthermore, the review summarizes the current landscape of image datasets, transfer learning approaches, challenges, and limitations within current AI literature and current regulatory pathways for approval of models as clinical decision support tools.
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Identifiers
What OpenQuestion holds
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.