ReviewNPJ digital medicine2023
Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 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
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- 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
- Analysis of the use of digital technologies in the preliminary diagnosis of dermatological diseases: a systematic review.Archives of dermatological research · 2024Pooled it
- A phase II, non-comparative randomised trial of two treatments involving liposomal amphotericin B and miltefosine for post-kala-azar dermal leishmaniasis in India and Bangladesh.PLoS neglected tropical diseases · 2024Trial
- Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026Review
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Artificial intelligence (AI)-assisted diagnosis of skin diseases: From image classification to dermatology-specific multimodal clinical reasoning.Global health & medicine · 2026Review
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Artificial Intelligence Meets Nail Diagnostics: Emerging Image-Based Sensing Platforms for Non-Invasive Disease Detection.Bioengineering (Basel, Switzerland) · 2026Review
- Publicly Available Large Language Models for Trichoscopy: A Head-to-Head Comparison with Dermatologists.Diagnostics (Basel, Switzerland) · 2026Article
- Objective assessment of rosacea erythema severity: a multimodal artificial intelligence framework integrating VISIA® imaging and image-derived tabular features.Frontiers in medicine · 2026Article
- Improving Clinical Reliability of Diffusion-Based Dermatology Image Synthesis Through Synthetic Note Alignment and Post-Generation Filtering.IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems · 2026Article
- The cutaneous mirror: leveraging drug-induced skin phenotypes as early visual risk signals for systemic toxicity, a comprehensive review.Frontiers in drug safety and regulation · 2026Review
- Transforming Skin Quality Evaluation With AI: From Subjective Grading to Data-Driven Precision.Journal of cosmetic dermatology · 2025Review
- Deep Ensemble Learning for Multiclass Skin Lesion Classification.Bioengineering (Basel, Switzerland) · 2025Article
- [The analysis of invention patents in the field of artificial intelligent medical devices].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Artificial Intelligence in the Assessment and Grading of Acne Vulgaris: A Systematic Review.Journal of personalized medicine · 2025Review
- Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives.Medical sciences (Basel, Switzerland) · 2025Review
- Unlocking Responsive and Unresponsive Signatures: A Transfer Learning Approach for Automated Classification in Cutaneous Leishmaniasis Lesions.Transboundary and emerging diseases · 2025Article
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
- HArmonyCa™ hybrid filler to restore connective tissue: An Italian real-life retrospective study.Journal of cosmetic dermatology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 6 institutions in 1 country.
Funding
Abstract
Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images via neural networks to make predictions. A focus of deep learning research is skin lesion triage to detect cancer, but this may not translate to the wider scope of >2000 other skin diseases. We searched for studies applying deep learning to skin images, excluding benign/malignant lesions (1/1/2000-23/6/2022, PROSPERO CRD42022309935). The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86-98; n = 11), rosacea (94%, 90-97; n = 4), eczema (93%, 90-99; n = 9) and psoriasis (89%, 78-92; n = 8) was high. Accuracy for grading severity was highest for psoriasis (range 93-100%, n = 2), eczema (88%, n = 1), and acne (67-86%, n = 4). However, 59 (92%) studies had high risk-of-bias judgements and 62 (97%) had high-level applicability concerns. Only 12 (19%) reported participant ethnicity/skin type. Twenty-four (37.5%) evaluated the algorithm in an independent dataset, clinical setting or prospectively. These data indicate potential of deep learning image analysis in diagnosing and monitoring common skin diseases. Current research has important methodological/reporting limitations. Real-world, prospectively-acquired image datasets with external validation/testing will advance deep learning beyond the current experimental phase towards clinically-useful tools to mitigate rising health and cost impacts of skin disease.
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.