ReviewDermatology and therapy2020
Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations.
Review in Dermatology and therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07705386 (Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis), which is not on this map. Cited by 78 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.
Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis: A Training and Validation Study
Who cites it
78 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Early Detection of Keratoconus: Diagnostic Advances and Their Impact on Visual Outcomes: A Systematic Review.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Diagnosis melanoma with artificial intelligence systems: A meta-analysis study and systematic review.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Pooled it
- Pooled it
- Mobile Health Strategies to Tackle Skin Neglected Tropical Diseases With Recommendations From Innovative Experiences: Systematic Review.JMIR mHealth and uHealth · 2020Pooled it
- Zero-shot performance of selected large language and multimodal models on the 2023 Brazilian Portuguese medical residency exam.Scientific reports · 2026Article
- Utility of 3D Imaging in the Objective Evaluation of Glabellar Lines Following Botulinum Toxin Treatment.Diagnostics (Basel, Switzerland) · 2026Article
- Insights into the Impact of Artificial Intelligence on Psoriasis Treatment Strategies: A Mini Review.Indian dermatology online journal · 2025Article
- Skin Lesion Image Classification With Tree-Based Ensembles: Benchmarking Random Forest and Gradient Boosting.Cureus · 2025Article
- Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review.Diagnostics (Basel, Switzerland) · 2025Review
- Mobile applications for skin cancer detection are vulnerable to physical camera-based adversarial attacks.Scientific reports · 2025Article
- Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label clinical document classification.Scientific reports · 2025Article
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.Scientific reports · 2025Article
- Next-generation approach to skin disorder prediction employing hybrid deep transfer learning.Frontiers in big data · 2025Article
- AI Grading of Lateral Canthal Lines: Novel Models for Unseen Synthetic Image Generation and Data Augmentation.Clinical, cosmetic and investigational dermatology · 2025Article
- A Comprehensive Review of Predictive Precision in Scar Medicine: From Molecular Predictors to Machine Learning Models.Clinical, cosmetic and investigational dermatology · 2025Review
- Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis.F1000Research · 2025Article
- AI in Aesthetic/Cosmetic Dermatology: Current and Future.Journal of cosmetic dermatology · 2025Review
- AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation.Cosmetics · 2024Article
- AI in Psoriatic Disease: Scoping Review.JMIR dermatology · 2024Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.
Indexed as
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.