ReviewCancers2023
AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 125 citations in OpenAlex.
- Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis.Frontiers in oncology · 2023Pooled it
- Attention guided fair artificial intelligence modeling for skin cancer diagnosis.NPJ digital medicine · 2026Article
- Accuracy of AI Tools in the Diagnosis of Benign, Potentially Malignant and Malignant Oral Lesions: A Pilot Study.Journal of clinical medicine · 2026Article
- A brain-inspired computational framework for image-based risk assessment.Scientific reports · 2026Article
- DermaScanAI an explainable hybrid deep learning framework for automated skin lesion classification using dual attention and metadata fusion.Scientific reports · 2026Article
- Roadmap for Representative Artificial Intelligence Models for Thyroid Cancer.The Laryngoscope · 2026Review
- The era of "Infectious Diseases+" has arrived: multi-disciplinary integration in pediatric infectious disease prevention and control.Frontiers in pediatrics · 2026Review
- Artificial intelligence-based miRNA analysis for precision oncology: diagnostic and prognostic insights.Frontiers in molecular biosciences · 2026Review
- Artificial Intelligence and New Technologies in Melanoma Diagnosis: A Narrative Review.Cancers · 2025Review
- Effective skin cancer classification by modified and optimized inception-ResNet-V2 model.Scientific reports · 2025Article
- Multi-stage knowledge distillation with layer fusion-based deep learning approach for skin cancer classification.Scientific reports · 2025Article
- Transforming Skin Quality Evaluation With AI: From Subjective Grading to Data-Driven Precision.Journal of cosmetic dermatology · 2025Review
- An Ingeniously Designed Skin Lesion Classification Model Across Clinical and Dermatoscopic Datasets.Diagnostics (Basel, Switzerland) · 2025Article
- New Release of User-Captured Images from the Oregon Health & Science University Melanoma MoleMapper Project.Scientific data · 2025Article
- A Novel Radiology-Adapted Logistic Model for Non-Invasive Risk Stratification of Pigmented Superficial Skin Lesions: A Methodological Pilot Study.Diagnostics (Basel, Switzerland) · 2025Article
- The Use of Artificial Intelligence for Skin Cancer Detection in Asia-A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- SADASNet: A Selective and Adaptive Deep Architecture Search Network with Hyperparameter Optimization for Robust Skin Cancer Classification.Diagnostics (Basel, Switzerland) · 2025Article
- A robust deep learning framework for multiclass skin cancer classification.Scientific reports · 2025Article
- The risks of artificial intelligence: A narrative review and ethical reflection from an Oral Medicine group.Oral diseases · 2025Review
- Implications of An Evolving Regulatory Landscape on the Development of AI and ML in Medicine.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
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 at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin cancer continues to remain one of the major healthcare issues across the globe. If diagnosed early, skin cancer can be treated successfully. While early diagnosis is paramount for an effective cure for cancer, the current process requires the involvement of skin cancer specialists, which makes it an expensive procedure and not easily available and affordable in developing countries. This dearth of skin cancer specialists has given rise to the need to develop automated diagnosis systems. In this context, Artificial Intelligence (AI)-based methods have been proposed. These systems can assist in the early detection of skin cancer and can consequently lower its morbidity, and, in turn, alleviate the mortality rate associated with it. Machine learning and deep learning are branches of AI that deal with statistical modeling and inference, which progressively learn from data fed into them to predict desired objectives and characteristics. This survey focuses on Machine Learning and Deep Learning techniques deployed in the field of skin cancer diagnosis, while maintaining a balance between both techniques. A comparison is made to widely used datasets and prevalent review papers, discussing automated skin cancer diagnosis. The study also discusses the insights and lessons yielded by the prior works. The survey culminates with future direction and scope, which will subsequently help in addressing the challenges faced within automated skin cancer diagnosis.
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.