SynthesisFrontiers in medicine2023
Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review.
Synthesis in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Artificial Intelligence for Diagnosing Normal Anatomical Variants and Pathological Oral Mucosal Lesions: A Prospective Observational Study.Medicina (Kaunas, Lithuania) · 2026Observational
- MEL-IA: An Interoperable AI System for Multimodal Skin Lesion Classification in Hospital Settings.Journal of medical systems · 2026Article
- How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review.Research square · 2026Article
- Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives.Bioengineering (Basel, Switzerland) · 2025Review
- The integration of dermatology experts into primary care to assess and treat patients with skin lesions is cost-effective: A quasi-experimental study.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Article
- Poloxamer-based drug delivery systems: Frontiers for treatment of solid tumors.Materials today. Bio · 2025Review
- The Use of Artificial Intelligence for Skin Cancer Detection in Asia-A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- CAD-Skin: A Hybrid Convolutional Neural Network-Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- A robust deep learning framework for multiclass skin cancer classification.Scientific reports · 2025Article
- Review
- Using the power of artificial intelligence to improve the diagnosis and management of nonmelanoma skin cancer.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Review
- Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP.Journal of imaging informatics in medicine · 2024Article
- Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study.Bioengineering (Basel, Switzerland) · 2024Article
- State-of-the-Art in Skin Fluorescent Photography for Cosmetic and Skincare Research: From Molecular Spectra to AI Image Analysis.Life (Basel, Switzerland) · 2024Review
- Artificial Intelligence Smartphone Application for Detection of Simulated Skin Changes: An In Vivo Pilot Study.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Performance evaluation of E-VGG19 model: Enhancing real-time skin cancer detection and classification.Heliyon · 2024Article
- An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks.Diagnostics (Basel, Switzerland) · 2024Article
- Joint Expedition: Exploring Clinical Medical Imaging and Artificial Intelligence as a Team Integration.Diagnostics (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of cancer can result in better prognoses and less invasive treatments for patients. With advances in Artificial Intelligence (AI), tools have emerged that can facilitate diagnosis and classify dermatological images, complementing traditional clinical assessments and being applicable where there is a shortage of specialists. Its adoption requires analysis of efficacy, safety, and ethical considerations, as well as considering the genetic and ethnic diversity of patients. Objective: The systematic review aims to examine research on the detection, classification, and assessment of skin cancer images in clinical settings. Methods: We conducted a systematic literature search on PubMed, Scopus, Embase, and Web of Science, encompassing studies published until April 4th, 2023. Study selection, data extraction, and critical appraisal were carried out by two independent reviewers. Results were subsequently presented through a narrative synthesis. Results: Through the search, 760 studies were identified in four databases, from which only 18 studies were selected, focusing on developing, implementing, and validating systems to detect, diagnose, and classify skin cancer in clinical settings. This review covers descriptive analysis, data scenarios, data processing and techniques, study results and perspectives, and physician diversity, accessibility, and participation. Conclusion: The application of artificial intelligence in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety.
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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.