ReviewCurrent oncology reports2023
Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods.
Review in Current oncology reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Chronic Infections as Catalysts for Melanoma Aggressiveness: Insights into Tumour Microenvironment Modulation.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.BMC cancer · 2025Pooled it
- Artificial Intelligence-Based Diagnosis of Kaposi Sarcoma Using Digital Photographs in Dark-Skinned Patients in Uganda.JCO global oncology · 2026Article
- Emerging Artificial Intelligence Models for Estimating Breslow Thickness from Dermoscopic Images.Biomedicines · 2026Review
- Machine learning prediction of overall survival in patients with primary gastrointestinal melanoma.Translational cancer research · 2025Article
- From Slide to Insight: The Emerging Alliance of Digital Pathology and AI in Melanoma Diagnostics.Cancers · 2025Review
- Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery.Clinics and practice · 2025Review
- Artificial Intelligence Algorithm Predicts Response to Immune Checkpoint Inhibitors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Diagnostic reproducibility of the 2018 Classification of Gingival Recessions: Comparing photographic and in-person diagnoses.Journal of periodontology · 2025Article
- Artificial intelligence in melanoma diagnosis: ethical considerations and clinical implementation.Proceedings (Baylor University. Medical Center) · 2025Article
- Article
- The Role of Digital Dermoscopy and Follow-Up in the Detection of Amelanotic/Hypomelanotic Melanoma in a Group of High-Risk Patients-Is It Useful?Life (Basel, Switzerland) · 2024Review
- Advances in Melanoma: From Genetic Insights to Therapeutic Innovations.Biomedicines · 2024Review
- Deep Learning Techniques for the Dermoscopic Differential Diagnosis of Benign/Malignant Melanocytic Skin Lesions: From the Past to the Present.Bioengineering (Basel, Switzerland) · 2024Review
- AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework.Journal of Cancer · 2024Article
- An update on methods for detection of prognostic and predictive biomarkers in melanoma.Frontiers in cell and developmental biology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
purpose of reviewThe purpose was to summarize the current role and state of artificial intelligence and machine learning in the diagnosis and management of melanoma. RECENT
findingsDeep learning algorithms can identify melanoma from clinical, dermoscopic, and whole slide pathology images with increasing accuracy. Efforts to provide more granular annotation to datasets and to identify new predictors are ongoing. There have been many incremental advances in both melanoma diagnostics and prognostic tools using artificial intelligence and machine learning. Higher quality input data will further improve these models' capabilities.
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.