ReviewCells2022
Artificial Intelligence and Advanced Melanoma: Treatment Management Implications.
Review in Cells, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis.Frontiers in immunology · 2024Pooled it
- Multidisciplinary tumor board decisions and artificial intelligence-generated recommendations in general surgery: a retrospective observational study.Updates in surgery · 2026Article
- Immunotherapy in Melanoma: A Dynamic Frontier in Cancer Treatment : Author List.Current treatment options in oncology · 2026Review
- Circ_0000972 Inhibits Hepatocellular Carcinoma Cell Stemness by Targeting miR-96-5p/PFN1.Biochemical genetics · 2025Article
- Artificial Intelligence-Driven Strategies for Targeted Delivery and Enhanced Stability of RNA-Based Lipid Nanoparticle Cancer Vaccines.Pharmaceutics · 2025Review
- Novel Method for the Synthesis of Hydroxycobalamin[International journal of molecular sciences · 2025Article
- Quality of life under treatment with the immune checkpoint inhibitors ipilimumab and nivolumab in melanoma patients. Real-world data from a prospective observational study at the Skin Cancer Center Kiel.Journal of cancer research and clinical oncology · 2024Observational
- Advancements and Challenges in Personalized Therapy forJournal of clinical medicine · 2024Review
- Review
- Development of Personalized Strategies for Precisely Battling Malignant Melanoma.International journal of molecular sciences · 2024Review
- Impact of artificial intelligence and digital technology-based diagnostic tools for communicable and non-communicable diseases in Africa.African journal of laboratory medicine · 2024Review
- More than Just Skin-Deep: A Review of Imaging's Role in Guiding CAR T-Cell Therapy for Advanced Melanoma.Diagnostics (Basel, Switzerland) · 2023Review
- 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by "intelligent" machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments.
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.