ReviewFuture science OA2022
An overview of artificial intelligence in oncology.
Review in Future science OA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2025Pooled it
- Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.Frontiers in oncology · 2024Pooled it
- Machine learning-based prediction of recurrence after curative resection in non-small cell lung cancer.Scientific reports · 2026Article
- Health economics evaluation of artificial intelligence in the field of oncology: a scoping review.Health economics review · 2026Review
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Artificial intelligence applications in oxaliplatin-based chemotherapy for colon cancer: advancing prognosis, toxicity prediction, and dose personalization.Frontiers in pharmacology · 2026Review
- Explainable deep learning approaches and clinical insights for cancer biomarker identification.Frontiers in oncology · 2026Review
- Implementation of AI in oncology: a systematic review of educational and clinical integration in global contexts.Frontiers in digital health · 2026Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Generative artificial intelligence, large language models and ChatGPT in musculoskeletal Oncology: Current applications and future potential.Journal of clinical orthopaedics and trauma · 2025Article
- How to Make AI Ready for Rheumatology: Challenges and Perspectives.Mediterranean journal of rheumatology · 2025Article
- Review
- Article
- The dark matter in cancer immunology: beyond the visible- unveiling multiomics pathways to breakthrough therapies.Journal of translational medicine · 2025Review
- Balancing between A and I.Acta oncologica (Stockholm, Sweden) · 2025Article
- AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis.Discover oncology · 2025Article
- Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.Cancer letters · 2025Review
- Predicting hepatocellular carcinoma survival with artificial intelligence.Scientific reports · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- The tumor immune microenvironment: implications for cancer immunotherapy, treatment strategies, and monitoring approaches.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes - prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.
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.