ReviewMolecular cancer2025
Artificial intelligence in cancer: applications, challenges, and future perspectives.
Review in Molecular cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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
21 citing papers in PubMed.
- Article
- A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.Cardio-oncology (London, England) · 2026Review
- Article
- Educational Frameworks for Diagnostic Decision-Making in AI-Enhanced Head and Neck Pathology.Head and neck pathology · 2026Review
- Review
- The mycobiome, virome and archaeome in gastrointestinal cancers: molecular pathogenesis and therapeutic intervention.Molecular cancer · 2026Review
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Liquid Biopsy in Colorectal Cancer: Future Perspectives Through the Lens of Artificial Intelligence-A Comprehensive Review of Novel Literature.International journal of molecular sciences · 2026Review
- Article
- Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Emerging evidence on micro- and nanoplastics carcinogenicity: mechanisms, models, and signaling networks.Molecular cancer · 2026Review
- Review
- Advances in Breast Cancer Research: Immunological, Pathological, and Pharmacological Perspectives for Improving Patient Outcomes.International journal of molecular sciences · 2026Review
- Mapping the integration of artificial intelligence in knee replacement surgery: a data-driven bibliometric analysis with emphasis on robotic innovation.Journal of robotic surgery · 2026Article
- Machine Learning in Biomarker-Driven Precision Oncology: Automated Immunohistochemistry Scoring and Emerging Directions in Genitourinary Cancers.Current oncology (Toronto, Ont.) · 2026Review
- AI-Guided Discovery of Oncogenic Signaling Crosstalk in Tumor Progression and Drug Resistance.Oncology research · 2026Review
- Targeting MDSCs in cancer: emerging immunotherapeutic and metabolic strategies.Frontiers in immunology · 2026Review
- From peripheral blood to tumor microenvironment: spatial dimension deficiency and paradigm reconstruction in immunotherapy biomarker research.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is rapidly revolutionizing the landscape of oncological research and the advancement of personalized clinical interventions. Progress in three interconnected areas, including the development of methods and algorithms for training AI models, the evolution of specialized computing hardware, and increased access to large volumes of cancer data such as imaging, genomics, and clinical information, has converged, leading to promising new applications of AI in cancer research. AI applications are systematically organized according to specific cancer types and clinical domains, encompassing the elucidation and prediction of biological mechanisms, the identification and utilization of patterns within clinical data to improve patient outcomes, and the unraveling of the complexities inherent in epidemiological, behavioral, and real-world datasets. When applied in an ethical and scientifically rigorous manner, these AI-driven approaches hold the promise of accelerating progress in cancer research and ultimately fostering improved health outcomes for all populations. We review examples demonstrating the integration of AI within oncology, highlighting cases where deep learning has adeptly addressed challenges once deemed insurmountable, while also discussing the barriers that must be surmounted to facilitate broader adoption of these technologies.
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.