ReviewJournal of translational medicine2025
Exploiting artificial intelligence in precision oncology: an updated comprehensive review.
Review in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Financing and health system capacity for precision medicine in Asia: a six country landscape analysis.Health affairs scholar · 2026Review
- Tuning epigenetics to enhance cancer virotherapy.Acta pharmaceutica Sinica. B · 2026Review
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- KIT-dependent acute myeloid leukemias are responsive to LSD1 inhibition.Clinical epigenetics · 2026Article
- Molecular Oncodiagnostics in Precision Oncology: Integrating Tumor Transcriptomics, Patient Pharmacogenetics, and Ex Vivo Chemoresistance Testing to Improve Individual Chemotherapy Response.Journal of personalized medicine · 2026Review
- Mitochondrial biology and immune crosstalk in breast cancer: therapeutic opportunities and challenges.Frontiers in immunology · 2026Review
- AI in Prostate Cancer Screening & Diagnosis: A Registry-Based Study of ClinicalTrials.gov Trials.Inquiry : a journal of medical care organization, provision and financingArticle
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
Precision oncology considers the genetic makeup of both the tumor and the cancer patient, medical history, clinical metadata, lifestyle and environmental factors. Thus, adoption of precision cancer medicine mandates sophisticated integrative analysis. Being a master in sorting and solving the puzzle pieces, artificial intelligence (AI)-powered frameworks emerged to fill the gap via executing multimodal analysis and generating meaningful outputs. Herein, we systematically discussed the opportunities and challenges of exploiting AI-assisted models in preclinical cancer research and in clinical oncology to promote precision medicine of cancer patients. We also shed light on the FDA-approved AI-driven tools and reviewed the clinical trials evaluating the capabilities of AI-supported algorithms in the diagnosis, screening, risk stratification, anticancer drug response prediction, clinical trial matching, informed treatment decision-making, personalized prescription and supportive care of cancer patients. Despite the foreseen promise of AI-driven frameworks in revolutionizing cancer personalized medicine, large-scale multicenter prospective studies and consensus regulatory guidelines are urged to ensure their safe, efficient and responsible use.
Indexed as
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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.