ReviewCancer cell2025
New horizons at the interface of artificial intelligence and translational cancer research.
Review in Cancer cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed.
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- Mechanism-Driven Diagnostic Development: A Specimen-Aware Framework Illustrated by Colorectal Cancer and Solid Tumours.Cancers · 2026Review
- nnMIL: a generalizable multiple instance learning framework for computational pathology.Nature biomedical engineering · 2026Article
- Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Article
- Early detection of gastric cancer: a novel circulating microbiome DNA based liquid biopsy assay.NPJ precision oncology · 2026Article
- Invest where impact begins: recommendations from Breast Cancer Research Foundation Early Career Investigator Working Group (Part 1 of 2).NPJ breast cancer · 2026Article
- 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
- From Pyroptosis Heterogeneity to an Interpretable Prognostic Signature for Risk Stratification and Therapy Insights in Pancreatic Adenocarcinoma.Biomedicines · 2026Article
- Single-cell mitophagy signature-based artificial intelligence model enhances prediction of prognosis and immunotherapy response in non-small-cell lung cancer.Respiratory research · 2026Article
- SpatialFusion: A lightweight multimodal foundation model for pathway-informed spatial niche mapping.bioRxiv : the preprint server for biology · 2026Article
- AI knowledge, attitudes, perceptions, and willingness to use AI among oncologists in China: a nationwide cross-sectional study.BMC medical education · 2026Article
- From Black Box to Biological Insight: AttentioFuse Unlocks Multi-Omics Dynamics in Lung Cancer.Cancers · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Convergence of machine learning and genomics for precision oncology.Nature reviews. Cancer · 2026Review
- Article
- The role of AI-powered molecular profiling in the diagnosis and management of cancers of unknown primary: a case report and literature review.Frontiers in oncology · 2026Article
- Beyond static biomarkers: systems biology and AI for decoding cancer dynamics.Frontiers in systems biology · 2026Review
- Glioma-intrinsic SLC1A3 hijacks the vascular niche to establish an immunosuppressive microenvironment.Frontiers in immunology · 2026Article
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
Abstract
Artificial intelligence (AI) is increasingly being utilized in cancer research as a computational strategy for analyzing multiomics datasets. Advances in single-cell and spatial profiling technologies have contributed significantly to our understanding of tumor biology, and AI methodologies are now being applied to accelerate translational efforts, including target discovery, biomarker identification, patient stratification, and therapeutic response prediction. Despite these advancements, the integration of AI into clinical workflows remains limited, presenting both challenges and opportunities. This review discusses AI applications in multiomics analysis and translational oncology, emphasizing their role in advancing biological discoveries and informing clinical decision-making. Key areas of focus include cellular heterogeneity, tumor microenvironment interactions, and AI-aided diagnostics. Challenges such as reproducibility, interpretability of AI models, and clinical integration are explored, with attention to strategies for addressing these hurdles. Together, these developments underscore the potential of AI and multiomics to enhance precision oncology and contribute to advancements in cancer care.
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.