ReviewNature reviews. Cancer2024
A guide to artificial intelligence for cancer researchers.
Review in Nature reviews. Cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 3 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
112 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Detection of oral potentially malignant disorders and oral cancer with white light through deep learning: a systematic review and meta-analysis.Clinical oral investigations · 2026Pooled it
- Mapping the frontiers: a bibliometric perspective on t cell-based immunotherapy in pancreatic cancer since the twenty-first century.Journal of cancer research and clinical oncology · 2025Pooled it
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- LungGPT: A unified multimodal system for interpretable diagnosis and clinical decision support of respiratory diseases.Cell reports. Medicine · 2026Article
- Development and Validation of a Computational Histology Artificial Intelligence-Powered Predictive Biomarker for Selection of Chemotherapy in Advanced Pancreatic Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- Integrating Lymph Node Metastasis and Programmed Death-Ligand 1 Prediction in Non-Small Cell Lung Cancer From a Single PET/CT Scan: Multicenter Radiomics Study.JMIR medical informatics · 2026Article
- From cell counts to cellular interactions: Cu-Cyto and the co-localization index as a spatial framework for the tumor immune microenvironment of rectal cancer.International journal of clinical oncology · 2026Review
- Review
- Application of transformer-enhanced convolutional neural network: multicenter MRI assessment of muscle invasion in bladder cancer.European radiology · 2026Article
- Development and validation of an immune-related molecular subtyping model based on a four-gene prognostic biomarker signature and multi-omics integrative analysis in Luminal B breast cancer.Breast cancer research : BCR · 2026Article
- A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing.Science advances · 2026Article
- Preclinical and Virtual Models of Mucosal Melanoma: Bridging Translational Gaps in a Rare and Lethal Cancer.Pigment cell & melanoma research · 2026Review
- AI-assisted histomorphological stratification of endometrial cancer: real-world validation of foundation models for molecular subtyping.NPJ precision oncology · 2026Article
- Profiling of extracellular vesicles from primary hepatocytes, organoids, and mash patients identifies cell injury-specific signatures.Scientific reports · 2026Article
- Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.Nature cancer · 2026Article
- Germline mutations and somatic mosaicism in steatotic liver diseases and related liver carcinogenesis.Nature reviews. Gastroenterology & hepatology · 2026Review
- [Artificial intelligence in daily urological care: results of the AI barometer].Urologie (Heidelberg, Germany) · 2026Article
52 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries. Building a basic literacy in these tools is useful for every cancer researcher. Researchers with a traditional biological science focus can use AI-based tools through off-the-shelf software, whereas those who are more computationally inclined can develop their own AI-based software pipelines. In this article, we provide a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them. We convey general principles of AI for applications in image analysis, natural language processing and drug discovery. In addition, we give examples of how non-computational researchers can get started on the journey to productively use AI in their own work.
Indexed as
Identifiers
38755439What 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.