ReviewFrontiers of medicine2024
Artificial intelligence methods available for cancer research.
Review in Frontiers of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 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
- Development and internal evaluation of an interpretable machine learning model based on clinical and radiomics features to differentiate lower extremity arterial embolism from atherothrombosis.Frontiers in medicine · 2026Article
- Nursing for cancer patients: research hotspots and trends through bibliometric and visualization analysis based on Web of Science (2014-2024).Annals of medicine and surgery (2012) · 2025Review
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis.BMJ open · 2025Article
- Integrating artificial intelligence in drug discovery and early drug development: a transformative approach.Biomarker research · 2025Review
- Identification of multiple complications as independent risk factors associated with 1-, 3-, and 5-year mortality in hepatitis B-associated cirrhosis patients.BMC infectious diseases · 2025Article
- Large language models in cancer: potentials, risks, and safeguards.BJR artificial intelligence · 2025Review
- Deep Learning Models for Predicting the Recurrence of Idiopathic Granulomatous Mastitis.Journal of inflammation research · 2025Article
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
Cancer is a heterogeneous and multifaceted disease with a significant global footprint. Despite substantial technological advancements for battling cancer, early diagnosis and selection of effective treatment remains a challenge. With the convenience of large-scale datasets including multiple levels of data, new bioinformatic tools are needed to transform this wealth of information into clinically useful decision-support tools. In this field, artificial intelligence (AI) technologies with their highly diverse applications are rapidly gaining ground. Machine learning methods, such as Bayesian networks, support vector machines, decision trees, random forests, gradient boosting, and K-nearest neighbors, including neural network models like deep learning, have proven valuable in predictive, prognostic, and diagnostic studies. Researchers have recently employed large language models to tackle new dimensions of problems. However, leveraging the opportunity to utilize AI in clinical settings will require surpassing significant obstacles-a major issue is the lack of use of the available reporting guidelines obstructing the reproducibility of published studies. In this review, we discuss the applications of AI methods and explore their benefits and limitations. We summarize the available guidelines for AI in healthcare and highlight the potential role and impact of AI models on future directions in cancer research.
Indexed as
Identifiers
39115792What Socratic 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 Socratic graph.