ReviewBritish journal of cancer2022
High-dimensional role of AI and machine learning in cancer research.
Review in British journal of cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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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.
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Who cites it
33 citing papers in PubMed.
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Predicting Postoperative Survival in Patients With Malignant Biliary Obstruction Using an Interpretable Machine Learning Model: A Multicenter Study.Cancer medicine · 2026Article
- Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Optimizing feature selection in cancer microarray data using a heap-driven evolutionary framework for high-dimensional spaces.Scientific reports · 2026Article
- Machine learning-based models for predicting the efficacy and safety of recombinant human interleukin-11 in the treatment of cancer therapy-induced thrombocytopenia: exploration and preliminary validation from a multicenter retrospective study.Frontiers in cell and developmental biology · 2026Article
- An interpretable machine learning model for diagnostic classification of liver cancer using multivariable clinical data.American journal of cancer research · 2026Article
- An integrated prognostic model incorporating clinical characteristics, multiparametric MRI radiomics, deep learning features, and intratumoral heterogeneity for glioma patients.Frontiers in oncology · 2026Article
- A fairness-aware machine learning framework for maternal health in Ghana: integrating explainability, bias mitigation, and causal inference for ethical AI deployment.BioData mining · 2025Article
- Advances in NIR-II Fluorescent Nanoprobes: Design Principles, Optical Engineering, and Emerging Translational Directions.Micromachines · 2025Review
- On the translational potential of atlases in precision oncology.Translational cancer research · 2025Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Unlocking the potential: multimodal AI in biotechnology and digital medicine-economic impact and ethical challenges.NPJ digital medicine · 2025Review
- Article
- Integrating ctDNA testing forAdvances in laboratory medicine · 2025Review
- Precision nanomedicine: navigating the tumor microenvironment for enhanced cancer immunotherapy and targeted drug delivery.Molecular cancer · 2025Review
- Artificial Intelligence Applications in Decision-Making for Disease Management:Sultan Qaboos University medical journal · 2025Article
- The Prognostic Impact of the Tumor Immune Microenvironment in Synovial Sarcoma: An Immunohistochemical Analysis Using Digital Pathology and Conventional Interpretation.Journal of personalized medicine · 2025Article
- Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.Discover oncology · 2025Article
- Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The role of Artificial Intelligence and Machine Learning in cancer research offers several advantages, primarily scaling up the information processing and increasing the accuracy of the clinical decision-making. The key enabling tools currently in use in Precision, Digital and Translational Medicine, here named as 'Intelligent Systems' (IS), leverage unprecedented data volumes and aim to model their underlying heterogeneous influences and variables correlated with patients' outcomes. As functionality and performance of IS are associated with complex diagnosis and therapy decisions, a rich spectrum of patterns and features detected in high-dimensional data may be critical for inference purposes. Many challenges are also present in such discovery task. First, the generation of interpretable model results from a mix of structured and unstructured input information. Second, the design, and implementation of automated clinical decision processes for drawing disease trajectories and patient profiles. Ultimately, the clinical impacts depend on the data effectively subjected to steps such as harmonisation, integration, validation, etc. The aim of this work is to discuss the transformative value of IS applied to multimodal data acquired through various interrelated cancer domains (high-throughput genomics, experimental biology, medical image processing, radiomics, patient electronic records, etc.).
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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.