ReviewNature reviews. Clinical oncology2024
Towards equitable AI in oncology.
Review in Nature reviews. Clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- Review
- Dissecting self-supervised learning strategies for transfer learning in MRI prostate cancer diagnosis.Scientific reports · 2026Article
- Artificial Intelligence Tools in Precision Lung Cancer Care: From Early Detection to Clinical Decision Support.Cancers · 2026Review
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- An AI framework for automated quality control of paraffin block and slide consistency: a clinical evaluation and human-machine comparison study.Virchows Archiv : an international journal of pathology · 2026Article
- The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer.Cancer biology & medicine · 2026Review
- Molecular subgroups and biomarker guided precision immunotherapy in triple-negative breast cancer: advances, challenges, and future directions.Frontiers in immunology · 2026Review
- Bibliometric mapping of artificial intelligence research in surgical education (1997-2025).Frontiers in surgery · 2026Article
- Artificial intelligence in cardio-oncology: decoding mechanisms, predicting toxicity, and personalizing cancer therapy.Frontiers in cardiovascular medicine · 2026Review
- A Multi-Modal Transfer Learning Framework to Reduce Health Disparities in Prostate Adenocarcinoma.bioRxiv : the preprint server for biology · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Opportunities for Artificial Intelligence in Oncology: From the Lens of Clinicians and Patients.JCO oncology practice · 2025Review
- Navigating the challenges of artificial intelligence integration in thoracic surgery.Current challenges in thoracic surgery · 2025Article
- Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.Discover oncology · 2025Review
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific.The Lancet regional health. Western Pacific · 2025Review
- Breast cancer: pathogenesis and treatments.Signal transduction and targeted therapy · 2025Review
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) stands at the threshold of revolutionizing clinical oncology, with considerable potential to improve early cancer detection and risk assessment, and to enable more accurate personalized treatment recommendations. However, a notable imbalance exists in the distribution of the benefits of AI, which disproportionately favour those living in specific geographical locations and in specific populations. In this Perspective, we discuss the need to foster the development of equitable AI tools that are both accurate in and accessible to a diverse range of patient populations, including those in low-income to middle-income countries. We also discuss some of the challenges and potential solutions in attaining equitable AI, including addressing the historically limited representation of diverse populations in existing clinical datasets and the use of inadequate clinical validation methods. Additionally, we focus on extant sources of inequity including the type of model approach (such as deep learning, and feature engineering-based methods), the implications of dataset curation strategies, the need for rigorous validation across a variety of populations and settings, and the risk of introducing contextual bias that comes with developing tools predominantly in high-income countries.
Indexed as
Identifiers
38849530What 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.