ReviewFrontiers in digital health2026
Implementation of AI in oncology: a systematic review of educational and clinical integration in global contexts.
Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- A Scoping Review of Implemented Innovations in Cancer Care: Implications for Pan-Canadian Scaling.Current oncology (Toronto, Ont.) · 2026Article
- Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges.Methods and protocols · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
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
Introduction: The global burden of cancer, coupled with inequalities in access to healthcare, has intensified interest in artificial intelligence (AI) as a transformative force in oncology. This study systematically analyzes how AI is being integrated into oncology education and clinical practice globally, highlighting its role in training the next generation of healthcare professionals. Methodology: A systematic review was conducted following the PRISMA and PICO guidelines, using data from PubMed, Scopus, and IEEE Xplore. Studies from 2019 to 2024 were selected based on empirical evaluations of AI-based educational interventions in oncology. Educational outcomes were extracted as performance (e.g., accuracy, test scores, comprehension/quality ratings), engagement/acceptability (e.g., usability or satisfaction ratings, completion), and skills development (e.g., task-based procedural metrics), as reported by each study. A total of 16 studies met the inclusion criteria after a rigorous selection and eligibility verification process. Results and discussion: The findings reveal that AI tools, especially chatbots based on large language models (e.g., ChatGPT), are increasingly being used in the education of both patients and professionals. Positive outcomes include improved knowledge retention, diagnostic reasoning, and engagement. Conclusion: AI is transforming cancer education through personalized and accessible learning experiences. Strategic integration, rigorous validation, and equitable access are essential for sustainable impact across diverse global settings.
Indexed as
Identifiers
What 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.