ReviewBMJ oncology2025
Large language models in oncology: a review.
Review in BMJ oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis 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
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Comparative Evaluation of AI Chatbots for Testicular Cancer Education: Validity, Information Quality, and Readability.Annals of surgical oncology · 2026Article
- Artificial intelligence in onco-anaesthesia: Current applications, challenges, and future directions.World journal of methodology · 2026Review
- Large language models in oncology: promise, pitfalls, and the path to real-world adoption.ESMO real world data and digital oncology · 2026Article
- Article
- Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review.JMIR cancer · 2026Article
- Who Is Responsible When AI Gets Cancer Information Wrong? Implications for Patient Education.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Applications of Large Language Models in Ovarian Cancer Management: Protocol for a Systematic Review and Meta-Analysis.JMIR research protocols · 2026Article
- AI-enabled clinical decision support in breast cancer care: a blinded multicenter benchmarking study comparing medically specialized with a general-purpose system.Journal of medical systems · 2026Article
- Beyond classical models: LLM-driven survival analysis for breast cancer prognosis using European cancer registry data.BMC medical informatics and decision making · 2026Article
- An AI-assisted, failure mode-based toolkit for proactive risk management in radiotherapy: A feasibility study.Technical innovations & patient support in radiation oncology · 2026Article
- Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents.ESMO real world data and digital oncology · 2026Article
- Benchmarking large language models in breast cancer care: agreement with radiology-led multidisciplinary tumor board decisions.BMC medical informatics and decision making · 2026Article
- Evaluating the clinical safety of large language models in oral cancer-related patient communication: a repeated-prompt observational study.BMC oral health · 2026Observational
- Liquid Biopsy in Colorectal Cancer: Future Perspectives Through the Lens of Artificial Intelligence-A Comprehensive Review of Novel Literature.International journal of molecular sciences · 2026Review
- Review
- MicroRNAs in oncology: a translational perspective in the era of AI.Nature reviews. Clinical oncology · 2026Review
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- Benchmarking Large Language Models Using a Best Evidence Topic Report in a Patient With Early Non-Small Cell Lung Cancer.Interdisciplinary cardiovascular and thoracic surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have demonstrated emergent human-like capabilities in natural language processing, leading to enthusiasm about their integration in healthcare environments. In oncology, where synthesising complex, multimodal data is essential, LLMs offer a promising avenue for supporting clinical decision-making, enhancing patient care, and accelerating research. This narrative review aims to highlight the current state of LLMs in medicine; applications of LLMs in oncology for clinicians, patients, and translational research; and future research directions. Clinician-facing LLMs enable clinical decision support and enable automated data extraction from electronic health records and literature to inform decision-making. Patient-facing LLMs offer the potential for disseminating accessible cancer information and psychosocial support. However, LLMs face limitations that must be addressed before clinical adoption, including risks of hallucinations, poor generalisation, ethical concerns, and scope integration. We propose the incorporation of LLMs within compound artificial intelligence systems to facilitate adoption and efficiency in oncology. This narrative review serves as a non-technical primer for clinicians to understand, evaluate, and participate as active users who can inform the design and iterative improvement of LLM technologies deployed in oncology settings. While LLMs are not intended to replace oncologists, they can serve as powerful tools to augment clinical expertise and patient-centred care, reinforcing their role as a valuable adjunct in the evolving landscape of oncology.
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What 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.