ReviewJournal of cancer research and clinical oncology2024
Large language models as decision aids in neuro-oncology: a review of shared decision-making applications.
Review in Journal of cancer research and clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- Omission and hallucination prevalence of clinical guidelines in diagnostic large language model outputs.BMJ health & care informatics · 2026Article
- Digitally Assisted Clinical Decision-Making in Traditional Chinese Medicine: Comparative Study of 5 Large Language Models.JMIR formative research · 2026Article
- Digital patient decision aids for endometriosis management: a scoping review protocol.BMJ open · 2026Article
- Evidence-Based Analysis of AI Chatbots in Oncology Patient Education: Implications for Trust, Perceived Realness, and Misinformation Management.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025Review
- Article
- Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer.Nature reviews. Cancer · 2025Review
- Fine-Tuning Large Language Models for Specialized Use Cases.Mayo Clinic proceedings. Digital health · 2025Review
- Application of large language models in disease diagnosis and treatment.Chinese medical journal · 2025Review
- Review
- Research progress and implications of the application of large language model in shared decision-making in China's healthcare field.Frontiers in public health · 2025Review
- Autonomous medical evaluation for guideline adherence of large language models.NPJ digital medicine · 2024Article
- Accuracy and consistency of publicly available Large Language Models as clinical decision support tools for the management of colon cancer.Journal of surgical oncology · 2024Article
- Artificial intelligence in neuro-oncology: Current advances and legal considerations in the European Union.Neuro-oncology advancesReview
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
Abstract
Shared decision-making (SDM) is crucial in neuro-oncology, fostering collaborations between patients and healthcare professionals to navigate treatment options. However, the complexity of neuro-oncological conditions and the cognitive and emotional burdens on patients present significant barriers to achieving effective SDM. This discussion explores the potential of large language models (LLMs) such as OpenAI's ChatGPT and Google's Bard to overcome these barriers, offering a means to enhance patient understanding and engagement in their care. LLMs, by providing accessible, personalized information, could support but not supplant the critical insights of healthcare professionals. The hypothesis suggests that patients, better informed through LLMs, may participate more actively in their treatment choices. Integrating LLMs into neuro-oncology requires navigating ethical considerations, including safeguarding patient data and ensuring informed consent, alongside the judicious use of AI technologies. Future efforts should focus on establishing ethical guidelines, adapting healthcare workflows, promoting patient-oriented research, and developing training programs for clinicians on the use of LLMs. Continuous evaluation of LLM applications will be vital to maintain their effectiveness and alignment with patient needs. Ultimately, this exploration contends that the thoughtful integration of LLMs into SDM processes could significantly enhance patient involvement and strengthen the patient-physician relationship in neuro-oncology care.
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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.