ArticleBMJ health & care informatics2023
ChatGPT in glioma adjuvant therapy decision making: ready to assume the role of a doctor in the tumour board?
Article in BMJ health & care informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 5 of them syntheses that pooled it.
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Who cites it
54 citing papers in PubMed, 5 syntheses or guidelines pooled it, 91 citations in OpenAlex.
- Leveraging artificial intelligence to support surgical oncology multidisciplinary team decision-making: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Large Language Models for Chatbot Health Advice Studies: A Systematic Review.JAMA network open · 2025Pooled it
- Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Pooled it
- Exploring the role of ChatGPT in patient care (diagnosis and treatment) and medical research: A systematic review.Health promotion perspectives · 2023Pooled it
- Development of a ChatGPT-based assistant in multidisciplinary team discussions in oncological urology: the UroMDT Advisor study.International urology and nephrology · 2026Article
- Concordance between GPT-4 and a multidisciplinary tumor board in pancreatic cancer: A prospective pilot study.Langenbeck's archives of surgery · 2026Observational
- Enhancing patient care with AI agents: integrating advanced chatbot technologies for improved healthcare delivery.BMC medical informatics and decision making · 2026Article
- Article
- Large language models in patient education for brain tumors: opportunities, risks, and ethical considerations.Frontiers in oncology · 2026Review
- ARTEMIS: a pilot study comparing AI-based and expert therapeutic decisions in simulated clinical cases of neuroendocrine neoplasms.NPJ digital medicine · 2025Article
- Temporal evolution of large language models (LLMs) in oncology.Journal of translational medicine · 2025Article
- Large Language Models in Neurological Practice: Real-World Study.Journal of medical Internet research · 2025Article
- Large Language Models in Neurology Treatment Decision-Making: a Scoping Review.Journal of medical systems · 2025Article
- Development and evaluation of large-language models (LLMs) for oncology: A scoping review.PLOS digital health · 2025Article
- Role of large language models in the multidisciplinary decision-making process for patients with renal cell carcinoma: a pilot experience.NPJ precision oncology · 2025Article
- Large language model integrations in cancer decision-making: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- A Review of the Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead.AJNR. American journal of neuroradiology · 2025Review
- Evaluating large language models as an educational tool for meningioma patients: patient and clinician perspectives.Radiation oncology (London, England) · 2025Article
- The Role of Artificial Intelligence (ChatGPT-4o) in Supporting Tumor Board Decisions.Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate ChatGPT's performance in brain glioma adjuvant therapy decision-making.
methodsWe randomly selected 10 patients with brain gliomas discussed at our institution's central nervous system tumour board (CNS TB). Patients' clinical status, surgical outcome, textual imaging information and immuno-pathology results were provided to ChatGPT V.3.5 and seven CNS tumour experts. The chatbot was asked to give the adjuvant treatment choice, and the regimen while considering the patient's functional status. The experts rated the artificial intelligence-based recommendations from 0 (complete disagreement) to 10 (complete agreement). An intraclass correlation coefficient agreement (ICC) was used to measure the inter-rater agreement.
resultsEight patients (80%) met the criteria for glioblastoma and two (20%) were low-grade gliomas. The experts rated the quality of ChatGPT recommendations as poor for diagnosis (median 3, IQR 1-7.8, ICC 0.9, 95% CI 0.7 to 1.0), good for treatment recommendation (7, IQR 6-8, ICC 0.8, 95% CI 0.4 to 0.9), good for therapy regimen (7, IQR 4-8, ICC 0.8, 95% CI 0.5 to 0.9), moderate for functional status consideration (6, IQR 1-7, ICC 0.7, 95% CI 0.3 to 0.9) and moderate for overall agreement with the recommendations (5, IQR 3-7, ICC 0.7, 95% CI 0.3 to 0.9). No differences were observed between the glioblastomas and low-grade glioma ratings.
conclusionsChatGPT performed poorly in classifying glioma types but was good for adjuvant treatment recommendations as evaluated by CNS TB experts. Even though the ChatGPT lacks the precision to replace expert opinion, it may serve as a promising supplemental tool within a human-in-the-loop approach.
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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.