ArticleNPJ precision oncology2025
Role of large language models in the multidisciplinary decision-making process for patients with renal cell carcinoma: a pilot experience.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of large language models in tumor boards: a systematic review.Oncology reviews · 2026Pooled 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 · 2026Article
- Role prompting modulates linguistic style but not clinical decision structure in GPT-5 tumour board simulation.NPJ digital medicine · 2026Article
- AI-driven smart patient retrieval for precision oncology.Nature reviews. Cancer · 2026Article
- AI-enhanced oncology MDT 2.0: from multi-modal data synergy to value-based care reconstruction - a systematic review of clinical efficacy and socioeconomic benefits.Frontiers in oncology · 2026Review
- Precision oncology: from large language models to multi-agent systems.Frontiers in oncology · 2026Review
- Leveraging Machine Learning and Artificial Intelligence in Cancer Diagnostics Imaging: A Systematic Review.Cureus · 2025Review
- Review
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We evaluated an AI chatbot's ability to suggest diagnostic and therapeutic pathways for renal cell carcinoma (RCC) in a multidisciplinary tumor board (MDT). A retrospective analysis of 103 cases (2023-2024) found 62.1% agreement with MDT decisions (κ = 0.44, p< 0.001). Concordance was highest in when follow-up imaging was suggested (p = 0.001), with disease status influencing agreement (p = 0.004). These results suggest AI could assist in RCC case assessments, warranting further research.
Identifiers
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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.