ArticleJournal of gastrointestinal cancer2024
The Fidelity of Artificial Intelligence to Multidisciplinary Tumor Board Recommendations for Patients with Gastric Cancer: A Retrospective Study.
Article in Journal of gastrointestinal cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging artificial intelligence to support surgical oncology multidisciplinary team decision-making: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Concordance Between a Generative Artificial Intelligence Model and a Hepatobiliary Multidisciplinary Team in Hepatocellular Carcinoma Management: A Retrospective Study.Annals of surgical oncology · 2026Article
- ChatGPT-4o as a decision-support tool in a urological tumour board: a prospective evaluation.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Multidisciplinary tumor board decisions and artificial intelligence-generated recommendations in general surgery: a retrospective observational study.Updates in surgery · 2026Article
- Assessment of Large Language Models in Colorectal Cancer Multidisciplinary Tumor Board Decision-Making: A Retrospective Single-Center Comparison of Guideline-Integrated General-Purpose vs. Domain-Specialized Models.Current oncology (Toronto, Ont.) · 2026Article
- Evaluation of Artificial Intelligence as a Decision-Support Tool in Urological Tumor Boards: A Study in Real Clinical Practice.Journal of clinical medicine · 2026Article
- When AI joins the table: evaluating large language model performance in soft tissue sarcoma tumor board decisions.Journal of cancer research and clinical oncology · 2026Article
- Molecular pathogenesis and novel therapeutic avenues in alpha-fetoprotein-producing gastric cancer.Frontiers in oncology · 2026Review
- Current Role of Artificial Intelligence in the Management of Gastric Cancer.Biomedicines · 2025Review
- Gastroenterology in the age of artificial intelligence: Bridging technology and clinical practice.World journal of gastroenterology · 2025Review
- Evaluating the Concordance Between ChatGPT and Multidisciplinary Teams in Breast Cancer Treatment Planning: A Study from Bosnia and Herzegovina.Journal of clinical medicine · 2025Article
- Chat GPT Performance in Multi-Disciplinary Boards-Should AI Be a Member of Cancer Boards?Healthcare (Basel, Switzerland) · 2025Article
- Concordance of ChatGPT artificial intelligence decision-making in colorectal cancer multidisciplinary meetings: retrospective study.BJS open · 2025Article
- The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists.Current oncology (Toronto, Ont.) · 2024Review
- Evaluating ChatGPT-4o as a decision support tool in multidisciplinary sarcoma tumor boards: heterogeneous performance across various specialties.Frontiers in oncology · 2024Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeDue to significant growth in the volume of information produced by cancer research, staying abreast of recent developments has become a challenging task. Artificial intelligence (AI) can learn, reason, and understand the enormous corpus of literature available to the scientific community. However, large-scale studies comparing the recommendations of AI and a multidisciplinary team board (MTB) in gastric cancer treatment have rarely been performed. Therefore, a retrospective real-world study was conducted to assess the level of concordance between AI and MTB treatment recommendations.
methodsTreatment recommendations of Watson for Oncology (WFO) and an MTB were retrospectively analyzed 322 patients with gastric cancer from January 2015 to December 2018 and the degree of agreement between them was compared. The patients were divided into concordance and non-concordance groups and factors affecting the concordance rate were analyzed.
resultsThe concordance rate between the AI and MTB was 86.96%. The concordance rates for each stage were 96.93% for stage I, 88.89% for stages II, 90.91% for stage III, and 45.83% for stage IV, respectively. In the multivariate analysis, age (p-value = 0.000), performance status (p-value = 0.003 for performance score 1; p-value = 0.007 for performance score 2; p-value = 0.000 for performance score 3), and stage IV (p-value = 0.017) had a significant effect on concordance between the MTB and WFO.
conclusionFactors affecting the concordance rate were age, performance status, and stage IV gastric cancer. To increase the validity of future medical AI systems for gastric cancer treatment, their supplementation with local guidelines and the ability to comprehensively understand individual patients is essential.
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