ArticleESMO real world data and digital oncology2026
Comparing artificial intelligence and multidisciplinary tumor board decision making in real-world cancer care: a prospective blinded concordance study.
Article in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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
3 citing papers in PubMed.
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Artificial Intelligence and Multidisciplinary Oncology Decision-Making: A Systematic Review of LLM Concordance with MDT Recommendations and Implications for Clinical Reasoning Education.Advances in medical education and practice · 2026Review
- Artificial intelligence in colorectal cancer multidisciplinary decision-making: concordance, predictive support and clinical translation.Frontiers in oncology · 2026Review
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Multidisciplinary tumor boards (MDTs) integrate expertise, enhance diagnostic accuracy, improve adherence to evidence-based guidelines, and facilitate individualized treatment planning. Recent advances in artificial intelligence (AI) help streamline these processes, though prospective real-world evaluations remain limited. Materials and methods: We conducted a prospective, non-interventional, blinded concordance study at a tertiary cancer center. Consecutive cases discussed at institutional MDTs between January 2025 and June 2025 were screened for eligibility. Cases with comprehensive clinical information and documented MDT decisions were included. Anonymized vignettes were input into ChatGPT® using a standardized template. A blinded expert reviewer assessed concordance using a predefined three-point scale. The primary outcome was mean concordance score (MCS) for primary clinical query. Secondary outcomes were domain-specific concordance and reviewer-perceived clinical acceptability of AI decisions. Results: A total of 106 cases (median age 53 years) were analyzed, spanning 21 tumor sites, with the most common being breast (17%), ovary (10.4%), and esophagus (8.5%). Disease stages at MDT discussion were early (20.8%), locally advanced (49.1%), Conclusions: In a prospective real-world setting, moderate-high concordance was observed for AI and MDT decisions across multiple domains. These findings support further evaluation of AI as a decision-support tool within multidisciplinary oncology care.
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