SynthesisFrontiers in artificial intelligence2026
Leveraging artificial intelligence to support surgical oncology multidisciplinary team decision-making: a systematic review.
Synthesis in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Artificial intelligence (AI)-based clinical decision support systems (CDSSs) are increasingly used to support clinicians by providing evidence-based treatment recommendations for multidisciplinary team (MDT) meetings. This systematic review mapped the current landscape of AI-based CDSSs in surgical oncology decision-making and synthesized evidence on their performance and factors influencing effectiveness. Methods: Cochrane, Ovid MEDLINE, and Embase were searched on February 3, 2025. Studies evaluating AI-based CDSSs for therapeutic decision-making in surgical oncology were included. Methodological quality was assessed using the Critical Appraisal Skills Programme Diagnostic Study Checklist, and data were synthesized narratively. Results: Fifty-nine studies, encompassing 23,158 patients, were included. CDSSs were classified into five categories: decision tree-based systems, knowledge representation-based systems, Watson for Oncology (WfO), large language models, and other AI-based systems. Concordance with MDT or guideline-based recommendations ranged from 23.2% to 99%. Decision tree- and knowledge-based systems generally demonstrated higher concordance and improved guideline adherence, while WfO performance varied substantially by region and treatment accessibility. Three key themes related to implementation challenges emerged: technical limitations, socioeconomic and healthcare system constraints, and patient- and tumour-specific factors. Reported benefits included improved adherence of MDT decisions to clinical guidelines, enhanced identification of patients eligible for clinical trial enrolment, support for less experienced clinicians, and facilitation of triage for routine cases. Discussion: AI-based CDSSs show promise in supporting MDT decision-making but remain constrained by challenges related to system maintenance, variability in clinical protocols, therapeutic availability, and patient heterogeneity. Larger prospective studies are needed to evaluate the real-world integration, clinical impact, and patient outcomes of AI-based CDSSs within MDT workflows. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42025639227, identifer: CRD42025639227.
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