ReviewiScience2025
Artificial intelligence in multidisciplinary tumor boards enhancing decision making and clinical outcomes in oncology.
Review in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
8 citing papers in PubMed.
- Quantum-Augmented Federated AI for Adaptive Pharmacogenomic Precision Oncology: A Perspective.ACS pharmacology & translational science · 2026Review
- Precision oncology meets Generative AI: assessing large language models in multidisciplinary GIST tumor boards.BMC cancer · 2026Article
- Single-session agreement of ChatGPT and Gemini treatment recommendations with multidisciplinary tumor board decisions in thoracic oncology.BMC cancer · 2026Observational
- Article
- Review
- Artificial Intelligence Language Models in Oncology: A Cross-Sectional Analysis of Published Studies.Cureus · 2026Review
- Comparing artificial intelligence and multidisciplinary tumor board decision making in real-world cancer care: a prospective blinded concordance study.ESMO real world data and digital oncology · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multidisciplinary tumor boards (MDTs) coordinate complex oncology decisions across imaging, pathology, genomics, and patient factors. Here we synthesize how artificial intelligence (AI)-including machine learning, natural language processing, deep learning, and large language models-supports MDT preparation and deliberation. Across cancers, reported agreement between AI recommendations and MDT decisions commonly ranges from 70% to 90%, and task-focused tools achieve high diagnostic performance in screening and staging. Benefits include faster information synthesis, more consistent guideline alignment, and clearer documentation of options, while human review remains central. Key limitations-data bias, uneven generalizability, privacy and governance concerns, and limited prospective validation-temper adoption. We outline implementation priorities: prospective multicenter evaluation, integration with electronic records, clinician training, and transparent oversight. Overall, AI can augment MDT decision-making and help personalize care and workflow efficiency when deployed with rigorous evaluation and safeguards.
Indexed as
Identifiers
What OpenQuestion holds
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.