ReviewCurrent oncology (Toronto, Ont.)2024
The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists.
Review in Current oncology (Toronto, Ont.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Precision Medicine and Artificial Intelligence in Next-Generation Cancer Surgery: A Comprehensive Analysis of Clinical Applications, Therapeutic Outcomes, and Implementation Strategies.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Large Language Models in Multidisciplinary Decision-Making for Hepatopancreatobiliary Oncology: Retrospective Comparative Feasibility Study.Journal of medical Internet research · 2026Article
- Precision oncology meets Generative AI: assessing large language models in multidisciplinary GIST tumor boards.BMC cancer · 2026Article
- Article
- AI-driven smart patient retrieval for precision oncology.Nature reviews. Cancer · 2026Article
- Artificial intelligence agents in cancer research and oncology.Nature reviews. Cancer · 2026Review
- A Deep Learning Algorithm for Liver Metastasis Detection at Contrast-enhanced Abdominal CT in Patients with Colorectal Cancer: A Comparative Study with Radiologists.Radiology. Imaging cancer · 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
- AI and human expertise in cancer care - striving for synergy.Nature reviews. Clinical oncology · 2026Article
- The role of artificial intelligence in postoperative clinical decision-making for pancreatic cancer: a pilot study.Frontiers in surgery · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Data-driven strategies for immunoradiotherapy in uveal melanoma: the role of artificial intelligence.Frontiers in pharmacology · 2026Review
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Intelligent therapeutic robotic-assisted surgery as the next frontier of precision oncology.Frontiers in oncology · 2026Review
- Artificial intelligence in multidisciplinary tumor boards enhancing decision making and clinical outcomes in oncology.iScience · 2025Review
- From Slide to Insight: The Emerging Alliance of Digital Pathology and AI in Melanoma Diagnostics.Cancers · 2025Review
- Gastroenterology in the age of artificial intelligence: Bridging technology and clinical practice.World journal of gastroenterology · 2025Review
- Research trends of global artificial intelligence application in obstetrics and gynecology from 1999 to 2025: a bibliometric analysis based on web of science.Journal of robotic surgery · 2025Review
- Chat GPT Performance in Multi-Disciplinary Boards-Should AI Be a Member of Cancer Boards?Healthcare (Basel, Switzerland) · 2025Article
- [Digital urology : Possible uses for artificial intelligence and digital health applications].Urologie (Heidelberg, Germany) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making. This review explores the perspectives of clinicians dedicated to the care of cancer patients-surgeons, medical oncologists, and radiation oncologists-on the application of AI within MTBs. Additionally, it examines the role of AI across various clinical specialties involved in cancer diagnosis and treatment. By analyzing both the potential and the challenges, this study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans. The findings highlight the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.
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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.