ArticleJAMIA open2026
Artificial intelligence for personalized management of vestibular schwannoma: a multidisciplinary clinical implementation study.
Article in JAMIA open, 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.
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Who cites it
3 citing papers in PubMed.
- 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
- The Real-World Impact of Vestibular Schwannoma Fully Automated Volume Measures on the Evaluation of Size Change and Clinical Management Outcomes in a Multidisciplinary Meeting Setting.The journal of international advanced otology · 2025Article
- Deep learning for automatic segmentation of vestibular schwannoma: a retrospective study from multi-center routine MRI.Frontiers in computational neuroscience · 2024Article
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Management of patients with vestibular schwannoma (VS) relies on precise tumor size and growth trend evaluation. We introduce and evaluate a novel computer-assisted reporting tool for clinical decision support during multidisciplinary team meetings (MDTMs) for VS patients. Materials and Methods: Our approach exploits deep learning for tumor segmentation, automating tumor volume, and standard linear measurement extraction. We conducted 2 simulated MDTMs with the same 50 patients evaluated in both arms to compare our proposed approach against the standard process, focusing on its impact on preparation time and decision-making. Results: Automated reports provided acceptable information for an expert neuroradiologist in 72% of cases, while the remaining 28% required some revision with manual feature extraction. The segmentation models used in this report generation task achieved Dice scores of 0.9392 ( Discussion: An initial learning curve in interpreting new data measurements is quickly mastered and the enhanced communication of growth patterns and more comprehensive assessments ultimately provides clinicians with the tools to offer patients more personalized care. Conclusion: This pilot clinical implementation study highlights the potential benefits of integrating automated measurements into clinical decision-making for VS management.
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Registered trials
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