Artificial intelligence based techniques for brain tumor analysis: A systematic review.

Artif Intell MedMay 30, 2026 (epub)
Clinical ResearchNeurologyOncology (Medical)

Oluwabukola G Adegboro, Julia Dietlmeier, Noel E O'Connor et al.

✦ AI-curated · Sources linked

In 30 seconds

This systematic review evaluates artificial intelligence techniques for brain tumor analysis, focusing on segmentation and classification methods. Out of 3950 sourced papers, 101 met the inclusion criteria. The review highlights a lack of fully adopted AI methods in clinical practice and identifies a significant research gap in weakly-supervised segmentation approaches.

Key findings

  • Only 101 out of 3950 sourced papers were selected for review.
  • No AI methods for brain tumor analysis have been fully adopted in clinical practice.
  • Only four articles on Explainable AI (XAI) were identified in the review.
  • The review highlights a research gap in weakly-supervised brain tumor segmentation.

Why it matters

Timely diagnosis of brain tumors is crucial for improving patient outcomes and reducing treatment costs. Understanding the current state of AI techniques can guide future research and clinical applications.

Source

PubMed · PMID 42235350

Published in Artif Intell Med. This summary was written by xxcode from the publication's abstract and metadata. It is not peer reviewed and is not a substitute for the original article. For clinical decisions, review the original publication.

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