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Brain Imaging01:14

Brain Imaging

204
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
204
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

4.9K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: May 31, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About

Yasir Hafeez1, Khuhed Memon2, Maged S Al-Quraishi3

  • 1Faculty of Science and Engineering, University of Nottingham, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia.

Diagnostics (Basel, Switzerland)
|January 25, 2025
PubMed
Summary

Explainable artificial intelligence (XAI) may bridge the gap between AI in diagnostic radiology and clinical practice. Current research prioritizes accuracy over explanation, hindering adoption.

Keywords:
brain MRIcomputer aided diagnosisdeep learningexplainable artificial intelligencemedical image analysisneurological disorders

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • AI has not yet been widely integrated into diagnostic medicine or clinical practice.
  • Despite advancements in computer-aided diagnosis (CAD) tools, their large-scale adoption in healthcare remains limited.
  • Diagnostic radiology, while utilizing advanced imaging techniques like MRI and CT, has not incorporated AI-powered scan analysis into standard procedures.

Purpose of the Study:

  • To review the contributions of AI in developing systems for brain MRI analysis for neurological disorder diagnosis.
  • To emphasize computer-aided diagnosis (CAD) systems that incorporate explainability.
  • To explore whether explainable artificial intelligence (XAI) can be a turning point for AI adoption in diagnostic radiology.

Main Methods:

  • A comprehensive literature review of studies from 2017 to 2024 focusing on AI and brain MRI analysis.
  • Emphasis on CAD systems with explainability features.
  • Inclusion of medical domain experts' opinions and challenges for XAI in medical diagnostics.

Main Results:

  • Forty-seven studies were summarized, detailing XAI technologies, datasets, and performance accuracies.
  • Strengths and weaknesses of reviewed studies were discussed.
  • Opinions from seven international medical experts were presented to guide AI tool development.

Conclusions:

  • Current CAD research focuses on performance accuracy, neglecting the authenticity and usefulness of explanations.
  • A shortage of ground truth data for explainability and a dominance of visual explanation methods were observed.
  • More comprehensive, human-like explanations are needed to build trust, alongside addressing legal, ethical, safety, and security concerns.