ReviewDiagnostics (Basel, Switzerland)2023
Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Decision processes in 3D structural MRI schizophrenia classification evaluated with saliency maps.Scientific reports · 2026Article
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Review
- Data-driven vestibular fMRI in PPPD and residual dizziness after BPPV: a narrative methodological review of AI-based phenotyping and longitudinal modeling.Frontiers in neurology · 2026Review
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Evaluation of attitudes of university students towards artificial intelligence using data mining methods.Scientific reports · 2025Article
- Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Obstetric and Gynecological MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Early evaluation of anti-angiogenic effects with gadolinium(III) labeled APN/CD13 specific binding peptides magnetic resonance imaging.Scientific reports · 2025Article
- Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.Diagnostics (Basel, Switzerland) · 2025Review
- Diagnosis of skull-base invasion by nasopharyngeal tumors on CT with a deep-learning approach.Japanese journal of radiology · 2024Article
- Ultrasound Image Analysis with Vision Transformers-Review.Diagnostics (Basel, Switzerland) · 2024Review
- Deep learning-based automated diagnosis of temporomandibular joint anterior disc displacement and its clinical application.Frontiers in physiology · 2024Article
- Deep learning for detecting prenatal alcohol exposure in pediatric brain MRI: a transfer learning approach with explainability insights.Frontiers in computational neuroscience · 2024Article
- An Umbrella Review of the Fusion of fMRI and AI in Autism.Diagnostics (Basel, Switzerland) · 2023Review
- Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease.Frontiers in aging neuroscience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as "black boxes". There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.
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.