ArticleImaging neuroscience (Cambridge, Mass.)2024
Applications of interpretable deep learning in neuroimaging: A comprehensive review.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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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
12 citing papers in PubMed.
- Brain Connectivity and Machine Learning Approaches to assess the underlying neurobiology and prediction accuracy of anorexia nervosa: A replication study.Psychiatry research. Neuroimaging · 2026Article
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- AutoML-Multiverse: An Instability-Aware Framework for Quantifying Analytic Variability in Alzheimer's Disease Machine-Learning Studies.medRxiv : the preprint server for health sciences · 2026Article
- Resting-State fMRI Co-Activation Patterns Reveal Multiscale Brain Functional Alterations in Primary Angle-Closure Glaucoma: Transcriptomic, Cellular, Neurochemical, and Machine-Learning Signatures.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- Explainable Artificial Intelligence in Non-Contrast Brain Computed Tomography Scan for Intracerebral Hemorrhage: A Scoping Review.Archives of academic emergency medicine · 2026Article
- Multimodal deep learning outperforms clinical and brain region models in predicting stroke-associated pneumonia: an explainable AI study.Frontiers in neurology · 2026Article
- Interpretable Disorder Signatures: Probing Neural Latent Spaces for Schizophrenia, Alzheimer's, and Autism Stratification.Brain sciences · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Autism Data Classification Using AI Algorithms with Rules: Focused Review.Bioengineering (Basel, Switzerland) · 2025Review
- Superpixel-ComBat modeling: A joint approach for harmonization and characterization of inter-scanner variability in T1-weighted images.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Clinical adoption of deep learning models has been hindered, in part, because the "black-box" nature of neural networks leads to concerns regarding their trustworthiness and reliability. These concerns are particularly relevant in the field of neuroimaging due to the complex brain phenotypes and inter-subject heterogeneity often encountered. The challenge can be addressed by interpretable deep learning (iDL) methods that enable the visualisation and interpretation of the inner workings of deep learning models. This study systematically reviewed the literature on neuroimaging applications of iDL methods and critically analysed how iDL explanation properties were evaluated. Seventy-five studies were included, and ten categories of iDL methods were identified. We also reviewed five properties of iDL explanations that were analysed in the included studies: biological validity, robustness, continuity, selectivity, and downstream task performance. We found that the most popular iDL approaches used in the literature may be sub-optimal for neuroimaging data, and we discussed possible future directions for the field.
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What Socratic 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 Socratic graph.