ReviewNeurology and therapy2019
Deep Learning and Neurology: A Systematic Review.
Review in Neurology and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 5 of them syntheses that pooled it.
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
54 citing papers in PubMed, 5 syntheses or guidelines pooled it, 174 citations in OpenAlex.
- A survey of deep learning techniques in detecting neurological disorders using MRI.Biomedical engineering online · 2026Pooled it
- Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: an umbrella review.Frontiers in psychiatry · 2026Pooled it
- Artificial intelligence in diabetes management: transformative potential, challenges, and opportunities in healthcare.Hormones (Athens, Greece) · 2025Pooled it
- Facial expression deep learning algorithms in the detection of neurological disorders: a systematic review and meta-analysis.Biomedical engineering online · 2025Pooled it
- Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- Assessment of Therapeutic Responses Using a Deep Neural Network Based onMedicina (Kaunas, Lithuania) · 2022Trial
- Review
- Identification of nonconvulsive status epilepticus in the ictal-interictal continuum using artificial intelligence: a prospective observational cohort study.Encephalitis (Seoul, Korea) · 2026Article
- Machine learning classification of early-stage Parkinson's disease using sit-to-walk biomechanical features.Scientific reports · 2026Article
- Artificial intelligence in acute and critical care: current challenges and strategic solutions.Frontiers in public health · 2026Review
- Article
- Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models.Current neurology and neuroscience reports · 2025Review
- Towards Clinical Diagnoses: Classifying Alzheimer's Disease Using Single fMRI, Small Datasets, and Transfer Learning.Brain and behavior · 2025Article
- Leptomeningeal metastatic disease: new frontiers and future directions.Nature reviews. Clinical oncology · 2025Review
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Fusion of 3D photorealistic lateral-to-medial brain white matter dissection and diffusion tensor imaging for dynamic visualization of key fiber tracts.Brain & spine · 2025Article
- Article
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.BMC neurology · 2024Review
- Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.European radiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deciphering the massive volume of complex electronic data that has been compiled by hospital systems over the past decades has the potential to revolutionize modern medicine, as well as present significant challenges. Deep learning is uniquely suited to address these challenges, and recent advances in techniques and hardware have poised the field of medical machine learning for transformational growth. The clinical neurosciences are particularly well positioned to benefit from these advances given the subtle presentation of symptoms typical of neurologic disease. Here we review the various domains in which deep learning algorithms have already provided impetus for change-areas such as medical image analysis for the improved diagnosis of Alzheimer's disease and the early detection of acute neurologic events; medical image segmentation for quantitative evaluation of neuroanatomy and vasculature; connectome mapping for the diagnosis of Alzheimer's, autism spectrum disorder, and attention deficit hyperactivity disorder; and mining of microscopic electroencephalogram signals and granular genetic signatures. We additionally note important challenges in the integration of deep learning tools in the clinical setting and discuss the barriers to tackling the challenges that currently exist.
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.