ReviewBrain communications2020
Artificial intelligence for clinical decision support in neurology.
Review in Brain communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Bridging the global Parkinson's divide: Technology as a structural solution for equitable and brain health-integrated care.Journal of Parkinson's disease · 2026Review
- The future digital outpatient clinic for movement disorders: from technological innovation to ethical and systemic implementation-a position paper.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Artificial intelligence revolutionizing CNS drug discovery and development.Drug discovery today · 2026Review
- Harnessing artificial intelligence for cancer rehabilitation: A call to action.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Towards precision MRI biomarkers in epilepsy with normative modelling.Brain : a journal of neurology · 2025Review
- Relational Ethics in the Administration of Healthcare Technology: AI, Automation and Proper Distance.Sociology of health & illness · 2025Article
- Evaluating the diagnostic performance of OpenBioLLM in neurology: A case-based assessment of a medical large language model.PloS one · 2025Article
- Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System.Journal of medical systems · 2024Article
- Computer-aided prognosis of tuberculous meningitis combining imaging and non-imaging data.Scientific reports · 2024Article
- Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review.JMIR medical informatics · 2024Review
- Could AI safeguard us from AI?Brain communications · 2024Article
- Combined Edge Loss UNet for Optimized Segmentation in Total Knee Arthroplasty Preoperative Planning.Bioengineering (Basel, Switzerland) · 2023Article
- Performance of Large Language Models on a Neurology Board-Style Examination.JAMA network open · 2023Article
- An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-based Schizophrenia Diagnosis.bioRxiv : the preprint server for biology · 2023Article
- Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine.Diagnostics (Basel, Switzerland) · 2023Review
- Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare.Journal of personalized medicine · 2023Review
- May Artificial Intelligence Influence Future Pediatric Research?-The Case of ChatGPT.Children (Basel, Switzerland) · 2023Article
- Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples.EBioMedicine · 2023Article
- Assessment of ChatGPT's performance on neurology written board examination questions.BMJ neurology open · 2023Article
- Towards greater neuroimaging classification transparency via the integration of explainability methods and confidence estimation approaches.Informatics in medicine unlocked · 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
6 authors.
Funding
Abstract
Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical data will lead to improved prognostic and diagnostic models in neurological disease, facilitating expert-level clinical decision tools across healthcare settings. Despite the clinical promise of artificial intelligence, machine and deep-learning algorithms are not a one-size-fits-all solution for all types of clinical data and questions. In this article, we provide an overview of the core concepts of artificial intelligence, particularly contemporary deep-learning methods, to give clinician and neuroscience researchers an appreciation of how artificial intelligence can be harnessed to support clinical decisions. We clarify and emphasize the data quality and the human expertise needed to build robust clinical artificial intelligence models in neurology. As artificial intelligence is a rapidly evolving field, we take the opportunity to iterate important ethical principles to guide the field of medicine is it moves into an artificial intelligence enhanced future.
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.