Trustworthy Neurological AI: Evidence Standards from Validation to Clinical Deployment

Artificial intelligence is now being investigated across much of clinical neurology, including EEG interpretation, seizure detection, stroke imaging, neurodegenerative disease classification, neurocritical care monitoring, and clinical decision support. Many studies report high performance using metrics such as accuracy, sensitivity, specificity, or area under the ROC curve. Yet the number of systems with robust evidence of real-world clinical benefit remains comparatively small. This translational gap reflects recurrent methodological and implementation problems: reliance on narrowly characterized public datasets, limited demographic and technical diversity, insufficient external validation, incomplete calibration assessment, and evaluation strategies that do not map onto clinically meaningful decisions.These problems are especially consequential in neurology, where AI is often intended to support time-sensitive, high-stakes, workflow-dependent decisions. In continuous EEG monitoring, aggregate discrimination metrics tell only part of the story; false alarms per hour, detection latency, alert burden, and interpretability may decide whether a system is usable at all. In stroke imaging, models trained under restricted scanner, protocol, or institutional conditions can degrade across heterogeneous environments. In neurodegenerative disease classification, validity depends heavily on cohort definitions, selection criteria, disease stage, comorbidity patterns, and how representative the training and validation populations are. More recently, large language models and multimodal foundation models have added challenges around hallucination, calibration, reproducibility, safety boundaries, clinician oversight, and regulatory accountability.The goal of this Research Topic is to advance a more deployment-oriented evidence standard for neurological AI. Rather than rewarding benchmark optimization alone, it seeks studies that test whether AI systems are valid, generalizable, clinically useful, safe, and appropriately reported for their intended use in real-world neurological settings. We are especially interested in work that examines generalization limits, replication attempts, external validation failures, post-deployment performance changes, and pre-specified clinical thresholds — including negative or null findings when methodologically robust. By giving equal weight to positive evidence and well-characterized limitations, the collection aims to support a more mature and clinically responsible phase of the field, in which performance is interpreted in relation to clinical context, deployment conditions, and patient-relevant impact.We welcome Original Research, Methods, Systematic Reviews, Policy and Practice Reviews, Study Protocols, and FAIR² Data Articles that substantively address at least one of the following themes, including but not limited to:- Dataset validity and external validation: critical appraisals of widely used neurological datasets; studies of representativeness, selection bias, data leakage, acquisition heterogeneity, and annotation quality; multi-center, multi-vendor, or multi-population validation; subgroup performance across demographic, clinical, and technical variables; and distribution shift, temporal drift, calibration, and generalizability in target deployment populations.- Clinically meaningful evaluation: assessment tied to defined clinical use cases rather than aggregate discrimination alone — false alarms per hour, alert burden, detection latency, time-to-detection, workload impact, calibration, decision-curve analysis, net benefit, pre-specified clinical thresholds, patient-relevant outcomes, and usability or workflow compatibility.- Reporting, governance, and deployment readiness: application, adaptation, or critique of AI-specific frameworks (TRIPOD-AI, STARD-AI, DECIDE-AI, CLAIM, CONSORT-AI, SPIRIT-AI, and related guidance); pre-deployment safety cases, post-deployment monitoring, model updating, accountability, clinician oversight, regulatory considerations, fairness, human–AI interaction, and the organizational requirements for safe integration.Clinical areas of interest include, but are not limited to: electrophysiology and continuous neurological monitoring (EEG interpretation, seizure detection and forecasting, status epilepticus monitoring, ICU neuromonitoring, sleep medicine); stroke and neuroimaging AI (acute stroke triage, large-vessel occlusion detection, perfusion analysis, structural neuroimaging, scanner/protocol heterogeneity); neurodegenerative and movement disorders (disease classification, progression modeling, digital biomarkers, multimodal risk stratification, real-world cohort validity); and foundation models and large language models in clinical neurology (multimodal and generative applications, hallucination characterization, calibration, safety boundaries, clinical reliability, and evaluation frameworks).Negative, null, replication, and failed-deployment studies are welcome when methodologically rigorous and clinically informative, as are pre-registered evaluation protocols, study protocols, methods papers, policy analyses, and dataset-focused contributions. Submissions based primarily on internal validation or retrospective benchmark aggregation should clearly justify their clinical relevance and, where possible, include external validation, calibration assessment, clinically meaningful performance metrics, or a defined pathway toward real-world evaluation. Topic editors: Shahab Haghayegh, Neeharika Krothapalli Source: https://www.frontiersin.org/research-topics/82642/trustworthy-neurological-ai-evidence-standards-from-validation-to-clinical-deployment