What does this research mean for the field?

While AI-driven digital biomarkers show promise for the early, non-invasive detection of neurological disorders, current evidence is largely retrospective and proof-of-concept, requiring prospective external validation before broad clinical deployment. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

September 2, 2026DiagnosticsOpen Access

Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review

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ARArshad Husain Rahmani TSTarique Sarwar

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Overview

Narrative review reveals potential of AI-driven digital biomarkers for early detection of neurological disorders, highlighting that broad clinical deployment requires prospective external validation.

Key Points

Synthesize current evidence on artificial intelligence and digital biomarkers for early detection and monitoring of neurological disorders, evaluating clinical utility and implementation barriers.
Conducted a narrative review synthesizing literature on AI and machine learning applied to digital biomarkers, including eye tracking, facial expressions, speech analysis, electrophysiology, and wearable sensing.
Analyzed disease-specific applications across conditions such as Alzheimer’s disease, Parkinson’s disease, and epilepsy alongside evaluation of validation designs and clinical workflows.
Digital biomarkers and AI models demonstrate promising non-invasive capabilities for identifying presymptomatic changes in neurological conditions.
Most available performance metrics are restricted to retrospective, case-control, or internally validated datasets, serving as proof-of-concept rather than proof of clinical readiness.
Successful clinical translation requires prospective external validation, integration with disease-modifying therapies, and advances in federated learning and explainable AI.

Cite This Study

Rahmani et al. (2026) studied this question.

synapsesocial.com/papers/6a98131ec562ede874ec7bfc https://doi.org/10.3390/diagnostics16172799

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September 2, 2026DiagnosticsOpen Access

Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review

View Full Paper

Ask AI

Bookmark

Authors

ARArshad Husain Rahmani TSTarique Sarwar

Discussion

Member takes

Overview

Narrative review reveals potential of AI-driven digital biomarkers for early detection of neurological disorders, highlighting that broad clinical deployment requires prospective external validation.

Key Points

Synthesize current evidence on artificial intelligence and digital biomarkers for early detection and monitoring of neurological disorders, evaluating clinical utility and implementation barriers.
Conducted a narrative review synthesizing literature on AI and machine learning applied to digital biomarkers, including eye tracking, facial expressions, speech analysis, electrophysiology, and wearable sensing.
Analyzed disease-specific applications across conditions such as Alzheimer’s disease, Parkinson’s disease, and epilepsy alongside evaluation of validation designs and clinical workflows.
Digital biomarkers and AI models demonstrate promising non-invasive capabilities for identifying presymptomatic changes in neurological conditions.
Most available performance metrics are restricted to retrospective, case-control, or internally validated datasets, serving as proof-of-concept rather than proof of clinical readiness.
Successful clinical translation requires prospective external validation, integration with disease-modifying therapies, and advances in federated learning and explainable AI.

Cite This Study

Rahmani et al. (2026) studied this question.

synapsesocial.com/papers/6a98131ec562ede874ec7bfc https://doi.org/10.3390/diagnostics16172799

View Full Paper

Ask AI

Bookmark