Ethical Challenges in Neurological Clinical Research in the Era of Artificial Intelligence and Digital Health: A Critical Review

Abstract

Artificial intelligence (AI) and digital health technologies are increasingly reshaping neurological clinical research by enabling more precise diagnostics, continuous patient monitoring, and data-driven therapeutic strategies. Applications such as machine learning–based neuroimaging analysis, wearable neurological sensors, remote electroencephalography (EEG) monitoring, brain–computer interfaces, and digital biomarkers are now widely explored in disorders including Alzheimer’s disease, Parkinson’s disease, epilepsy, stroke, and multiple sclerosis. Although these innovations have improved research efficiency and expanded opportunities for personalized neurology, they also introduce complex ethical and regulatory concerns that remain insufficiently addressed within existing research governance frameworks. This critical review examines the major ethical challenges associated with AI-enabled neurological research, with particular emphasis on informed consent in cognitively vulnerable populations, neurodata privacy and security, algorithmic bias, explainability of AI systems, digital inequities, and regulatory oversight. Unlike conventional healthcare datasets, neurological data may reveal highly sensitive information related to cognition, behavior, emotional processing, and personal identity, thereby raising unique neuroethical concerns regarding autonomy and mental privacy.
Drawing on recent literature published between 2021 and 2026, this review critically evaluates emerging ethical tensions in decentralized and AI-assisted neurology research while discussing practical approaches for responsible implementation. The paper argues that the future integration of AI into neurological clinical research will depend not only on technological advancement but also on the development of ethically resilient governance frameworks capable of protecting patient rights, ensuring transparency, minimizing bias, and maintaining public trust in increasingly data-intensive neurological ecosystems.