ArticleJournal of neuroengineering and rehabilitation2026
Explainability in AI-enabled medical neurotechnology: a scoping review.
Article in Journal of neuroengineering and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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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.
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Who cites it
2 citing papers in PubMed.
- Closed-loop brain-computer interfaces for post-stroke sensorimotor loop restoration.Frontiers in neuroscience · 2026Review
- Interpretable side-aware kinematic-sEMG gait-state representations relevant to adaptive neurorobotic assistance after stroke: a public-dataset study.Frontiers in neurorobotics · 2026Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) approaches, including Machine Learning (ML), and other complex algorithms, are driving progress in medical closed-loop neurotechnology, including neurostimulation systems and brain–computer interfaces (BCIs). These advances are transforming the treatment landscape for neurological and psychiatric conditions. However, the inherent opacity of many AI models raises clinical, epistemological and ethical challenges. Explainability is widely recognized as a critical requirement for addressing these challenges, yet its concrete application in neurotechnology remains insufficiently explored. Objective. This scoping review maps how Explainable AI (XAI) methods are implemented in AI-enabled closed-loop neurotechnologies and examines how explainability is conceptualized and operationalized in this domain. Approach. Following JBI guidance and PRISMA-ScR, we systematically searched five databases for original research on AI-enabled medical closed-loop neurotechnologies targeting neurological and psychiatric conditions. A total of 161 studies were included and analyzed using descriptive statistics and qualitative content analysis to identify the presence and framing of XAI methods. Main results. Explainable AI adoption in medical neurotechnology is limited: only 14 studies (9%) employed explicit XAI techniques. Thematic analysis of the full corpus identified three recurring barriers and focal points: (A) technical constraints and proposed workarounds, (B) challenges in explanation quality and system transparency, and (C) the relationship between explainability and user trust. Significance. Although closed-loop neurotechnologies are rapidly advancing, explainability is rarely implemented in practice, constraining transparency, accountability, and clinical usability. Our findings reveal key factors behind the explainability gap and provides a framework to guide future research and development. Addressing this shortfall is essential for fostering ethically sound, clinically effective, and patient-trusted neurotechnological applications.
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Registered trials
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