Clinician perspectives on explainability in AI-driven closed-loop neurotechnology
Closed-loop neurotechnology is moving from promise to practice, but explainability remains a sticking point. A new qualitative study of 20 German- and Swiss-based clinicians surfaces a clear takeaway: clinicians don't want a tour of the model's internals-they want clinically meaningful signals that are safe, actionable, and aligned with outcomes.
Why this matters
In high-stakes systems that modulate the brain in real time, opacity is more than a technical nuisance. It affects safety, oversight, and trust. Regulatory shifts (e.g., the EU AI Act) now expect "meaningful information" for high-risk AI, making it vital to define what "meaningful" looks like in neurotechnology.
Study at a glance
Interviews with 20 clinicians (neurology, neurosurgery, psychiatry) working with closed-loop systems explored what explanations they find useful. Using reflexive thematic analysis, the study mapped priorities across inputs (training data), algorithms, outputs (decisions/actions), and user interfaces.
What clinicians actually want explained
- Inputs (training data): Representativeness to their patient population; clarity on inclusion of multimodal sources (wearables, video, patient-reported outcomes); evidence of artifact handling; sample size and variability; and when possible, access to source data for independent inspection.
- Algorithms: Low interest in architecture details (layers, parameters). High expectation that engineers, manufacturers, and regulators handle deep validation and safety. Transparency at the system level matters more than model internals.
- Outputs (decisions/actions): Safety limits and operational logic (how a signal changes stimulation). Hard boundaries on autonomous adjustments. Trust increases when outputs align with clinical reasoning and show real patient benefit in trials, not just technical metrics.
Interface expectations
- Data transparency via visuals: Descriptive statistics, charts, and symptom-cluster views to judge patient-dataset fit.
- Grounding in evidence: Links to peer-reviewed sources that support a given recommendation to help users verify medical rationale.
- Explainability tools, selectively: Feature importance, relevance rankings, and counterfactuals for targeted use. Some clinicians still prefer paper-based summaries for complex cases-flexibility is key.
- Practical intelligibility over full transparency: Visualization helps, but the goal is safe, confident use-not exposing every model detail.
Key risks clinicians flagged
- Epiphenomena and spurious proxies: Models can latch onto statistically strong but clinically meaningless signals. Explanations must help distinguish correlation from plausible mechanisms.
- Dual-layer opacity in neurotech: Inputs themselves (e.g., LFPs, EEG) are not inherently interpretable to many clinicians, compounding the model's black box.
- Overhype: Interest is high, but expectations are tempered. Benefits must be shown in realistic clinical studies, not only in lab settings.
Design principles you can act on
- Prioritize functional intelligibility: Show how inputs map to outputs and patient-relevant endpoints; avoid deep dives into architecture unless required for safety cases.
- Build hard guardrails: Enforce safe operating ranges for autonomous parameter updates; surface these limits in the UI.
- Adopt hybrid explainability: Combine feature importance and saliency with case-based examples (similar patient trajectories) and counterfactuals.
- Lean into multimodal data: Fuse neural signals with wearables, video, and patient-reported outcomes to improve clinical relevance.
- Be artifact-aware: Show preprocessing steps and quality indicators so users know the model learned from signal, not noise.
- Expose uncertainty: Provide confidence or reliability scores alongside recommendations.
- Keep clinicians in the loop: Enable overrides, audit trails, and clear handoffs between autonomous and clinician-controlled modes.
- Adaptive interfaces: Let users drill down from high-level justification to data evidence; support both digital dashboards and printable summaries.
- Two-way explanations: Where appropriate, conversational tools can translate technical outputs into clinical language without overpromising.
Governance and policy notes
Expectations for high-risk AI include human oversight, safety, and the right to meaningful information. For context, see the EU AI Act's provisions on explanation and oversight: Article 86.
- Tiered explainability: Different stakeholders need different views-clinicians (functional clarity), regulators (assurance and accountability), patients (rights-based explanations).
- Data access incentives: Balanced frameworks for sharing deidentified datasets can strengthen validation while respecting IP and privacy law.
- Clinical accountability: AI-generated parameters must meet the same scrutiny as other clinical interventions.
- Independent validation and monitoring: Real-world performance, safety events, and drift should be continuously evaluated.
Limitations and scope
The sample was limited to 20 German-speaking clinicians, with uneven gender participation and varied AI literacy. Findings are qualitative and not statistically generalizable. This work identifies end-user requirements; it is not a proof-of-concept for specific AI or XAI methods.
What's next
- Translate these requirements into testable metrics, benchmarks, and interface patterns.
- Run co-designed pilots with clinicians to validate usability and safety under real constraints.
- Include patient perspectives, especially for autonomous adjustments that affect mental or physical integrity.
- Leverage new implantable devices for longitudinal, real-world datasets and prospective trials.
Deidentified quotes supporting the themes are available on Zenodo: https://doi.org/10.5281/zenodo.16528872.
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