Clinician perspectives on explainability in AI-driven closed-loop neurotechnology

• Clinician needs for explainability in closed-loop neurotech (e.g. adaptive stimulation, BCIs) affect trust and adoption.¹
• Design requirements for clinical AI-driven closed-loop systems are identified in a Nature paper, with tier-1/2 relevance for deployment and regulation.^{1 1}

Weekly enrichment (2026-07-20)

• The underlying study (Ienca and colleagues, published in Nature’s Scientific Reports, 2025) used semi-structured interviews with 20 clinicians—13 neurologists, 5 neurosurgeons, and 2 psychiatrists—based in Germany (n=16) and Switzerland (n=4), analyzed with reflexive thematic analysis; interviews ran August 2024–January 2025 and lasted 20–43 minutes (mean 34).^{2 3}
• A consistent theme was limited clinician interest in technical specifications: 9 of 20 explicitly saw little value in details such as algorithm type, number of layers, or parameter counts.²
• Clinicians voiced their strongest concerns (11 of 20) about system outputs—safety, patient benefit, and clinical relevance—and 7 of 20 emphasized hard safety boundaries for systems that autonomously adjust stimulation (e.g., responsive neurostimulation for seizure detection).²
• Only 3 of 20 spontaneously requested formal explainable-AI methods (feature importance, counterfactuals), whereas 9 of 20 valued descriptive statistics and visual summaries of the training data to judge dataset representativeness.²
• The authors propose hybrid explainability—combining statistical techniques such as SHAP values and saliency maps with case-based reasoning (similar patient trajectories)—delivered through adaptive interfaces that surface explanation depth according to user expertise.^{2 4}
• Several participants likened AI-driven neurotech to conventional deep brain stimulation for Parkinson’s disease, which is trusted and effective despite incomplete mechanistic understanding.²
• Reported limitations include lack of statistical generalizability and low female participation: only 2 of 26 women contacted enrolled (~8%), versus a 17% participation rate among men.²
• Findings are framed as directly informing technology design, regulatory frameworks, and ethical guidelines for clinically robust, socially aligned neurotechnology; corresponding author Marcello Ienca is at TU Munich.^{2 3}

Footnotes

1. https://news.google.com/rss/articles/CBMiX0FVX3lxTE9McWNjdGZRd3I0Vjkwc3YwMF9RNzZ2S3pHczZNNklCdFEzUzdvQzFEakt5VDRDOW96ZUQwVktYWW1WQnpBUFJhTk9YdjNUOXZieE8zQ01JMWRPTk9XNW1Z?oc=5 ↩ ↩² ↩³

2. https://www.nature.com/articles/s41598-025-19510-9 ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸

3. https://doi.org/10.1038/s41598-025-19510-9 ↩ ↩²

4. https://arxiv.org/pdf/2603.18028 ↩