Clinically Meaningful Explainability for NeuroAI: An ethical, technical, and clinical perspective
#NeuroAI #explainability #clinical #ethics #transparency #healthcare #AI bias #patient safety
📌 Key Takeaways
- NeuroAI explainability must be clinically meaningful to ensure ethical use in healthcare.
- Explainability involves balancing technical feasibility with clinical utility and patient safety.
- Ethical considerations include transparency, accountability, and avoiding bias in AI decisions.
- A multidisciplinary approach integrating ethics, technology, and clinical practice is essential.
📖 Full Retelling
arXiv:2603.18028v1 Announce Type: cross
Abstract: While explainable AI (XAI) is often heralded as a means to enhance transparency and trustworthiness in closed-loop neurotechnology for psychiatric and neurological conditions, its real-world prevalence remains low. Moreover, empirical evidence suggests that the type of explanations provided by current XAI methods often fails to align with clinicians' end-user needs. In this viewpoint, we argue that clinically meaningful explainability (CME) is e
🏷️ Themes
AI Ethics, Healthcare Technology
Entity Intersection Graph
No entity connections available yet for this article.
Original Source
arXiv:2603.18028v1 Announce Type: cross
Abstract: While explainable AI (XAI) is often heralded as a means to enhance transparency and trustworthiness in closed-loop neurotechnology for psychiatric and neurological conditions, its real-world prevalence remains low. Moreover, empirical evidence suggests that the type of explanations provided by current XAI methods often fails to align with clinicians' end-user needs. In this viewpoint, we argue that clinically meaningful explainability (CME) is e
Read full article at source