From Black-Box to Clinical Insight: A Multi-Stage Explainable Framework for Speech-Based Cognitive Impairment Detection
arXiv:2606.27973v1 Announce Type: cross Abstract: Speech-based cognitive impairment detection offers a noninvasive, accessible alternative to costly biomarker assays, yet transformer-based models remain clinically uninterpretable. We propose a multi-stage explainability framework that translates black-box transformer predictions into clinically grounded narratives by integrating SHapley Additive exPlanations (SHAP)-based token attribution, theory-informed linguistic features, and a four-stage LLM reasoning pipeline using LLaMA-3.1-70B-Instruct. Built on the SpeechCARE-Adaptive Gating Network m
The rapid advancement of large language models and transformer architectures has created a need for interpreting their complex black-box predictions within critical domains like healthcare.
This development moves AI-based medical diagnostics from opaque predictions to clinically actionable insights, fostering trust and accelerating adoption in areas like cognitive impairment detection.
The ability to explain AI's reasoning in healthcare settings shifts the paradigm from 'AI as a black box' to 'AI as an explainable assistant,' directly impacting patient care and regulatory acceptance.
- · AI-driven diagnostic companies
- · Healthcare providers
- · Patients with cognitive impairments
- · Generative AI infrastructure providers
- · Traditional diagnostic methods
- · Companies offering uninterpretable AI solutions
Increased adoption of speech-based AI diagnostics for cognitive impairment due to improved interpretability.
Broader regulatory acceptance and integration of explainable AI frameworks across various medical AI applications.
The establishment of new clinical standards that mandate explainability for AI systems used in patient diagnosis and treatment planning.
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Read at arXiv cs.AI