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New framework makes AI speech analysis for cognitive impairment clinically interpretable

By PulseAugur Editorial · [4 sources] ·

Researchers have developed a multi-stage explainability framework to make transformer-based models for speech-based cognitive impairment detection more interpretable for clinical use. This framework integrates SHAP-based token attribution and linguistic features with an LLM reasoning pipeline using LLaMA-3.1-70B-Instruct. The system, built on the SpeechCARE-Adaptive Gating Network, achieved an F1 score of 72.11% on the NIA PREPARE benchmark and demonstrated high potential for clinical workflow integration with a System Usability Scale score of 82/100. AI

IMPACT Enhances the interpretability of AI models in healthcare, potentially leading to wider clinical adoption of AI for cognitive impairment detection.

RANK_REASON The cluster contains an academic paper detailing a new framework and methodology.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New framework makes AI speech analysis for cognitive impairment clinically interpretable

COVERAGE [4]

  1. arXiv cs.AI TIER_1 English(EN) · Olivier Jiyoun Jung, Jonghyeon Park, Myungwoo Oh ·

    Listening Between the Lines: Joint Learning of ASR Embeddings and LLM-Augmented Linguistics for Dementia Detection

    arXiv:2606.30675v1 Announce Type: cross Abstract: Early detection of dementia through speech analysis offers a non-invasive screening alternative, but capturing both acoustic and linguistic biomarkers remains challenging. We propose a multimodal framework leveraging Whisper for d…

  2. arXiv cs.AI TIER_1 English(EN) · Jonghyeon Park, Olivier Jiyoun Jung, Myungwoo Oh ·

    LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features

    arXiv:2606.28445v1 Announce Type: cross Abstract: Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive screening modality. Conventional approaches often focus on a single representational dimension …

  3. arXiv cs.AI TIER_1 English(EN) · Yasaman Haghbin, Sina Rashidi, Ali Zolnour, Fatemeh Taherinezhad, Ali Fartoot, Hossein Azadmaleki, James M Noble, Maryam Dadkhah, Maryam Zolnoori ·

    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…

  4. arXiv cs.AI TIER_1 English(EN) · Maryam Zolnoori ·

    From Black-Box to Clinical Insight: A Multi-Stage Explainable Framework for Speech-Based Cognitive Impairment Detection

    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…

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