Selected work

Representative Papers

Measuring the practice of shared-decision making (OPTION12): An Investigation into Open-sourced Smaller LLMs (OS-sLLMs) for Better Privacy and Sustainability

Jul 07, 2026

This study explores the feasibility of using locally deployed, small open-source language models to automatically evaluate shared decision-making (SDM) in clinical settings while preserving privacy and sustainability. Grounded in the Observer OPTION12 framework, we assess multiple general-purpose and medical-domain small language models on Dutch melanoma consultation transcripts and introduce, for the first time, a Judge-LLM multi-model consensus mechanism to resolve scoring discrepancies. Experimental results indicate that general-purpose models outperform medical-specific ones, with Gemma3:12b achieving the highest correlation with human ratings (Pearson r = 0.51, Spearman ρ = 0.59). This work presents a novel, privacy-preserving, on-premises approach to automated SDM assessment under the OPTION12 framework and reveals systematic model limitations in temporal reasoning, role attribution, and evidence anchoring.

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How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series

Jan 15, 2026

This study addresses the challenge of high computational cost and poor real-time performance in automated classification of neural muscular disorders using high-sampling-rate needle electromyography (nEMG) signals, where the impact of downsampling on diagnostic information retention remains unclear. The authors propose a unified evaluation framework that, for the first time, integrates shape-aware downsampling, feature space analysis, and classification performance to systematically quantify how different downsampling strategies affect waveform integrity and diagnostic capability in high-frequency temporal biosignals. Evaluated on a three-class neuromuscular disorder classification task, the framework identifies an optimal downsampling configuration that balances computational efficiency with preservation of diagnostic information. Results demonstrate that shape-aware downsampling significantly outperforms conventional methods, offering a generalizable analytical paradigm for efficient processing of high-frequency temporal biosignals.

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Out of the Box, into the Clinic? Evaluating State-of-the-Art ASR for Clinical Applications for Older Adults

Aug 12, 2025

This study evaluates the applicability of general-purpose multilingual automatic speech recognition (ASR) models to clinical voice interactions in Dutch spoken by older adults—specifically, the Welzijn.AI healthcare chatbot—addressing the underrepresentation of elderly populations in ASR development. Method: Using real-world speech data from older Dutch speakers, we compare a zero-shot multilingual ASR model against a version fine-tuned specifically on elderly Dutch speech, evaluating performance via word error rate (WER), inference latency, and hallucination frequency. Contribution/Results: Contrary to expectations, the unadapted multilingual ASR outperformed the elderly-specific fine-tuned variant across all metrics. Structural pruning enabled a 2.1× speedup in inference while preserving high accuracy (WER ≤12.3%). Hallucinations—particularly prevalent under low signal-to-noise ratio conditions—emerged as the dominant error source. To our knowledge, this is the first systematic empirical validation of off-the-shelf multilingual ASR for geriatric clinical voice interfaces, demonstrating both its plug-and-play viability and an effective lightweight optimization pathway for resource-constrained deployment in elderly healthcare AI systems.

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Recent publications

Latest Papers

Measuring the practice of shared-decision making (OPTION12): An Investigation into Open-sourced Smaller LLMs (OS-sLLMs) for Better Privacy and Sustainability

Jul 07, 2026

This study explores the feasibility of using locally deployed, small open-source language models to automatically evaluate shared decision-making (SDM) in clinical settings while preserving privacy and sustainability. Grounded in the Observer OPTION12 framework, we assess multiple general-purpose and medical-domain small language models on Dutch melanoma consultation transcripts and introduce, for the first time, a Judge-LLM multi-model consensus mechanism to resolve scoring discrepancies. Experimental results indicate that general-purpose models outperform medical-specific ones, with Gemma3:12b achieving the highest correlation with human ratings (Pearson r = 0.51, Spearman ρ = 0.59). This work presents a novel, privacy-preserving, on-premises approach to automated SDM assessment under the OPTION12 framework and reveals systematic model limitations in temporal reasoning, role attribution, and evidence anchoring.

0 citationsRead paper

How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series

Jan 15, 2026

This study addresses the challenge of high computational cost and poor real-time performance in automated classification of neural muscular disorders using high-sampling-rate needle electromyography (nEMG) signals, where the impact of downsampling on diagnostic information retention remains unclear. The authors propose a unified evaluation framework that, for the first time, integrates shape-aware downsampling, feature space analysis, and classification performance to systematically quantify how different downsampling strategies affect waveform integrity and diagnostic capability in high-frequency temporal biosignals. Evaluated on a three-class neuromuscular disorder classification task, the framework identifies an optimal downsampling configuration that balances computational efficiency with preservation of diagnostic information. Results demonstrate that shape-aware downsampling significantly outperforms conventional methods, offering a generalizable analytical paradigm for efficient processing of high-frequency temporal biosignals.

0 citationsRead paper

Out of the Box, into the Clinic? Evaluating State-of-the-Art ASR for Clinical Applications for Older Adults

Aug 12, 2025

This study evaluates the applicability of general-purpose multilingual automatic speech recognition (ASR) models to clinical voice interactions in Dutch spoken by older adults—specifically, the Welzijn.AI healthcare chatbot—addressing the underrepresentation of elderly populations in ASR development. Method: Using real-world speech data from older Dutch speakers, we compare a zero-shot multilingual ASR model against a version fine-tuned specifically on elderly Dutch speech, evaluating performance via word error rate (WER), inference latency, and hallucination frequency. Contribution/Results: Contrary to expectations, the unadapted multilingual ASR outperformed the elderly-specific fine-tuned variant across all metrics. Structural pruning enabled a 2.1× speedup in inference while preserving high accuracy (WER ≤12.3%). Hallucinations—particularly prevalent under low signal-to-noise ratio conditions—emerged as the dominant error source. To our knowledge, this is the first systematic empirical validation of off-the-shelf multilingual ASR for geriatric clinical voice interfaces, demonstrating both its plug-and-play viability and an effective lightweight optimization pathway for resource-constrained deployment in elderly healthcare AI systems.

0 citationsRead paper