Selected work

Representative Papers

ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation

Aug 06, 2026

This work proposes ECG-LENS, an end-to-end framework for multi-lead electrocardiogram (ECG) report generation aimed at alleviating clinician workload and improving diagnostic efficiency. The approach integrates a lead-aware encoder with global dependency modeling, augmented by a clinical terminology–enhanced textual prompting mechanism and an ECG-specific report preprocessing strategy to enable diagnosis-aware, context-guided text generation. Additionally, the authors introduce F1-ECGBERT, a BERT-based evaluation metric tailored to ECG report assessment. Evaluated on the PTB-XL and MIMIC-IV-ECG datasets, the model substantially outperforms existing methods, achieving relative improvements of 4.0% in METEOR, 6.3% in ROUGE-L, and 11.5% in F1-ECGBERT.

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A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models

Jun 25, 2026

This work addresses the scarcity of real-world clinical text due to privacy constraints, which hinders the development of clinical AI systems. The authors propose a modular synthetic data generation pipeline that integrates structured patient modeling, semi-structured clinical course simulation, and large language model (LLM)-driven generation of unstructured clinical notes. This approach ensures longitudinal consistency and clinical plausibility while enabling diverse writing styles. Innovatively, the framework incorporates an LLM-based validation and refinement mechanism to enhance the fidelity and realism of the synthetic data. The study releases a benchmark dataset comprising 70 virtual patients, each with 20–50 clinical notes spanning their entire hospitalization, offering a high-quality, scalable resource for developing and evaluating clinical AI tools.

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Integrating Explainable AI in Medical Devices: Technical, Clinical and Regulatory Insights and Recommendations

May 10, 2025

The opacity of black-box AI models in medical devices undermines clinical trust and poses regulatory compliance risks due to insufficient explainability. Method: This study pioneers a systematic integration of eXplainable Artificial Intelligence (XAI) techniques, empirical clinical human–AI interaction research, and regulatory requirements—yielding an XAI integration pathway and tiered validation framework tailored to real-world clinical workflows. Leveraging multimodal expert consensus, in situ clinical behavioral observation, AI explanation evaluation, and MHRA regulatory alignment analysis, we developed a comprehensive XAI implementation guideline spanning development, verification, and deployment phases. Contribution/Results: The guideline was formally adopted by the UK’s Medicines and Healthcare products Regulatory Agency (MHRA) as an official regulatory reference, demonstrably enhancing clinical acceptance and risk controllability. It establishes a methodological paradigm and practical standard for deploying trustworthy, clinically viable AI in healthcare.

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Recent publications

Latest Papers

ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation

Aug 06, 2026

This work proposes ECG-LENS, an end-to-end framework for multi-lead electrocardiogram (ECG) report generation aimed at alleviating clinician workload and improving diagnostic efficiency. The approach integrates a lead-aware encoder with global dependency modeling, augmented by a clinical terminology–enhanced textual prompting mechanism and an ECG-specific report preprocessing strategy to enable diagnosis-aware, context-guided text generation. Additionally, the authors introduce F1-ECGBERT, a BERT-based evaluation metric tailored to ECG report assessment. Evaluated on the PTB-XL and MIMIC-IV-ECG datasets, the model substantially outperforms existing methods, achieving relative improvements of 4.0% in METEOR, 6.3% in ROUGE-L, and 11.5% in F1-ECGBERT.

0 citationsRead paper

A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models

Jun 25, 2026

This work addresses the scarcity of real-world clinical text due to privacy constraints, which hinders the development of clinical AI systems. The authors propose a modular synthetic data generation pipeline that integrates structured patient modeling, semi-structured clinical course simulation, and large language model (LLM)-driven generation of unstructured clinical notes. This approach ensures longitudinal consistency and clinical plausibility while enabling diverse writing styles. Innovatively, the framework incorporates an LLM-based validation and refinement mechanism to enhance the fidelity and realism of the synthetic data. The study releases a benchmark dataset comprising 70 virtual patients, each with 20–50 clinical notes spanning their entire hospitalization, offering a high-quality, scalable resource for developing and evaluating clinical AI tools.

0 citationsRead paper

Integrating Explainable AI in Medical Devices: Technical, Clinical and Regulatory Insights and Recommendations

May 10, 2025

The opacity of black-box AI models in medical devices undermines clinical trust and poses regulatory compliance risks due to insufficient explainability. Method: This study pioneers a systematic integration of eXplainable Artificial Intelligence (XAI) techniques, empirical clinical human–AI interaction research, and regulatory requirements—yielding an XAI integration pathway and tiered validation framework tailored to real-world clinical workflows. Leveraging multimodal expert consensus, in situ clinical behavioral observation, AI explanation evaluation, and MHRA regulatory alignment analysis, we developed a comprehensive XAI implementation guideline spanning development, verification, and deployment phases. Contribution/Results: The guideline was formally adopted by the UK’s Medicines and Healthcare products Regulatory Agency (MHRA) as an official regulatory reference, demonstrably enhancing clinical acceptance and risk controllability. It establishes a methodological paradigm and practical standard for deploying trustworthy, clinically viable AI in healthcare.

0 citationsRead paper