This work introduces and evaluates a privacy-preserving knowledge distillation framework for LLM-based clinical modeling, using multimorbidity scoring as a healthcare task, and establishes a trustworthy, privacy-compliant pathway for large-scale healthcare applications of LLMs.
Rising societal and lifestyle complexity has been linked to a growing prevalence of mental distress worldwide. Educational institutions, workplaces, clinics, etc. collect large volumes of mental health survey data to understand and reduce this burden. Collaborative analysis of such data could yield effective generaliza...
MedAL is a scalable method for fine-tuning LLMs across many health systems without sharing patient-level data, enabling high-performance local models for reasoning over clinical notes and may also be useful for training multimodal healthcare AI models.
Ahmed Bakr, A. Garcia-Agundez, Travis Atkison et al.· Journal of Biomedical Inform...· 0 citations
Verified Extraction is introduced, an auditing framework that distinguishes identifiers attributable to fine-tuning data from spurious or prior-driven outputs and quantifies recoverable leakage under explicit query budgets.
Florent Pollet, Tong Wang, Rahul Gupta et al.· 0 citations
Clinical risk models seldom fail for want of a better algorithm. They fail because the records that would make them dependable sit behind institutional and statutory walls, because a single snapshot cannot describe a physiology that shifts within hours and because a clinician will not act on a number that arrives with...
G. D. Puri· Natural Resources for Human...· 0 citations
Explainable Artificial Intelligence (XAI) is increasingly important in healthcare, where transparent and trustworthy predictive models are essential for supporting clinical decision-making and safe adoption of data-driven systems. Local Interpretable Model-agnostic Explanations (LIME) is a widely used post-hoc, model-a...
Rehan Raza, Kok-Wai Wong, Hamid Laga et al.· IEEE journal of biomedical a...· 0 citations
Initial evaluations using various machine-learning algorithms on pre-and post-generalized datasets demonstrate the privacy framework’s effectiveness in mitigating privacy risks while preserving data usability.
Ze-Yang Zhu, Matthias N. Louws, Roland V. Bumbuc et al.· 0 citations
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