Latest AI and machine learning research in medicolegal for healthcare professionals.
Reliable explanations are important for trustworthy medical applications of artificial intelligence (AI), but attribution-based explanations can vary across model randomization and small analytic changes. We present NEXIM (Nash Equilibrium-based Explainability and Interpretability Model), implemented here as an accuracy-constrained, equilibrium-inspired model-selection framework that jointly evalu...
Background: Frailty is common in acute ischemic stroke (AIS) and predicts poor outcomes, but is not routinely captured in acute stroke care. Manual frailty tools are difficult to apply consistently in busy inpatient settings, while existing electronic frailty indices (eFIs) often rely on limited data modalities. We developed a scalable pre-stroke electronic frailty index (eFI) using multisource el...
Background Large Language Models (LLMs) are increasingly explored for pharmacovigilance tasks, including information extraction, case documentation, a...
Despite contributing substantially to clinician burnout, nursing documentation lacks empirical evidence distinguishing clinically essential from admin...
Vision Language Models (VLMs) have shown promising capabilities in medical image analysis by jointly understanding visual and textual information for ...
Background: Motor neuron disease (MND) is a fatal neurodegenerative condition with significant clinical heterogeneity that is incompletely captured by...
Purpose: To evaluate the performance of secure cloud-based large language models (LLMs) in extracting glaucoma diagnosis, type, and severity from free...
Background: Veterans face an elevated risk of suicide compared to the general population, motivating national efforts to develop predictive models tha...
Background: Suicide remains a significant and potentially preventable cause of death among United States veterans. Predictive models based on structur...
Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagn...
Question Are adverse childhood experiences (ACEs) associated with altered growth trajectories in childhood? Findings In this cohort study of 412,549 c...
Biology has accumulated a vast ecosystem of omics methods, but much of this ecosystem remains built for expert humans rather than scientific agents. M...
AI-assisted clinical documentation tools increasingly summarize, standardize, and reformat radiology reports using large language models (LLMs). We pr...
Agent skills are emerging as an important attack surface in LLM-based systems. Through an empirical study of existing skill scanners, we find that cur...
Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance bu...
Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance bu...
ONTOSIGHT(R) ADMETron is an AI-driven platform designed for rapid prediction and visualization of Absorption, Distribution, Metabolism, Excretion, and...
We develop a statistical learning theory for gradient boosting applied to the estimation of covariate-dependent Generalized Pareto (GP) distributions ...
Genome-scale metabolic models (GSMs) underpin pathway and strain engineering by linking genes to metabolic reactions and enabling system-level simulat...
In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practi...