Artificial Intelligence Medical Compendium

Background Large-scale retrospective studies often require researchers to adjudicate outcomes or cohort eligibility from unstructured clinical records. Manual review can provide reliable labels but is difficult to scale. We developed and evaluated a ... read more 

Background: Passively collected smartphone and wearable data, combined with genomic information, may enable scalable early identification of youth who are at risk for substance use disorder. However, heterogeneous data collected in naturalistic setti... read more 

Context Current guidelines for primary aldosteronism (PA) recommend screening all patients with hypertension. Given low screening rates, an automated artificial intelligence (AI)-based risk-stratified approach may facilitate guideline implementation.... read more 

Background: Clinical registries and institutional quality control (QC) in pathology heavily depend on complex histopathological data. However, critical tumor characteristics are mostly locked in unstructured free-text reports. We hypothesize that loc... read more 

Objectives To examine the prevalence of interruptions during UK secondary care outpatient consultations, their association with clinician mental workload, and the strategies clinicians use to manage them. Design Nationwide cross-sectional online surv... read more 

Background: Social vulnerability is associated with adverse health outcomes, but existing social vulnerability indices often rely on cohort-based and self-reported data, potentially limiting their population-level applicability. Therefore, this study... read more 

Foundation models for colour fundus photography (CFP) are candidate visual encoders for multimodal medical AI, but existing models are large and are assessed under fine-tuning rather than the frozen-encoder conditions such systems impose. We present ... read more 

Medical large language models (LLMs) have shown promise in medical knowledge retrieval and clinical reasoning. However, their capacity for quantitative reasoning over dense, longitudinal health data remains limited, largely due to the lack of effecti... read more 

Computer vision approaches to disease motor assessment are limited by clinical video dataset scarcity and distributional imbalance, motivating interest in generative video models as a potential source of training data. We introduce a three-component ... read more 

Background. Large language models (LLMs) are increasingly used to automate title-abstract screening in systematic reviews, but validation studies typically report sensitivity, specificity, or Cohen's kappa: metrics that are unstable under the low and... read more