Latest AI and machine learning research in clinical trials for healthcare professionals.
Background: Hospital incident risk scoring has long relied on two- or three-dimensional frameworks (Severity Assessment Codes or Risk Priority Numbers),even though root cause analysis standards recognize that clinical risk is multi-factorial. The obstacle has been mainly cognitive: human reviewers cannotreliably score many dimensions across high incident volumes, so richer assessmenthas not been o...
Background: Adverse drug events (ADEs) are a critical indicator of patient safety but are often documented only in free-text clinical notes. The potential of recent advances in natural language processing (NLP), particularly generative large language models (LLMs), to identify ADEs remains understudied. This study aimed to compare the performance of multiple LLMs in identifying ADE-Drug relationsh...
Insulin therapy for type 1 diabetes requires continual dose adjustment to meals, activity, stress, illness, and changing insulin sensitivity, creating...
Impaired learning that both novel and previously dangerous stimuli are safe (safety and extinction learning, respectively) are long standing, robust, ...
Background & Aims: Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) is the leading cause of chronic liver disease in children. However...
Testing object detectors in safety-critical domains requires semantically meaningful probes beyond pixel-level corruptions. We present SemProbe, a too...
Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specif...
Urgent suspected colorectal cancer (CRC) referrals create operational bottlenecks because semi-structured clinical documents often require manual revi...
Recommender systems generally optimises user engagement, but this approach is dangerous in mental health contexts. When vulnerable users show signs of...
Clinical AI systems have achieved strong predictive performance; however, prediction accuracy is not sufficient for clinical safety. Retrieval-augment...
Background Community-wide active case-finding (ACF) is being increasingly implemented as a tuberculosis (TB) elimination intervention. However, conven...
Background: Professionalism and effective communication are foundational determinants of patient safety and quality of care. Unprofessional behaviors ...
Accurate modeling of aerodynamic loads is essential for understanding and predicting the responses of complex structural systems. However, these model...
Background Patients worldwide receive healthcare in many languages, yet medical AI systems are validated almost exclusively in high-resource languages...
Rhythmic temporal structure improves working memory, but how this benefit emerges from recurrent dynamics remains unclear. Here, we trained excitatory...
Background: Heterogeneity in symptom presentation and treatment response in irritable bowel syndrome (IBS) remains poorly understood. The gut microbio...
Background: Large language models (LLMs) demonstrate strong performance in controlled medical environments such as multiple choice exams, but their ut...
Background: Large language models (LLMs) are increasingly used in telehealth, but their safety in antibiotic prescribing remains uncertain, particular...
Machine-learning surrogate models are positioned to help optimize deep brain stimulation (DBS) usage by predicting neural activation in response to el...
Background: Mental health systems face escalating demand that exceeds clinician capacity, making accurate severity-based triage a critical bottleneck....