Latest AI and machine learning research in clinical trials for healthcare professionals.
Large language models (LLMs) increasingly guide clinical decisions through population-level evidence, yet they cannot encode individual patient preferences. When treatments yield comparable outcomes, patient choice may drive decisions, though its effect remains unquantified. The Spine Patient Outcomes Research Trial (SPORT), marked by similar surgical and nonoperative results and substantial cross...
The rapid evolution of embodied agents has accelerated the deployment of household robots in real-world environments. However, unlike structured industrial settings, household spaces introduce unpredictable safety risks, where system limitations such as perception latency and lack of common sense knowledge can lead to dangerous errors. Current safety evaluations, often restricted to static images,...
Mechanical ventilation (MV) is a life-saving intervention for patients with acute respiratory failure (ARF) in the ICU. However, inappropriate ventila...
More than one third of adults with diabetes can experience diabetes distress due to the demands of daily self-care. As a cognitive therapy, mindfulnes...
Purpose: Large language models (LLMs) offer significant potential for automating the classification of clinical trials by eligibility criteria. Howeve...
Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Trans...
Background: Cardiovascular disease (CVD) readmissions impose substantial clinical and economic burden. Machine learning (ML) may improve risk stratifi...
Large healthcare institutions typically operate multiple business intelligence (BI) teams segmented by domain, including clinical performance, fundrai...
Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Trans...
Multimodal large language models (MLLMs) face safety misalignment, where visual inputs enable harmful outputs. To address this, existing methods requi...
Although autonomous driving systems demonstrate high perception performance, they still face limitations when handling rare situations or complex road...
Multiple sclerosis (MS) is a chronic neurodegenerative disease characterised by progressive neurological disability and heterogeneous symptom trajecto...
Background: Large language models (LLMs) are increasingly deployed in medical contexts as patient-facing assistants, providing medication information,...
Patient Activity Recognition (PAR) in clinical settings uses activity data to improve safety and quality of care. Although significant progress has be...
Purpose: Large language models (LLMs) are used for biomedical text processing, but individual decisions are often hard to audit. We evaluated whether ...
Diffusion-weighted imaging (DWI) can support lesion detection and characterization in breast magnetic resonance imaging (MRI), however especially high...
Background: Cardiovascular disease remains the leading cause of global morbidity and mortality. The original My Heart Counts smartphone application de...
Safety evaluation and red-teaming of large language models remain predominantly text-centric, and existing frameworks lack the infrastructure to syste...
As artificial intelligence systems move toward clinical deployment, ensuring reliable prediction behavior is fundamental for safety-critical decision-...
Platelets expire within five days. Blood banks face uncertain daily demand and must balance ordering decisions between costly wastage from overstockin...