Latest AI and machine learning research in patient safety / risk management for healthcare professionals.
Scientific machine-learning (SciML) surrogates approximate expensive simulations, but exact expected outputs for arbitrary inputs are unavailable (the oracle problem). Metamorphic testing checks relations across executions, yet a candidate relation is not automatically valid: its preconditions, output mapping, and the numerical floor of the scoring operator determine whether a violation is meaning...
Synthetic and distilled student data are increasingly used to enable privacy-conscious learning analytics, yet their suitability for decision-facing institutional support remains uncertain. In dropout support, generated data must preserve not only predictive utility or distributional resemblance, but also the financial-status evidence used to guide advising, payment-plan assistance, and scholarshi...
Suicidal risk may be encoded in everyday communication patterns but diluted in routine digital interactions. We introduce a method for surfacing this ...
Artificial intelligence (AI) tools have been rapidly adopted by medical researchers, yet whether early career researchers in low and middle income cou...
Posttraumatic stress disorder (PTSD) is a prevalent and debilitating mental health condition with significant personal and societal impacts. Current c...
Background: With growing impetus to integrate artificial intelligence (AI) tools into radiology, clinical practices must navigate workflow redesign. T...
Text-based counseling is an important interface for AI mental-health support, where transcripts may be used to monitor depression severity and flag se...
CLIP-style contrastive pretraining typically curates web-scale image-text pairs using sample-level filtering signals, often based on pair-level alignm...
Foundation diffusion models can generate photorealistic natural images, but adapting them to medical imaging remains challenging. In medical adaptatio...
Standardized patients (SPs) are central to clinical communication training but are constrained by cost, scalability, and reliance on trained actors. W...
Recent image editing models have achieved strong visual fidelity but often struggle with tasks requiring complex reasoning. To investigate and enhance...
Recent text-to-image (T2I) models have demonstrated impressive capabilities in photorealistic synthesis and instruction following. However, their reli...
Clinical research involves labor-intensive processes such as study design, cohort construction, model development, and documentation, requiring domain...
The effectiveness of Direct Preference Optimization (DPO) depends on preference data that reflect the quality differences that matter in multimodal ta...
Target trial emulation (TTE) enables causal inference from observational data but remains bottlenecked by manual, expert-dependent protocol operationa...
Computer-use agents hold the promise of assisting in a wide range of digital economic activities. However, current research has largely focused on sho...
Recent advances in image generation models have expanded their applications beyond aesthetic imagery toward practical visual content creation. However...
In recent years, progress in medical informatics and machine learning has been accelerated by the availability of openly accessible benchmark datasets...
Background: Statistical Analysis Plans (SAPs) are essential for trial transparency and credibility but are resource-intensive to produce. While Large ...
Background: The administrative burden of clinical documentation is a recognised contributor to clinician burnout and diminished care quality. Ambient ...