Latest AI and machine learning research in patient safety / risk management for healthcare professionals.
Vision-language process reward models (VL-PRMs) are increasingly used to score intermediate reasoning steps and rerank candidates under test-time scaling. However, they often function as black-box judges: a low step score may reflect a genuine reasoning mistake or simply the verifier's misperception of the image. This entanglement between perception and reasoning leads to systematic false positive...
The use of synthetic data has become increasingly popular as a privacy-preserving alternative to sharing real datasets, especially in sensitive domains such as healthcare, finance, and demography. However, the privacy assurances of synthetic data are not absolute, and remain susceptible to membership inference attacks (MIAs), where adversaries aim to determine whether a specific individual was pre...
Infertility generates profound psychological and social distress for both women and men, yet mens communicative experiences remain comparatively under...
Large healthcare institutions typically operate multiple business intelligence (BI) teams segmented by domain, including clinical performance, fundrai...
We developed and validated a self-administered clinical vignette platform powered by a large language model (LLM), deployed through a SurveyCTO web su...
Recent methods for pathology report generation from whole-slide image (WSI) are capable of producing slide-level diagnostic descriptions but fail to g...
Background: Objective Structured Clinical Examination (OSCE; Clinical Performance Examination [CPX] in South Korea) is a high-stakes assessment of cli...
Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases ca...
We introduce TeMLM, a set of transparency-first release artifacts for clinical language models. TeMLM unifies provenance, data transparency, modeling ...
Integrating deep learning into healthcare enables personalized care but raises trust issues due to model opacity. To improve transparency, we propose ...
In July 2025, 18 academic manuscripts on the preprint website arXiv were found to contain hidden instructions known as prompts designed to manipulat...
This study examines how domestic violence victims can effectively disclose their experiences in online support groups to receive meaningful support, a...
Family caregivers of individuals with Alzheimer's Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that p...
The rapid rise of generative artificial intelligence (AI) is fundamentally transforming the landscape of medical writing and publishing. In response, ...
We study the ability of language models to reason about appropriate information disclosure - a central aspect of the evolving field of agentic priva...
Autonomous Mobility-on-Demand (AMoD) systems, powered by advances in robotics, control, and Machine Learning (ML), offer a promising paradigm for fu...
Minoritised ethnic people are marginalised in society, and therefore at a higher risk of adverse online harms, including those arising from the loss...
Purpose To evaluate the diagnostic performance of artificial intelligence (AI) models in detecting and classifying aortic dissection (AD) from CT imag...
IMPORTANCE: Individuals whose chronic pain is managed with opioids are at high risk of developing an opioid use disorder. Electronic health records (E...
BACKGROUND: The rapid advancement of artificial intelligence (AI) in healthcare necessitates comprehensive and standardized reporting guidelines to en...