Latest AI and machine learning research in clinical trials for healthcare professionals.
Large Language Models are increasingly used in consumer-facing mental health tools, many of which claim that prompt engineering alone can ensure safe therapeutic behavior. This study evaluates that assumption by testing 20 proprietary and open-source LLMs on high-risk psychiatric scenarios, using prompts grounded in behavioral therapy principles. Prompt engineering reduced some predictable risks, ...
Abstract Background: Remote clinical reviews have become an integral component of contemporary nursing practice across community and acute care settings. Nurses increasingly make autonomous clinical decisions using telephone, video, and online/digital systems, often with limited sensory information and under conditions of uncertainty. However, empirical understanding of how nurses make clinical de...
Single-cell transcriptomics resolves CAR T-cell states, yet translating heterogeneous cellular signals into patient-level therapeutic response remains...
Rising costs, slow accrual and molecular substratification of cancers necessitate novel clinical trial designs. We demonstrate that artificial intelli...
State-of-the-art flow based text-to-image (T2I) models exhibit remarkable generative abilities but remain vulnerable to producing unsafe content. Prio...
Methods INCA is a prospective, single-center cohort study with nationwide recruitment. Participation is open to adult patients and informal caregivers...
Background General-purpose large language models increasingly encounter emotional and therapy-like conversation, yet are not developed or evaluated as...
Frozen small code LLMs are deployed locally, yet the information guiding a retry after a failed attempt is still measured without placebo controls in ...
_ SURPASS-HF: Safety and Utility of Remote Pulmonary Artery Sensor Shared-management in Heart Failure --Background-- Insulin-dependent diabetics self-...
Existing evaluations of healthcare AI often treat interoperability as a technical infrastructure issue rather than a factor that directly influences t...
Background: Documentation burden significantly impacts nursing workload and well-being, with nurses spending an estimated 20-40% of their time on docu...
Introduction: Climate change disproportionately affects disadvantaged communities, yet construction workforce education rarely addresses interconnecte...
Background: Nuclear medicine and radiopharmaceutical development require coordinated radiochemistry, dosimetry, molecular imaging, radiation-safety an...
Background Timely assessment, classification, and escalation of public health events are essential for effective outbreak response, yet decision-makin...
Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulner...
Three audiences -- the family of a newly diagnosed Ewing sarcoma patient, the long-term survivor, and the cooperative-group trial statistician -- rece...
Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment a...
Image guardrails are typically trained and evaluated under a fixed safety policy, implicitly treating safety as an intrinsic property of an image. Rea...
Text-to-image diffusion models have achieved high visual fidelity and broad adoption, but remain vulnerable to safety violations when adversaries expl...
In many decision-making settings, new interventions are acceptable only if they do not reduce outcomes below some established threshold. For example, ...