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To quantify run-to-run reproducibility of Gemini 3 Flash Preview and GPT-5.2 for trial-success classification across temperature and reasoning/thinking settings and determine whether single-run reporting suffices.

Intelligent agent-driven research co-pilots, leveraging advances in generative AI, are transforming how scientists access biomedical knowledge. This paper presents Med.ai ASK, an agentic question-answering system designed to address biomedical inquiries through dynamic retrieval augmentation and tool-driven reasoning. We aim to develop a system capable of parsing the nuance in biomedical scientists' research questions to provide reliable, grounded responses that are more accurate than other generative AI solutions.

Foundation models pretrained on structured electronic health record (EHR) data promise improved predictive performance, sample efficiency and resilience to distribution shifts. However, model design, scale and use remain unclear. Objectives were to characterize foundation models pretrained on structured EHR data; examine temporal trends in model application and scale, architecture and design; and assess the extent to which publications omitted methodological details.

Traditional electronic health record (EHR) foundation models fail to process unseen medical codes, limiting generalizability across institutions with different vocabularies. To address this problem, we introduce medical concept representation (MedRep), standardized medical concept representations for EHR foundation models, enabling recognition of semantically similar concepts regardless of their specific IDs.

Health-related social needs (HRSN) significantly influence health outcomes, yet healthcare organizations face persistent challenges tracking referrals to community-based organizations and interpreting referral outcomes across fragmented clinical and social care systems. Prior studies report low referral fulfillment rates, but much of this evidence derives from single organizations, manual data collection, or incentivized documentation workflows, limiting insight into how information infrastructure shapes what is observable at scale.

Patients living with dementia (PLWD) require attention to social determinants of health (SDoH), but social information is often unavailable or incomplete during care encounters, and clinicians report uncertainty about how to act on this information.

In our recent study we showed that GPT's ability to perform OPS-code extraction from operational reports was equivalent to coding neurosurgeons. In this study we aim to evaluate the effect of context enhancement on GPT-5's code extraction abilities.

Hospital artificial intelligence (AI) is increasingly embedded in electronic health record workflows, cloud inference pipelines, imaging, medication review, triage, early warning, documentation, and operational management. This paper proposes a risk-tiered governance framework and implementation pathway for hospital AI applications that are integrated into clinical and operational workflows.

This study evaluated the effect of a Spatial Awareness Integrated Electronic Health Record (SAI-EHR) prototype on efficiency, usability, and cognitive workload among intensive care nurses performing standardized documentation tasks, compared with a traditional linear flowsheet-style interface (LID-EHR).

Clinical documentation consumes substantial clinician time, potentially detracting from patient care. Generative artificial intelligence (AI) may support drafting discharge summaries and patient referral documents, but feasibility in non-Western-language oncology settings using real-world electronic health record (EHR) data remains insufficiently evaluated. This study assessed feasibility in a Japanese cancer hospital using an enterprise AI system.