Search AMIA.org

To evaluate the effectiveness of generative query expansion for biomedical literature retrieval.

Mixed methods are used to holistically understand the "what", "how", and "why" questions within a single study by integrating quantitative and qualitative methods. Although this approach has demonstrated value in other disciplines, their use in applied clinical informatics research remains largely unexplored. This methodological review characterized the types of informatics intervention studies that used mixed methods designs and the specific methods of evaluation and reporting.

Artificial Intelligence models are increasingly used in health care, yet global performance metrics can mask variations in reliability across individual patients or subgroups with shared attributes, called patient profiles. This study introduces predictive performance precision analysis in medicine (MED3pa), a method that identifies when models are less reliable, allowing clinicians to better assess model limitations.

Despite widespread implementation of predicted patient wait time information systems in hospital emergency departments (EDs), the relationship between quality of announced wait time information and ED overcrowding mitigation remains unclear. This study investigates how prediction accuracy, update frequency, and patient adoption rates affect ED overcrowding level.

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.

Predictive artificial intelligence (AI) promises to transform care delivery, enhance patient safety, and improve health outcomes. Realizing these benefits will require careful design, implementation, and monitoring strategies to avoid unintended consequences, including automation bias (i.e., erroneously favoring recommendations from automated systems). Automation bias is particularly concerning due to the variability of AI performance across time and populations, leading to predictions that may be variably incorrect, uncertain, or unfair.

Generative artificial intelligence (AI) chatbots built on large language models are rapidly entering mental-health care, offering human-like support without meeting evidentiary standards for safety or effectiveness.