AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Background: Rare diseases affect an estimated 300 million people worldwide, yet the research needed to guide diagnosis and treatment is often fragmented across multiple unstructured literature sources. Natural history studies (NHS) are a key source of this evidence, but manually extracting structured information from N...
LLM-based patient-facing pathology report interpretation shows potential to bridge specialist pathology language and patient communication, and evaluation should extend beyond readability to include fidelity to the original pathology report, patient understanding, safety, and usability.
393 Background: Inpatient clinicians need timely access to prior serious illness conversations (SICs) for patients with advanced cancer. Although structured electronic health record (EHR) modules exist to document SICs, they are infrequently completed, and most SIC information remains embedded in free-text clin...
Abstract Electronic health records (EHRs) show that health information technology can digitise clinical work while increasing documentation burden, workflow rigidity and professional burnout. Artificial intelligence (AI) may enter a deeper layer of medicine than the EHR because it can draft summaries, interpret images,...
Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failure mode shared between these two applications by assessing their sensitivity to information incidental to the patient encounter. In 576 patient-clinician dialogues, we...
BACKGROUND Large language models (LLMs) and multimodal foundation models are increasingly proposed for diagnostic decision support, symptom triage, report interpretation, and electronic health record (EHR)-grounded workflows. Because medical informatics is concerned with how information and communication technologies b...
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