Purpose To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weight language models (LMs) and retrieval-augmented generation (RAG) and to assess the ef...
Background Ovarian-Adnexal Reporting and Data System (O-RADS) for MRI helps assign malignancy risk, but radiologist adoption is inconsistent. Automatic assignment of O-RADS scores from reports could increase adoption and accuracy. Purpose To evaluate...
Background Incomplete clinical histories are a well-known problem in radiology. Previous dedicated quality improvement efforts focusing on reproducible assessments of the completeness of free-text clinical histories have relied on tedious manual anal...
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2024
OBJECTIVES: Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide ...
Journal of the American Medical Informatics Association : JAMIA
Sep 1, 2024
OBJECTIVES: To evaluate the proficiency of a HIPAA-compliant version of GPT-4 in identifying actionable, incidental findings from unstructured radiology reports of Emergency Department patients. To assess appropriateness of artificial intelligence (A...
Studies in health technology and informatics
Aug 22, 2024
Radiology reports are an essential communication method for ensuring smooth workflow in healthcare. However, many of these reports are described in free text, and findings documented by radiologists may not be adequately addressed. In this study, foc...
Studies in health technology and informatics
Aug 22, 2024
Artificial intelligence (AI) is expected to transform healthcare systems and make them more sustainable. Despite the increased availability of AI tools for disease detection, evidence of their impact on healthcare organisations and patient care remai...
The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations als...