PACS Radiology vs. Fluency for Imaging

Although numerous contemporary radiology machines come with their own Picture Archival and Communication System (PACS), there are several persuasive reasons to opt for our PACS instead. Our PACS provides advanced tools for managing DICOM images, leading to a more effective workflow overall. Moreover, it integrates effortlessly with the RIS of our Electra HMIS product, thereby avoiding any possible compatibility challenges with other PACS systems. This integration facilitates the storage of radiology images in conjunction with the patient’s Electronic Medical Record (EMR) information, ensuring that accurate records are maintained concerning the date, time, and medical circumstances related to the diagnostic imaging performed. Furthermore, our PACS streamlines the process for accessing and manipulating both radiology images and their corresponding reports, which significantly boosts productivity and enhances patient care. By adopting our PACS, healthcare providers can create a more efficient experience that contributes to better diagnostics and improved patient outcomes. In an ever-evolving medical landscape, leveraging such technology is crucial for maintaining high standards of care.

Fluency for Imaging is an AI-powered radiology reporting platform developed by Jacobian to support faster, more accurate diagnostic reporting in medical imaging workflows. Radiology departments face increasing workloads and complex reporting requirements, which often lead to documentation inefficiencies and cognitive overload for clinicians. Fluency for Imaging addresses these challenges by combining advanced speech recognition technology, AI-driven automation, and structured reporting capabilities into a unified reporting environment. Radiologists can dictate reports using natural speech while the platform automatically organizes the narrative into structured clinical documentation. The system integrates directly with PACS platforms to transfer measurement data, imaging findings, and AI-generated insights directly into the report, eliminating manual transcription and reducing the potential for errors. Fluency for Imaging also incorporates large language model–based ambient reporting features that can listen to dictations, understand clinical context, and generate structured impressions automatically. Built-in guideline support and intelligent nudging help ensure reports follow established medical standards such as BI-RADS, PI-RADS, and TNM classifications. The platform also enables ontology-based coding using clinical standards including RadLex, SNOMED CT, and ICD-10, transforming unstructured text into structured, data-rich reports suitable for analytics, research, and clinical decision support. Multimedia reporting features allow radiologists to include annotated images, tables, and visual diagrams to improve report clarity for referring physicians. Additionally, Fluency for Imaging supports interoperability through standards such as HL7, FHIR, and FHIRcast, enabling seamless communication between imaging systems, hospital information systems, and analytics platforms.