Sirona vs. Fluency for Imaging

Fluency for Imaging

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What is Sirona?

Sirona Medical introduces RadOS, an all-encompassing cloud-based radiology platform that integrates essential radiology IT functions such as worklist management, image visualization, archiving, reporting, and artificial intelligence into a single, user-friendly web interface. By leveraging AWS for its cloud infrastructure, RadOS does away with the need for physical servers, software installations, and hardware maintenance, thereby simplifying the operational processes for radiology departments. The platform is accessible via any device with a Chromium-based browser, allowing radiologists the convenience of accessing imaging and patient records from virtually any location around the globe at any hour. In addition, it features a centralized PACS archive that boasts an exceptional 99.99% uptime and rapid data retrieval capabilities, along with a built-in viewer adept at handling imaging inconsistencies through its unique patented ontology. Moreover, RadOS includes a "Pixel-Powered Reporter" that utilizes agentic AI—a specialized large language model in radiology—and advanced natural language processing to assist in the pre-drafting of structured, study-aware reports while also generating tailored impressions for individual radiologists. This rich array of functionalities positions RadOS as an essential asset in improving both the efficiency and precision of radiological workflows, ultimately benefiting healthcare providers and patients alike. As a result, RadOS not only streamlines processes but also enhances overall diagnostic accuracy, making it a vital component in the evolving landscape of medical imaging.

What is Fluency for Imaging?

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.