Solumed RIS vs. Fluency for Imaging

Solumed RIS serves as an all-encompassing radiology information system designed to optimize the complete radiology workflow, which includes patient scheduling, clinical reporting, billing, and the delivery of final reports, thereby boosting efficiency, precision, and communication while supporting both on-site and teleradiology activities. It effectively addresses common challenges faced in radiology, such as workflow bottlenecks, disjointed systems, communication difficulties, delays in reporting, compliance issues, and the need for transparency by integrating various components of the radiology workflow through seamless data interchange and dynamic HL7 message mapping. Furthermore, Solumed RIS enhances patient management by automating communications through channels like email, SMS, and WhatsApp, and promotes better teamwork with real-time version control features. The system is also bolstered by state-of-the-art technologies, including a Vendor Neutral Archive and the MedDream DICOM Viewer, which guarantee secure storage solutions and efficient web-based access. In addition, its intuitive interface ensures that radiologists and staff can navigate the system with ease, making it an indispensable asset for contemporary radiology practices. Ultimately, the robust capabilities of Solumed RIS position it as a transformative solution in the radiology field, paving the way for improved patient care and operational excellence.

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.