Skia vs. Fluency for Imaging

Skia is an advanced platform tailored for radiology reporting, specifically aimed at groups and teleradiology services that emphasize exceptional results while maintaining efficiency. In contrast to conventional methods, SkiaQA conducts a thorough review of each report before it is submitted, adeptly spotting issues such as discrepancies in laterality, missing or incorrect comparison dates, contradictions within the report, inconsistencies between findings and impressions, critical findings that could be easily overlooked, and incomplete sections, all in a matter of seconds. Moreover, SkiaReporter transforms the reporting workflow by doing away with the dictation and correction stages; radiologists can effortlessly click on findings, leading to the automatic generation of reports in accurate clinical terminology, with impressions directly derived from these selections, thus ensuring precision without any guesswork or mistakes. Initial findings suggest that the speed of reporting has improved by 30 to 40%, with an impressive 70 to 90% of impressions being generated automatically. In addition, SkiaManager enhances coordination by overseeing worklist synchronization, automatic assignments based on modality and subspecialty, alerts for critical findings, context pulled from previous studies, and direct submissions to PACS. Crucially, Skia upholds stringent privacy standards, as no patient information is stored or transmitted beyond your PACS system, thereby guaranteeing confidentiality and compliance with regulations. This cutting-edge solution not only streamlines workflow but also considerably minimizes the chances of errors in radiology reporting, ultimately leading to improved patient care and safety.

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.