SignalPET 360° vs. Fluency for Imaging

SignalPET 360° is a state-of-the-art veterinary radiology system that integrates advanced vision-based artificial intelligence, context-aware language processing, and PACS technology, all while ensuring continuous access to board-certified radiologists for swift and actionable imaging insights across various modalities, such as X-rays. This comprehensive platform meticulously assesses each case and image, acting as an extra layer of evaluation to provide rapid AI-generated insights within minutes, emphasizing critical pathologies in thoracic, abdominal, musculoskeletal, soft tissue, and dental areas, and it can produce structured preliminary evaluations in under five minutes. Its Complete Report feature utilizes sophisticated machine vision and extensive language models to create detailed findings, conclusions, and clinical recommendations that closely mirror those of a traditional radiologist's report, usually delivering results within a 30-minute window, thereby assisting clinicians in understanding the findings, their significance, and possible next steps. Furthermore, this groundbreaking platform boosts the efficiency and precision of veterinary diagnostics, ultimately leading to better patient care and outcomes, while simultaneously enabling veterinarians to make more informed decisions based on comprehensive insights. Such advancements not only streamline the diagnostic process but also foster a collaborative approach to animal health management.

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.