Chicago/Bengaluru – 5C Network, the global leader in AI-driven radiology operations, today announced that it will present six scientific papers at the Radiological Society of North America (RSNA) 2025 in Chicago. Backed by the world’s largest medical imaging dataset and its proprietary multimodal vision-language model, Bionic, 5C Network is building the world’s first AI-native global radiology cloud to increase radiologist capacity, enhance diagnostic quality, and expand access to care at scale.
At RSNA 2025, taking place from November 30 – December 4, 2025 at McCormick Place, Chicago, IL, 5C Network will showcase six scientific studies spanning emergency neuro-radiology, teleradiology quality assurance, infectious disease screening, operational intelligence, agentic AI workflows, and occupational lung disease detection. Together, these presentations demonstrate how AI-native radiology can simultaneously improve speed, accuracy, quality, and system-level efficiency.
“These six RSNA presentations reflect years of deep scientific work to build an AI-native radiology platform that goes far beyond point solutions,” said Kalyan Sivasailam, CEO and Co-Founder of 5C Network. “With Bionic, we are creating an intelligent radiology cloud that augments clinical decision-making, improves quality at scale, and solves real-world operational challenges. RSNA 2025 is an important milestone in showcasing how India-built AI can power the future of global radiology.”
Bionic combines advanced computer vision with large language models to understand medical images, interpret complex patterns, generate structured reports, and orchestrate intelligent radiology workflows. The platform supports radiologists across the entire lifecycle of care – from triage and quality assurance to intelligent scheduling, operational forecasting, and large-scale population screening.
Scientific Papers Presented at RSNA 2025
Scientific Category
1. AI Augmented CT Brain Bleed Detection – A real-world validation of Bionic Vision for rapid and reliable detection of intracranial hemorrhage on non-contrast CT, designed to reduce time to intervention and improve accuracy in emergency settings.
2. Real-Time Quality Assurance in Teleradiology using LLM-Based Report Consistency Checks – A demonstration of how Bionic LM performs automated consistency checks across findings, impressions, and clinical context, enabling continuous quality improvement in high-volume teleradiology networks.
3. Vision-Language Model for AI-Powered TB Screening – A multimodal approach combining chest radiographs and clinical metadata to identify early-stage tuberculosis at scale, supporting national TB elimination programs.
4. Intelligent Scheduling in Teleradiology using AI Forecasting of Study Volume and Radiologist Availability – A capacity-management system that predicts imaging volume by time of day and aligns it with radiologist availability to improve turnaround times and reduce operational friction.
5. Bionic LM: Multi-Tier AI Agentic Workflow for Radiology Report Quality and Efficiency Enhancement – A coordinated multi-agent framework that assists with question generation, findings verification, clinical query matching, quality scoring, and structured reporting to improve radiologist productivity and report integrity.
Cutting-Edge Category
6. AI-Enhanced Silicosis Screening from Chest Radiographs: An Opportunistic Approach for Occupational Lung Disease – An advanced deep-learning system that identifies early silicosis changes on routine chest radiographs, enabling proactive screening of at-risk workers and large-scale occupational health programs.
The presentations also highlight the growing role of multimodal and agentic AI in solving some of the most complex challenges in modern radiology – from emergency diagnostics and infectious disease screening to workforce shortages, reporting quality, and large-scale preventive healthcare.
