Ushering in the Next Era of Cloud-Native AI Capabilities for Radiology
Introducing Dragon Copilot, your AI companion for PowerScribe One For radiologists, the reporting workflow of the future is here. At RSNA 2025, in Chicago, we’re showcasing Dragon Copilot, a cloud-native companion for PowerScribe One. Currently in preview, Dragon Copilot builds on the trusted capabilities of PowerScribe One to accelerate innovation and modernize reporting workflows while unlocking extensibility for radiology teams and partners. Why we built it: Technical drivers for a new era With growing demand for imaging services coupled with a workforce shortage, healthcare professionals face increased workloads and burnout while patients experience greater wait times. With our breadth of healthcare industry experience combined with our AI expertise and development at Microsoft, we immediately understood how we could help address these challenges. For radiologists, we sought to plugin into existing reporting workflows with rapid innovation, scalable AI, and open extensibility. How we built it: Modern architecture and extensibility By delivering Dragon Copilot as cloud-native solution built on Azure, we can enable new services globally. We apply the full capabilities of Azure for compute, storage, and security for high availability and compliance. Our modular architecture enables fast delivery of new features with APIs at the core to allow seamless integration, extensibility, and partner innovation. To imbue the workflow with AI through our platform, we harness the latest generative, multimodal, and agentic AI (both internal and through our partners) to support clinical reporting, workflow automation, and decision support. Key architectural highlights: AI services: Integrated large language models (LLMs) and vision-language models (VLMs) for multimodal data processing. API-first design: RESTful APIs expose core functions (draft report content generation, prior summarization, quality checks and chat) enabling partners and developers to build extensions and custom workflows. Extensibility framework: Open platform for 1st- and 3rd-party extensions, supporting everything from custom AI models to workflow agents. Inside the innovation Dragon Copilot alongside PowerScribe provides a unified AI experience. Radiologists can take advantage of the latest AI advancements without disruption to their workflows. They do not need another widget taking up room on their desktop. Instead, they need AI that fits seamlessly into existing workflows connecting their data to the cloud. Our cloud-first approach brings increased reliability, stability, and performance to a radiologists’ workflow. I’m thrilled to highlight the key capabilities of this dynamic duo: PowerScribe One with Dragon Copilot. Prior report summary: Automatically summarizes relevant prior reports, surfacing key findings, and context for the current study. AI-generated draft reports and quality checks: The most transformative aspect of Dragon Copilot is its open, extensible architecture for AI integration. We don’t limit radiology teams to a single set of AI tools. We enable seamless plug-ins for AI apps & agents from both Microsoft and our growing ecosystem of 3rd-parties. We provide a single surface for all your AI needs. This approach will enable radiology departments to discover, acquire, & deploy new AI-powered extensions. We’re enthusiastic about embarking on this journey with partners. We're also excited about collaborations with developers and academic innovators to bring their own AI models and services directly into the Dragon Copilot experience. Integrated chat experience with credible knowledge sources and medical safeguards: This chat interface connects radiologists to credible, clinically validated sources from Radiopedia and Radiology Assistant. It enables agentic orchestration and safeguards provided by Azure's Healthcare Agent Services for PHI and clinical accuracy. In the future, we expect to have a variety of other sources for radiology customers to choose from as well as the ability for organizations to add their own approved policies and protocols. This chat is designed to route questions to the right agent, provide evidence for claims, and filter responses for clinical validity. Over time, it will include extensions with custom agents powered by Copilot Studio. Help us shape what’s next As we continue to evolve Dragon Copilot alongside PowerScribe One, we invite innovators, developer partners, and academics to join us in shaping the future of radiology workflow. Dragon Copilot is more than a product; it’s a solution for rapid, responsible innovation in radiology. By combining cloud-native architecture, advanced AI capabilities, and open extensibility, we’re enabling radiology teams to work smarter, faster, and with greater confidence. Ready to see it in action? Visit us at RSNA 2025 (November 30–December 4), booth #1311 South Hall. Or contact our team to join the journey.
Empowering radiologists with clinical guidance, quality standards, and scoring and assessments
In March 2024, we announced general availability of Radiology Insights, a new model built in Azure AI Health Insights service. This model uses radiology reports to surface relevant insights that can help radiologists enhance the quality of their reports. Today, we are announcing three new capabilities added to the Radiology Insights model: clinical guidance, quality measures, and scoring and assessment. Each plays a crucial role in enhancing clinicians’ decision-making, improving healthcare quality, and standardizing evaluations in medical imaging. Clinical Guidance Clinical guidance uses an evidence-based approach based on industry guidelines (ACR Guidelines [1,2,3,4] and Fleischner Society Guidelines [5] ) to help radiologists make the most appropriate recommendations and timing for future actions, such as specific follow-up studies. Clinical guidance extracts clinical finding information from the documentation, retrieving the necessary evidence to support proposed recommendations. If no follow-up action can be proposed, clinical guidance will identify what information is missing from the documentation. Figure 1 – Clinical Guidance: In this example, only two findings are considered and highlighted, each serving as a trigger for the pulmonary nodule clinical guideline. Radiology Insights model response: The first finding in the report proposes two candidate recommendations, including their modality and anatomy. The details of the first finding, LOBE and SIZE, are present in the report and extracted by the model. The second finding does not lead to a recommendation proposal due to missing information (Size). The first finding is ranked higher because of its greater information depth. Quality Measures Quality measures guidelines are an essential tool in healthcare to monitor the quality of care by providing frameworks (MIPS QCDR measures [6]) for measuring, reporting, and continuously improving healthcare practices. These measures are now supported by the Radiology Insights model to ensure healthcare providers meet established quality standards. The model captures quality measures criteria explicitly documented in the report and checks if all criteria necessary to meet quality standards are included. For each quality measure there are three possible outcomes: The report meets the required criteria: the performance is 'met'. The report does not meet all the criteria: the performance is 'not met'. The report does not meet all the required criteria: the model states an ‘exception’. For example, if a patient is allergic to Chlorhexidine, a substance used to meet quality measures for Central Venous Catheter (CVC) insertion, the standard procedure cannot be followed. The model will recognize this as an exception. When quality measure criteria are missing from the report, the documentation could be updated to include this information, or a review by a healthcare professional could be conducted to understand why these important criteria were not documented. Quality measures play a vital role in ensuring healthcare providers adhere to high standards, ultimately improving patient care. By following these measures, healthcare providers can avoid complications and deliver better outcomes for their patients. Figure 2 – Quality Measures: In this example, findings about a new nodule are considered and highlighted by the model. The model triggers the quality measure for incidental pulmonary nodule. Radiology Insights model response: For the quality measure ‘Incidental Pulmonary Nodule’, the performance is 'met'. The model surfaces the criteria for having a follow-up recommendation in the report, which is the only criterion required for compliance. Scoring and Assessment Scoring and assessment systems [7,8] are used in medical imaging and diagnostics to help standardize the evaluation and reporting of findings and provide a structured approach to interpreting imaging studies, assessing disease risk, and guiding clinical management. The Radiology Insights model surfaces and highlights scoring and assessments with their classifications or values that were explicitly documented by radiologists in their reports. In the sample below, the model identifies two assessments with its values: the ASCVD (Atherosclerotic Cardiovascular Disease) risk and the Agatston Score, which measures the amount of calcium in the coronary arteries. In this example, there is a 17.6% chance of experiencing a cardiovascular event in the next 10 years (ASCVD) and Agatston Score of 0 suggests low short-term risk of heart attack. Figure 3 – Scoring and Assessment: In this example, two assessments, ASCVD and Agatston score, are reported with their values. Radiology Insights model response: The model surfaces two scoring and assessment instances, one of category ASCVD Risk with a value of 17.6% and one of category Calcium Score with value 0. Do more with your data with Microsoft Cloud for Healthcare With Azure AI Health Insights, health organizations can transform their patient experience, discover new insights with the power of machine learning and AI, and manage protected health information (PHI) data with confidence. Enable your data for the future of healthcare innovation with Microsoft Cloud for Healthcare. We look forward to working with you as you build the future of health. Learn more about Azure AI Health Insights and how to start working with this Azure resource in the Azure AI Health Insights documentation Learn more about Radiology Insights Important Radiology Insights is a capability provided “AS IS” and “WITH ALL FAULTS.” Radiology Insights isn’t intended or made available for use as a medical device, clinical support, diagnostic tool, or other technology intended to be used in diagnosis, cure, mitigation, treatment, or prevention of disease or other conditions, and no license or right is granted by Microsoft to use this capability for such purposes. This capability isn’t designed or intended to be implemented or deployed as a substitute for professional medical advice or healthcare opinion, diagnosis, treatment, or the clinical judgment of a healthcare professional, and should not be used as such. The customer is solely responsible for any use of the Radiology Insights model.
