Transforming Clinical Workflows with Microsoft Dragon Copilot’s Partner-Driven AI Innovations
Healthcare software integration is notoriously complex, demanding technical rigor, regulatory compliance, organizational alignment, and workflow transformation. Clinicians face an overwhelming cognitive load—during a typical primary care visit, a physician may juggle over 150 discrete data points and make a dozen crucial decisions in less than 15 minutes. In this environment, AI must do more than offer intelligence—it must deliver the right information, at the right time, without increasing administrative burden or disrupting the clinician’s focus on patient care. Microsoft is expanding Dragon Copilot by enabling an open ecosystem of developer-built AI solutions for specific tasks and specialties, allowing healthcare organizations to access clinical intelligence at the point of care while further streamlining workflows, reducing administrative burden, boosting productivity, and enhancing revenue integrity. Dragon Copilot’s partner-driven AI extensibility empowers technology providers to deliver specialized solutions directly into clinical workflows, accelerating AI adoption at the point of care and enabling better outcomes for patients and clinicians. By opening Dragon Copilot, Microsoft enables the creation of seamless, scalable solutions that help clinicians reduce administrative tasks and improve patient care. Developers gain an opportunity to collaborate with industry leaders, shape healthcare technology, and deliver impactful AI applications for clinicians and patients. Partner Success Spotlight: Canary Health’s Innovative Solution Canary Speech is a Utah-based, AI-powered voice biomarker health tech company, utilizing patented real-time vocal analysis to screen for mental health and neurological disorders. Canary’s technology swiftly captures and analyzes speech data supporting clinicians in identifying cognitive and behavioral changes. Recently, Canary Speech launched Canary Ambient™, an API-first solution for real-time voice analysis in healthcare and contact centers. This software provides actionable insights from patient-clinician conversations by tracking speech patterns for real-time assessments of cognitive and behavioral health conditions. Canary Speech advances speech and language applications across health systems, payers, and pharmaceutical markets. The collaboration with Dragon Copilot is seamless and secure. Canary Speech receives patient audio—ideally during the encounter—via infrastructure designed to enable HIPAA compliance and newly developed endpoints. Their AI processes the audio, then surfaces summarized cognitive and behavioral assessments using Microsoft’s Adaptive Card framework. These interactive cards appear natively within Dragon Copilot, enabling clinicians to quickly review findings and to initiate follow-up assessments or treatment plans—all without leaving their workflow. The Canary Speech solution with Dragon Copilot is currently and being validated with real clinicians and patients. It demonstrates how Dragon Copilot can share not only encounter audio, but also clinical notes, patient context, and relevant history with trusted partners—expanding the universe of AI-powered insights available at the point of care. How AI Applications Work: Technical Enablement for Continuous Innovation Dragon Copilot’s extensibility framework supports partners in surfacing AI insights and enrichments at multiple points in the clinical workflow: Pre-encounter – Provide AI-generated insights to clinicians before they see the patient, supporting preparation and personalized care planning. During encounter – Surface real-time, contextual recommendations while the provider is examining the patient, ensuring decisions are timely and informed. Post-encounter – Deliver follow-up insights after the clinical note is generated, enabling comprehensive care coordination and outcome tracking. Microsoft’s Adaptive Card framework makes it easy for developers to build interactive platform-agnostic content blocks that integrate directly into Dragon Copilot. These cards support rich text, reference links, action buttons, and in-card feedback, creating a unified and intuitive user experience for clinicians. The following diagram shows the data flow between Microsoft Dragon Copilot and partner endpoints that provides extensions such as Canary, via infrastructure designed to enable HIPAA compliance, access to the encounter audio data. In addition to audio, other data elements such as turn transcript and the clinical note can also be made available to extensions. Driving Innovation Together through Partnership Our commitment to driving innovation through collaboration is sustained by actively engaging with partners and continually analyzing the market to keep our solutions effective and aligned with evolving needs. At this time, we have a growing list of early partners that have signaled interest in bringing their solutions to the point of care, through Dragon Copilot and help to represent the leading edge of healthcare technology, each bringing specialized expertise and transformative solutions to the table. Their work exemplifies how collaborative innovation can address real-world challenges across the care continuum. Artisight’s Smart Hospital Platform, brings hands-free, device-free documentation directly to the bedside. By pairing a clinician’s unique voice profile with their secure RTLS badge, Artisight eliminates logins and shared device interactions to allow care teams to focus on patients, not paperwork. Ambient documentation runs seamlessly in the background, generating accurate clinical notes while freeing clinicians to deliver more meaningful, face-to-face care. This integration streamlines workflows, reduces administrative burden, and drives measurable improvements in efficiency, compliance, and the overall patient experience. Atropos Health is the leader in translating real-world clinical data into personalized real-world evidence (RWE) and insights. Atropos Health is the developer of GENEVA OS®, the operating system for rapid healthcare evidence across a robust network of real-world data. Healthcare and life science organizations work with Atropos Health to close evidence gaps from bench to bedside, improving individual patient outcomes with data-driven care, expediting research that advances the field of medicine, and more. We aim to transform healthcare with timely, relevant real-world evidence. Cohere Health’s prior authorization application automates care requests, eliminating the administrative burden for clinicians and enabling real-time care approvals. Cohere's solution provides critical transparency right at the point of care while collecting relevant clinical documentation, patient summaries, and orders. It shares prior authorization requirements and offers in-the-moment guidance to physicians, helping them secure faster approvals and ensure patients receive the care they need more quickly. Elsevier's ClinicalKey AI delivers fast, reliable clinical insights at the point of care—combining proprietary medical content, leading textbooks, top scientific journals, government publications, and clinical guidelines into one trusted platform. Powered by advanced AI and backed by expert human review, it ensures answers are clinically relevant and backed by research. With privacy built in and citations clinicians can trust, ClinicalKey AI helps healthcare professionals make confident, informed decisions faster. Ensemble’s revenue cycle intelligence engine, EIQ, helps health systems proactively prevent payment denials, improve revenue integrity, and accelerate cash flow. Drawing on insights from more than 80,000 denial audit letters and 80 million annual claim transactions, EIQ analyzes clinical documentation against historic payer patters to detect nuanced issues that could trigger DRG downgrades or medical necessity rejections. It flags issues for operator intervention with context-aware prompts designed to ensure diagnoses are backed by precise clinical evidence and meet payer documentation requirements. Beyond denial prevention, EIQ strengthens the documentation foundation needed for downstream audits, ensuring that every claim is defensible and complete. hellocare.ai is a leading provider of AI-assisted virtual care solutions. Headquartered in Clearwater, FL, the company supports more than 80 health systems across the U.S. and is rapidly expanding globally. hellocare.ai helps health systems deliver high-quality, patient-centered care while improving clinical efficiency and staff wellbeing. Its fully integrated platform includes AI-Assisted Virtual Nursing, Virtual Sitting, Patient Engagement, Digital Whiteboard, Digital Room Signage, Ambient Documentation, Hospital-at-Home, Remote Patient Monitoring (RPM), and Digital Clinic, seamlessly embedding into existing healthcare EHRs, infrastructure, and care delivery models to power the next generation of healthcare. Humata Health is revolutionizing clinical clearance, moving beyond the narrow confines of traditional prior authorization to address the entire patient journey, from referrals and specialty drugs to post-acute care. By bringing providers and payers together on a single, shared infrastructure, its technology uses powerful AI and automation to streamline fragmented approvals. This collaborative approach unlocks faster, smarter, and more confident decisions, ensuring patients receive the appropriate care they deserve, exactly when they need it. Driven by a vision to see care move forward, Humata Health is built for yes. Lightbeam Health Solutions helps healthcare organizations manage and scale population health programs to improve performance within risk-based contracts. Leveraging clinical, health plan, and social determinants of health data, Lightbeam identifies and delivers actionable insights to clinicians and patients for proactive intervention. Proven outcomes include quality improvement, risk adjustment accuracy, reduced cost of care and avoidable utilization, and a better experience for clinicians and patients alike. Optum is collaborating with Microsoft to integrate ambient listening technology into clinical settings with Optum Real, helping physicians reduce documentation time and focus more on patient care. This integration supports smarter, real-time clinical workflows through AI-powered automation. Pangaea Data’s AI platform helps close care gaps by finding untreated and under-treated patients – including those who are undiagnosed, misdiagnosed, or miscoded – across hard-to–diagnose conditions. Seamlessly integrated with electronic health record (EHR) systems, the platform processes patient’s medical records along with their conversation with the treating clinician at the point of care, empowering clinicians to address care gaps without disrupting workflows. This enables better outcomes, lower costs through earlier diagnosis, smarter triaging, and more appropriate levels of revenue through faster pre-authorizations and treatment initiation. Built and deployed on Microsoft Azure, the platform ensures full compliance with privacy standards. Press Ganey turns patient-clinician conversations into actionable insights. By analyzing both content and tone alongside patient experience data, the platform helps organizations address concerns early and gives clinicians a more complete understanding of their patients. As AI reshapes healthcare, Regard is working towards its mission to ensure every patient receives exceptional care while giving physicians more time with their patients. Regard’s Proactive Documentation platform reviews all data in the medical record, and from physician-patient conversations, to recommend diagnoses to providers and surface critical clinical context. This diagnosis-first approach delivers accurate documentation at the point of care, supporting both clinical and revenue cycle teams, by ensuring no critical information is missed. Rhyme eliminates prior authorization through touchless workflows and a new sustainable approach to gold carding. They help 89 of the largest hospital systems and over 300 payers work together to process over 5 million auths per year, save millions of dollars, reduce claim denials, lower patient costs, and accelerate time to care. RhythmX AI enables clinicians to deliver hyper-personalized treatments that expand access and capture measurable value. The platform optimizes medical economics in both fee-for-service and value-based care settings. Built on more than $1B in R&D and validated through coding reviews and 25,000+ clinical assessments, it unifies 8+ data sources – including clinical, financial, payer, and formulary data – and can incorporate health system–specific guidelines and pathways into real-time care recommendations. RhythmX AI drives care orchestration across primary, specialty, and emergency settings, enabling comprehensive diagnoses, optimized specialist referrals and measurable ROI (e.g., $57M annually for 200 PCPs). With access to 300M patient records, 4.4B annual claims, 1.8M clinicians and 300K facilities, the platform provides broad data coverage, consistent accuracy in suspected condition identification and scalable precision-care capabilities across diverse care environments. Through these strategic collaborations, Dragon Copilot continues to expand its reach and deepen its impact, supporting a dynamic environment where developers, clinicians, and organizations can thrive. The collective expertise of our partners is not only shaping the future of healthcare technology but also driving meaningful change for patients and clinicians everywhere. Join Us in Shaping the Future of Healthcare AI Enabling partner-driven AI applications into Dragon Copilot ushers in a transformative period of collaborative innovation in healthcare. Through Dragon Copilot’s extensibility framework, we’re accelerating the delivery of clinical intelligence, reducing friction, and ensuring that every provider has the insights they need to deliver the highest quality care. Whether you’re building the next breakthrough AI application or seeking to bring transformative solutions to clinicians worldwide, we invite you to join our community. Ready to shape the future of healthcare AI? Explore our developer resources, connect with our team, and become a Dragon Copilot extensibility partner today. Read through our documentation on how extensions for Dragon Copilot work and how to build your own - https://learn.microsoft.com/en-us/industry/healthcare/dragon-copilot/extensions Check out the sample repo with sample code, and more – https://github.com/microsoft/dragon-copilot-extension-samples Contact mailto:dragon_extensions@microsoft.com Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations. Generative AI Disclaimer: Generative AI does not always provide accurate or complete information. AI outputs do not reflect the opinions of Microsoft. Customers/partners will need to thoroughly test and evaluate whether an AI tool is fit for the intended use and identify and mitigate any risks to end users associated with its use. Customers/partners should thoroughly review the product documentation for each tool.Healthcare agent service in Microsoft Copilot Studio is now Generally Available
Healthcare organizations continue to face immense challenges: workforce shortages, rising costs, and growing demands for patient care. The clinical staff is overburdened, leading to stress, burnout, and staff shortages. Generative AI presents a powerful opportunity when it can automate administrative workflows, surface relevant insights, and assist the clinical staff with contextual, credible and up-to-date information. With that opportunity, we are excited to announce General Availability (GA) of healthcare agent service in Microsoft Copilot Studio. Building responsible, AI-powered healthcare agents With healthcare agent service, organizations can create healthcare-specialized AI applications that use generative AI within a framework that promotes trust, compliance, and real-world clinical scenarios. Agents combine built-in credible medical sources, such as FDA, CDC, MedlinePlus, MSD Manuals, DailyMed and more, with the organization’s own knowledge sources and plugins, while leveraging healthcare-specific actions. Customers can define the intended healthcare roles, such as healthcare professionals or patients, so the behavior is relevant and appropriate for the audience and use case. Pre-built use cases include clinical documentation assistance, patient self-service, helping healthcare professionals triage by organizing information, finding medication information, accessing recent clinical guidelines information, and more. Because responsible AI in healthcare is a top priority, healthcare agent service is infused with safeguards that are reinforced by a healthcare-adapted orchestrator optimized for safety. Clinical, chat, and compliance safeguards help keep interactions evidence-based and trustworthy, increasing the reliability and accuracy of generated responses and adherence to the highest standards of safety, privacy, and regulatory compliance. Healthcare agent service underscores our ongoing commitment to responsible AI in healthcare, by offering customers a reliable, production-ready foundation for healthcare solutions that can be used to help support patients and medical professionals. Extending Dragon Copilot with conversational solutions Healthcare agent service provides a framework for building conversational AI applications that can be integrated directly into Dragon Copilot, giving partners and healthcare organizations the ability to extend its functionality in a scalable, compliant way. Today, Information Assist in Dragon Copilot, built on healthcare agent service, delivers safeguarded generative AI answers grounded in trusted sources and enriched with patient history and context, ensuring clinicians receive accurate, timely, and context-aware insights. Clinicians can effortlessly access a broad range of clinical topics directly within their workflow using natural language, surfacing insights from leading, trusted healthcare content partners that promote more informed clinical decisions with less administrative work. Partners and healthcare organizations can use healthcare agent service to create tailored solutions with built-in safeguards that help ensure output meets healthcare standards and supports safe decision-making at the point of care. These solutions can be integrated directly into Dragon Copilot to enhance both clinical and financial performance. Real-world impact with customers Healthcare organizations are already adopting healthcare agent service to bring generative AI into real-world care settings. Early adopters are seeing meaningful impact in reducing administrative burden, improving patient experience, and empowering clinicians with trusted information. Bayer Pharmaceuticals has recently worked with Microsoft to enable new agentic AI workflows for drug submission using healthcare agent service in Copilot Studio: “We have collaborated with Microsoft to build an AI-powered multi-agent decision board using the healthcare agent service in Copilot Studio. This multi-agent decision board revolutionizes how we strategize drug submissions, pricing, and patient targeting for global market access. By simulating expert board discussions and synthesizing diverse data—from regulatory approvals to health economics and real-world evidence—the system streamlines the complex process of securing drug reimbursement. Healthcare agent service helped us get results quicker, empowering teams to make smarter, data-driven decisions without replacing human expertise, which would enable better access to life-changing therapies for patients worldwide. Importantly, this tool is not limited to pharmaceutical companies. It also supports decision-making for health authorities, NGOs, and other stakeholders across the healthcare ecosystem—enabling more informed, collaborative, and impactful choices that benefit public health at large.” — Shay Zohar, local Market Access Director and member of Bayer Pharmaceutical’s global Early Access team Allgemeines Krankenhaus (AKH) Wien, the largest hospital in Vienna, Austria and the Medical University of Vienna collaborated with Microsoft to extend Dragon Copilot with healthcare agent service, to automate pre-anesthesia intake. “Transforming pre-anesthesia assessments with AI agents for greater efficiency has a great potential to decrease the administrative burden on anesthesiologists. In this project we used healthcare agent service to extend Dragon Copilot with AI-powered agents that automate pre-anesthesia intake to enhance clinical documentation, significantly reducing the administrative workload for anesthesiologists. By orchestrating conversational and workflow agents, the solution interacts with patients, completes assessments, checks for data conflicts, and generates clinical notes, all consolidated for physician review in Dragon Copilot.” — Dr. Oliver Kimberger, Professor for Perioperative Information Management at the Department of General Anesthesia and Intensive Care Medicine, AKH Wien. Empowering healthcare innovation Healthcare agent service offers a low-code interface for building and deploying custom AI solutions with chat, compliance and clinical safeguards that support safety and accuracy in generative AI. With seamless integration and the ability to extend the capabilities of Dragon Copilot, you gain the flexibility to tailor solutions to your organization’s evolving needs. Learn more in healthcare agent service in Copilot Studio documentation Explore the possibilities with Microsoft Copilot Studio Expand your knowledge about Microsoft for Healthcare Discover how we are shaping the future of health with cutting-edge solutions and collaborative efforts here Medical Device Disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations. Generative AI Disclaimer: Generative AI does not always provide accurate or complete information. AI outputs do not reflect the opinions of Microsoft. Customers/partners will need to thoroughly test and evaluate whether an AI tool is fit for the intended use and identify and mitigateAgentic AI in Healthcare
Healthcare organizations are at a crossroads where rising patient loads, complex data, and administrative burdens demand new solutions. Agentic AI – AI systems capable of autonomous action – is emerging as a catalyst for transformation, promising to act not just as tools but as collaborative digital team members. Microsoft’s ecosystem of AI technologies provides a robust foundation to harness agentic AI in healthcare. This report offers a comprehensive overview of agentic AI, distinguishes it from traditional AI, and explores its role in clinical workflows, administrative efficiency, patient engagement, and data governance. It also examines how Microsoft’s offerings (Microsoft 365 Copilot, Azure Health Data Services, Microsoft Fabric, Copilot Studio, and more) enable these advances responsibly and in compliance with healthcare regulations like HIPAA.
Copilot Chat: Prompting
To start a new prompt, head over to Copilot Chat and hit the blue chat button in the upper right corner. 🔄 When should I start a new chat? A good rule of thumb: hit that button whenever you're switching contexts or subject areas. This helps keep Copilot focused and prevents information from getting muddled.🍸 🧪 How do I improve my prompts? To get the best results, use the GCSE Formula—that’s: Goal: What do you want Copilot to do? Context: What background info will help? Source: Where should Copilot pull from? Expectations: What kind of output do you want? 🧩 Example Here’s a basic prompt: Give me a concise summary of recent news about Pfizer. Now let’s expand it using the GCSE Formula: Summarize the latest news about Pfizer from reputable sources like Reuters or Bloomberg. Focus on developments in their vaccine pipeline and financial performance. Keep it concise—under 150 words. Let's see how this might look like in practice. This is my prompt: Give me a concise summary of recent news about Pfizer. Let's try expanding this to include our other key ingredients: 🎯 Challenge Try using the GCSE Formula in your next prompt and compare it to using just the goal. See how your results stack up!Allow booking by invitation only? Single-use booking URLs?
We are trying to find a way to allow the public to book appointments for covid vaccines, but we need to pre-screen each person first to determine eligibility. If someone is eligible, I'd like to send them a link to a bookings page to schedule their appointment, but I need to ensure that they can only schedule one appointment using the URL I send. They should not be able to schedule an appointment for a second person using the same link. Is that possible?Copilot Chat: Downloads
On PC and Mac: Follow the download links below to install the Copilot Chat desktop app. Double-click the installer when prompted, and you're in. Windows: Microsoft 365 Copilot - Free download and install on Windows | Microsoft Store MacOS: Microsoft 365 Copilot on the App Store On Mobile: Scan the QR code to download the app to your device. In Your Browser: Prefer not to download anything? You can also access Copilot Chat from Microsoft 365 Copilot Chat. Once you're in, try starting a conversation in the prompt box. Not sure where to begin? No worries—use or tweak one of the suggested prompts to get going. Here are a few other handy entry points:Image Search Series Part 2: AI Methods for the Automation of 3D Image Retrieval in Radiology
Introduction As the use of diagnostic 3D images increases, effective management and analysis of these large volumes of data grows in importance. Medical 3D image search systems can play a vital role by enabling clinicians to quickly retrieve relevant or similar images and cases based on the anatomical features and pathologies present in a query image. Unlike traditional 2D imaging, 3D imaging offers a more comprehensive view for examining anatomical structures from multiple planes with greater clarity and detail. This enhanced visualization has potential to assist doctors with improved diagnostic accuracy and more precise treatment planning. Moreover, advanced 3D image retrieval systems can support evidence-based and cohort-based diagnostics, demonstrating an opportunity for more accurate predictions and personalized treatment options. These systems also hold significant potential for advancing research, supporting medical education, and enhancing healthcare services. This blog offers guidance on using Azure AI Foundry and the recently launched healthcare AI models to design and test a 3D image search system that can retrieve similar radiology images from a large collection of 3D images. Along with this blog, we share a Jupyter Notebook with the the 3D image search system code, which you may use to reproduce the experiments presented here or start you own solution. 3D Image Search Notebook: http://aka.ms/healthcare-ai-examples-mi2-3d-image-search It is important to highlight that the models available on the AI Foundry Model Catalog are not designed to generate diagnostic-quality results. Developers are responsible for further developing, testing, and validating their appropriateness for specific tasks and eventually integrating these models into complete systems. The objective of this blog is to demonstrate how this can be achieved efficiently in terms of data and computational resources. The Problem Generally, the problem of 3D image search can be posed as retrieving cross-sectional (CS) imaging series (3D image results) that are similar to a given CS imaging series (query 3D image). Once posited this way, the key question becomes how to define such similarity? In the previous blog of this series, we worked with radiographs of the chest which constrained the notion of "similar" to the similarity between two 2D images, and a certain class of anatomy. In the case of 3D images, we are dealing with a volume of data, and a lot more variations of anatomy and pathologies, which expands the dimensions to consider for similarity; e.g., are we looking for similar anatomy? Similar pathology? Similar exam type? In this blog, we will discuss a technique to approximate the 3D similarity problem through a 2D image embedding model and some amount of supervision to constrain the problem to a certain class of pathologies (lesions) and cast it as "given cross-sectional MRI image , retrieve series with similar grade of lesions in similar anatomical regions". To build a search system for 3D radiology images using a foundation model (MedImageInsight) designed for 2D inputs, we explore the generation of representative 3D embedding vectors for the volumes with the foundation model embeddings of 2D slices to create a vector index from a large collection of 3D images. Retrieving relevant results for a given 3D image then consists in generating a representative 3D image embedding vector for the query image and searching for similar vectors in the index. An overview of this process is illustrated in Figure 1. Figure 1: Overview of the 3D image search process. The Data In the sample notebook that is provided alongside this blog, we use 3D CT images from the Medical Segmentation Decathlon (MSD) dataset [2-3] and annotations from the 3D-MIR benchmark [4]. The 3D-MIR benchmark offers four collections (Liver, Colon, Pancreas, and Lung) of positive and negative examples created from the MSD dataset with additional annotations related to the lesion flag (with/without lesion), and lesion group (1, 2, 3). The lesion grouping focuses on lesion morphology and distribution and considers the number, length, and volume of the lesions to define the three groups. It also adheres to the American Joint Committee on Cancer's Tumor, Node, Metastasis classification system’s recommendations for classifying cancer stages and provides a standardized framework for correlating lesion morphology with cancer stage. We selected the 3D-MIR Pancreas collection. 3D-MIR Benchmark: https://github.com/abachaa/3D-MIR Since the MSD collections only include unhealthy/positive volumes, each 3D-MIR collection was augmented with volumes randomly selected from the other datasets to integrate healthy/negative examples in the training and test splits. For instance, the Pancreas dataset was augmented using volumes from the Colon, Liver, and Lung datasets. The input images consist of CT volumes and associated 2D slices. The training set is used to create the index, and the test set is used to query and evaluate the 3D search system. 3D Image Retrieval Our search strategy, called volume-based retrieval, relies on aggregating the embeddings of the 2D slices of a volume to generate one representative 3D embedding vector for the whole volume. We describe additional search strategies in our 3D-MIR paper [4]. The 2D slice embeddings are generated using the MedImageInsight foundation model [5-6] from Azure AI Foundry model catalog [1]. In the search step, we generate the embeddings of the 3D query volumes according to the selected Aggregation method (Agg) and search for the top-k similar volumes/vectors in the corresponding 3D (Agg) index. We use the Median aggregation method to generate the 3D vectors and create the associated 3D index. We construct a 3D (Median) index using the training slices/volumes from the 3D-MIR Pancreas collection. Three other aggregation methods are available in the 3D image search notebook: Max Pooling, Average Pooling, and Standard Deviation. The search is performed following the k-Nearest Neighbors algorithm (or k-NN search) to find the k nearest neighbors of a given vector by calculating the distances between the query vector and all other vectors in the collection, then selecting the K vectors with the shortest distances. If the collection is large, the computation can be expensive, and it is recommended to use specific libraries for optimization. We use the FAISS (Facebook AI Similarity Search) library, an open-source library for efficient similarity search and clustering of high-dimensional vectors. Evaluation of the search results The 3D-MIR Pancreas test set consists of 32 volumes: 4 volumes with no lesion (lesion flag/group= -1) 3 volumes with lesion group 1 19 volumes with lesion group 2 6 volumes with lesion group 3 The training set consists of 269 volumes (with and without lesions) and was used to create the index. We evaluate the 3D search system by comparing the lesion group/category of the query volume and the top 10 retrieved volumes. We then compute Precision@k (P@k). Table 1 presents the P@1, P@3, P@5, P@10, and overall Precision. Table 1: Evaluation results on the 3D-MIR Pancreas test set The system accurately recognizes Healthy cases, consistently retrieving the correct label in test scenarios involving non-lesion pancreas images. However, performance varies for different lesion groups, reflecting challenges in precisely identifying smaller lesions (Group 1) or more advanced lesions (Group 3). This discrepancy highlights the complexity of lesion detection and underscores the importance of carefully tuning embeddings or adjusting the vector index to improve retrieval accuracy for specific lesion sizes. Visualization Figure 2 presents four different test queries from the Pancreas test set and the top 5 nearest neighbors retrieved by the volume-based search method. In each row, the first image is the query, followed by the retrieved images ranked by similarity. The visual overlays help in assessing retrieval accuracy; Blue indicates the pancreas organ boundaries, and Red highlights the mark regions corresponding to the pancreas tumor. Figure 2: Top 5 results for different queries from the Pancreas test set Table 2 presents additional results of the volume-based retrieval system [4] on other 3D-MIR datasets/organs (Liver, Colon, and Lung) using additional foundation models: BiomedCLIP [7], Med-Flamingo [8], and BiomedGPT [9]. When considering the macro-average across all datasets, MedImageInsight-based retrieval outperforms substantially other foundation models. Table 2: Evaluation Results on the 3D-MIR benchmark (Liver, Colon, Pancreas, and Lung) These results mirror a use case akin to lesion detection and severity measurement in a clinical context. In real-world applications—such as diagnostic support or treatment planning—it may be necessary to optimize the model to account for particular goals (e.g., detecting critical lesions early) or accommodate different imaging protocols. By refining search criteria, integrating more domain-specific data, or adjusting embedding methods, practitioners can enhance retrieval precision and better meet clinical requirements. Conclusion The integration of 3D image search systems in clinical environment can enhance and accelerate the retrieval of similar cases and provide better context to clinicians and researchers for accurate complex diagnoses, cohort selection, and personalized patient care. This 3D radiology image search blog and related notebook offers a solution based on 3D embedding generation for building and evaluating a 3D image search system using the MedImageInsight foundation model from Azure AI Foundry model catalog. References Model catalog and collections in Azure AI Foundry portal https://learn.microsoft.com/en-us/azure/ai-studio/how-to/model-catalog-overview Michela Antonelli et al. The medical segmentation decathlon. Nature Communications, 13(4128), 2022 https://www.nature.com/articles/s41467-022-30695-9 MSD: http://medicaldecathlon.com/ Asma Ben Abacha, Alberto Santamaría-Pang, Ho Hin Lee, Jameson Merkow, Qin Cai, Surya Teja Devarakonda, Abdullah Islam, Julia Gong, Matthew P. Lungren, Thomas Lin, Noel C. F. Codella, Ivan Tarapov: 3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology. CoRR abs/2311.13752, 2023 https://arxiv.org/abs/2311.13752 Noel C. F. Codella, Ying Jin, Shrey Jain, Yu Gu, Ho Hin Lee, Asma Ben Abacha, Alberto Santamaría-Pang, Will Guyman, Naiteek Sangani, Sheng Zhang, Hoifung Poon, Stephanie Hyland, Shruthi Bannur, Javier Alvarez-Valle, Xue Li, John Garrett, Alan McMillan, Gaurav Rajguru, Madhu Maddi, Nilesh Vijayrania, Rehaan Bhimai, Nick Mecklenburg, Rupal Jain, Daniel Holstein, Naveen Gaur, Vijay Aski, Jenq-Neng Hwang, Thomas Lin, Ivan Tarapov, Matthew P. Lungren, Mu Wei: MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging. CoRR abs/2410.06542, 2024 https://arxiv.org/abs/2410.06542 MedImageInsight: https://aka.ms/mi2modelcard Sheng Zhang, Yanbo Xu, Naoto Usuyama, Hanwen Xu, Jaspreet Bagga, Robert Tinn, Sam Preston, Rajesh Rao, Mu Wei, Naveen Valluri, Cliff Wong, Andrea Tupini, Yu Wang, Matt Mazzola, Swadheen Shukla, Lars Liden, Jianfeng Gao, Angela Crabtree, Brian Piening, Carlo Bifulco, Matthew P. Lungren, Tristan Naumann, Sheng Wang, Hoifung Poon. BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. NEJM AI 2025; 2(1) https://ai.nejm.org/doi/full/10.1056/AIoa2400640 Moor, M., Huang, Q., Wu, S., Yasunaga, M., Dalmia, Y., Leskovec, J., Zakka, C., Reis, E.P., Rajpurkar, P.: Med-flamingo: a multimodal medical few-shot learner. Machine Learning for Health, ML4H@NeurIPS 2023, 10 December 2023, New Orleans, Louisiana, USA. Proceedings of Machine Learning Research, vol. 225, pp. 353–367. PMLR, (2023) https://proceedings.mlr.press/v225/moor23a.html Zhang, K., Zhou, R., Adhikarla, E., Yan, Z., Liu, Y., Yu, J., Liu, Z., Chen, X., Davison, B.D., Ren, H., et al.: A generalist vision–language foundation model for diverse biomedical tasks. Nature Medicine, 1–13 (2024) https://www.nature.com/articles/s41591-024-03185-2 Image Search Series Image Search Series Part 1: Chest X-ray lookup with MedImageInsight | Microsoft Community Hub Image Search Series Part 2: AI Methods for the Automation of 3D Image Retrieval in Radiology | Microsoft Community Hub Image Search Series Part 3: Foundation Models and Retrieval-Augmented Generation in Dermatology | Microsoft Community Hub Image Search Series Part 4: Advancing Wound Care with Foundation Models and Context-Aware Retrieval | Microsoft Community Hub The Microsoft healthcare AI models are intended for research and model development exploration. The models are not designed or intended to be deployed in clinical settings as-is nor for use in the diagnosis or treatment of any health or medical condition, and the individual models’ performances for such purposes have not been established. You bear sole responsibility and liability for any use of the healthcare AI models, including verification of outputs and incorporation into any product or service intended for a medical purpose or to inform clinical decision-making, compliance with applicable healthcare laws and regulations, and obtaining any necessary clearances or approvals.Image Search Series Part 3: Foundation Models and Retrieval-Augmented Generation in Dermatology
Introduction Dermatology is inherently visual, with diagnosis often relying on morphological features such as color, texture, shape, and spatial distribution of skin lesions. However, the diagnostic process is complicated by the large number of dermatologic conditions, with over 3,000 identified entities, and the substantial variability in their presentation across different anatomical sites, age groups, and skin tones. This phenotypic diversity presents significant challenges, even for experienced clinicians, and can lead to diagnostic uncertainty in both routine and complex cases. Image-based retrieval systems represent a promising approach to address these challenges. By enabling users to query large-scale image databases using a visual example, these systems can return semantically or visually similar cases, offering useful reference points for clinical decision support. However, dermatology image search is uniquely demanding. Systems must exhibit robustness to variations in image quality, lighting, and skin pigmentation while maintaining high retrieval precision across heterogeneous datasets. Beyond clinical applications, scalable and efficient image search frameworks provide valuable support for research, education, and dataset curation. They enable automated exploration of large image repositories, assist in selecting challenging examples to enhance model robustness, and promote better generalization of machine learning models across diverse populations. In this post, we continue our series on using healthcare AI models in Azure AI Foundry to create efficient image search systems. We explore the design and implementation of such a system for dermatology applications. As a baseline, we first present an adapter-based classification framework for dermatology images by leveraging fixed embeddings from the MedImageInsight foundation model, available in the Azure AI Foundry model catalog. We then introduce a Retrieval-Augmented Generation (RAG) method that enhances vision-language models through similarity-based in-context prompting. We use the MedImageInsight foundation model to generate image embeddings and retrieve the top-k visually similar training examples via FAISS. The retrieved image-label pairs are included in the Vision-LLM prompt as in-context examples. This targeted prompting guides the model using visually and semantically aligned references, enhancing prediction quality on fine-grained dermatological tasks. It is important to highlight that the models available on the AI Foundry Model Catalog are not designed to generate diagnostic-quality results. Developers are responsible for further developing, testing, and validating their appropriateness for specific tasks and eventually integrating these models into complete systems. The objective of this blog is to demonstrate how this can be achieved efficiently in terms of data and computational resources. The Data The DermaVQA-IIYI [2] dermatology image dataset is a de-identified, diverse collection of nearly 1,000 patient records and nearly 3,000 dermatological images, created to support research in skin condition recognition, classification, and visual question answering. DermaVQA-IIYI dataset: https://osf.io/72rp3/files/osfstorage (data/iiyi) The dataset is split into three subsets: Training Set: 2,474 images associated with 842 patient cases Validation Set: 157 images associated with 56 cases Test Set: 314 images associated with 100 cases Total Records: 2,945 images (998 patient cases) Patient Demographics: Out of 998 patient cases: Sex – F: 218, M: 239, UNK: 541 Age (available for 398 patients): Mean: 31 yrs | Min: 0.08 yrs | Max: 92 yrs This wide range supports studies across all age groups, from infants to the elderly. A total of 2,945 images are associated with the patient records, with an average of 2.9 images per patient. This multiplicity enables the study of skin conditions from different perspectives and at various stages. Image Count per Entry: 1 image: 225 patients 2 images: 285 patients 3 images: 200 patients 4 or more images: 288 patients The dataset includes additional annotations for anatomic location, comprising 39 distinct labels (e.g., back, fingers, fingernail, lower leg, forearm, eye region, unidentifiable). Each image is associated with one or multiple labels. We use these annotations to evaluate the performance of various methods across different anatomical regions. Image Embeddings We generate image embeddings using the MedImageInsight foundation model [1] from the Azure AI Foundry model catalog [3]. We apply Uniform Manifold Approximation and Projection (UMAP) to project high-dimensional image embeddings produced by the MedImageInsight model into two dimensions. The visualization is generated using embeddings extracted from both the DermaVQA training and test sets, which covers 39 anatomical regions. For clarity, only the most frequent anatomical labels are displayed in the projection. Figure 1. UMAP projection of image embeddings produced by the MedImageInsight Model on the DermaVQA dataset. The resulting projection reveals that the MedImageInsight model captures meaningful anatomical distinctions: visually distinct regions such as fingers, face, fingernail, and foot form well-separated clusters, indicating high intra-class consistency and inter-class separability. Other anatomically adjacent or visually similar regions, such as back, arm, and abdomen, show moderate overlap, which is expected due to shared visual features or potential labeling ambiguity. Overall, the embeddings exhibit a coherent and interpretable organization, suggesting that the model has learned to encode both local and global anatomical structures. This supports the model’s effectiveness in capturing anatomy-specific representations suitable for downstream tasks such as classification and retrieval. Enhancing Visual Understanding We explore two strategies for enhancing visual understanding through foundation models. I. Training an Adapter-based Classifier We build an adapter-based classification framework designed for efficient adaptation to medical imaging tasks (see our prior posts for introduction into the topic of adapters: Unlocking the Magic of Embedding Models: Practical Patterns for Healthcare AI | Microsoft Community Hub). The proposed adapter model builds upon fixed visual features extracted from the MedImageInsight foundation model, enabling task-specific fine-tuning without requiring full model retraining. The architecture consists of three main components: MLP Adapter: A two-layer feedforward network that projects 1024-dimensional embeddings (generated by the MedImageInsight model) into a 512-dimensional latent space. This module utilizes GELU activation and Layer Normalization to enhance training stability and representational capacity. As a bottleneck adapter, it facilitates parameter-efficient transfer learning. Convolutional Retrieval Module: A sequence of two 1D convolutional layers with GELU activation, applied to the output of the MLP adapter. This component refines the representations by modeling local dependencies within the transformed feature space. Prediction Head: A linear classifier that maps the 512-dimensional refined features to the task-specific output space (e.g., 39 dermatology classes). The classifier is trained for 10 epochs (approximately 48 seconds) using only CPU resources. Built on fixed image embeddings extracted from the MedImageInsight model, the adapter efficiently tailors these representations for downstream classification tasks with minimal computational overhead. By updating only the adapter components, while keeping the MedImageInsight backbone frozen, the model significantly reduces computational and memory overhead. This design also mitigates overfitting, making it particularly effective in medical imaging scenarios with limited or imbalanced labeled data. A Jupyter Notebook detailing the construction and training of an MedImageInsight -based adapter model is available in our Samples Repository: https://aka.ms/healthcare-ai-examples-mi2-adapter Figure 3: MedImageInsight-based Adapter Model II. Boosting Vision-Language Models with in-Context Prompting We leverage vision-language models (e.g., GPT-4o, GPT-4.1), which represent a recent class of multimodal foundation models capable of jointly reasoning over visual and textual inputs. These models are particularly promising for dermatology tasks due to their ability to interpret complex visual patterns in medical images while simultaneously understanding domain-specific medical terminology. 1. Few-shot Prompting In this setting, a small number of examples from the training dataset are randomly selected and embedded into the input prompt. These examples, consisting of paired images and corresponding labels, are intended to guide the model's interpretation of new inputs by providing contextual cues and examples of relevant dermatological features. 2. MedImageInsight-based Retrieval-Augmented Generation (RAG) This approach enhances vision-language model performance by integrating a similarity-based retrieval mechanism rooted in MedImageInsight (Medical Image-to-Image) comparison. Specifically, it employs a k-nearest neighbors (k-NN) search to identify the top k dermatological training images that are most visually similar to a given query image. The retrieved examples, consisting of dermatological images and their corresponding labels, are then used as in-context examples in the Vision-LLM prompt. By presenting visually similar cases, this approach provides the model with more targeted contextual references, enabling it to generate predictions grounded in relevant visual patterns and associated clinical semantics. As illustrated in Figure 2, the system operates in two phases: Index Construction: Embeddings are extracted from all training images using a pretrained vision encoder (MedImageInsight). These embeddings are then indexed to enable efficient and scalable similarity search during retrieval. Query and Retrieval: At inference time, the test image is encoded similarly to produce a query embedding. The system computes the Euclidean distance between this query vector and all indexed embeddings, retrieving the k nearest neighbors with the smallest distances. To handle the computational demands of large-scale image datasets, the method leverages FAISS (Facebook AI Similarity Search), an open-source library designed for fast and scalable similarity search and clustering of high-dimensional vectors. The implementation of the image search method is available in our Samples Repository: https://aka.ms/healthcare-ai-examples-mi2-2d-image-search Figure 2: MedImageInsight-based Retrieval-Augmented Generation Evaluation Table 1 presents accuracy scores for anatomic location prediction on the DermaVQA-iiyi test set using the proposed modeling approaches. The adapter model achieves a baseline accuracy of 31.73%. Vision-language models perform better, with GPT-4o (2024-11-20) achieving an accuracy of 47.11%, and GPT-4.1 (2025-04-14) improving to 50%. However, incorporating few-shot prompting with five randomly selected in-context examples (5-shot) slightly reduces GPT-4.1’s performance to 48.72%. This decline suggests that unguided example selection may introduce irrelevant or low-quality context, potentially reducing the effectiveness of the model’s predictions for this specialized task. The best performance among the vision-language approaches is achieved using the retrieval-augmented generation (RAG) strategy. In this setup, GPT-4.1 is prompted with five nearest-neighbor examples retrieved using the MedImageInsight-based search method (RAG-5), leading to a notable accuracy increase to 51.60%. This improvement over GPT-4.1’s 50% accuracy without retrieval showcases the relevance of the MedImageInsight-based RAG method. We expect larger performance gains when using a more extensive dermatology dataset, compared to the relatively small dataset used in this example -- a collection of 2,474 images associated with 842 patient cases which served as the basis for selecting relevant cases and similar images. Dermatology is a particularly challenging domain, marked by a high number of distinct conditions and significant variability in skin tone, texture, and lesion appearance. This diversity makes robust and representative example retrieval especially critical for enhancing model performance. The results underscore the importance of example relevance in few-shot prompting, demonstrating that similarity-based retrieval can effectively guide the model toward more accurate predictions in complex visual reasoning tasks. Table 1: Comparative Accuracy of Anatomic Location Prediction on DermaVQA-iiyi Figure 2: Confusion Matrix of Anatomical Location Predictions by the trained MLP adapter: The matrix illustrates the model's performance in classifying wound images across 39 anatomical regions. Strong diagonal values indicate correct classifications, while off-diagonal entries highlight common misclassifications, particularly among anatomically adjacent or visually similar regions such as 'lowerback' vs. 'back' and 'hand' vs. 'fingers'. Figure 3. Examples of correct anatomical predictions by the RAG approach. Each image depicts a case where the model's predicted anatomical region exactly matches the ground truth. Shown are examples from visually and anatomically distinct areas including the eye region, lips, lower leg, and neck. Figure 4. Examples of misclassifications by the RAG approach. Each image displays a case where the predicted anatomical label differs from the ground truth. In several examples, predictions are anatomically close to the correct regions (e.g., hand vs. hand-back, lower leg vs. foot, palm vs. fingers), suggesting that misclassifications often occur between adjacent or visually similar areas. These cases highlight the challenge of precise localization in fine-grained anatomical classification and the importance of accounting for anatomical ambiguity in both modeling and evaluation. Conclusion Our exploration of scalable image retrieval and advanced prompting strategies demonstrates the growing potential of vision-language models in dermatology. A particularly challenging task we address is anatomic location prediction, which involves 39 fine-grained classes of dermatology images, imbalanced training data, and frequent misclassifications between adjacent or visually similar regions. By leveraging Retrieval-Augmented Generation (RAG) with similarity-based example selection using image embeddings from the MedImageInsight foundation model, we show that relevant contextual guidance can significantly improve model performance in such complex settings. These findings underscore the importance of intelligent image retrieval and prompt construction for enhancing prediction accuracy in fine-grained medical tasks. As vision-language models continue to evolve, their integration with retrieval mechanisms and foundation models holds substantial promise for advancing clinical decision support, medical research, and education at scale. In the next blog of this series, we will shift focus to the wound care subdomain of dermatology, and we will release accompanying Jupyter notebooks for the adapter-based and RAG-based methods to provide a reproducible reference implementation for researchers and practitioners. The Microsoft healthcare AI models, including MedImageInsight, are intended for research and model development exploration. The models are not designed or intended to be deployed in clinical settings as-is nor for use in the diagnosis or treatment of any health or medical condition, and the individual models’ performances for such purposes have not been established. You bear sole responsibility and liability for any use of the healthcare AI models, including verification of outputs and incorporation into any product or service intended for a medical purpose or to inform clinical decision-making, compliance with applicable healthcare laws and regulations, and obtaining any necessary clearances or approvals. References Noel C. F. Codella, Ying Jin, Shrey Jain, Yu Gu, Ho Hin Lee, Asma Ben Abacha, Alberto Santamaría-Pang, Will Guyman, Naiteek Sangani, Sheng Zhang, Hoifung Poon, Stephanie L. Hyland, Shruthi Bannur, Javier Alvarez-Valle, Xue Li, John Garrett, Alan McMillan, Gaurav Rajguru, Madhu Maddi, Nilesh Vijayrania, Rehaan Bhimai, Nick Mecklenburg, Rupal Jain, Daniel Holstein, Naveen Gaur, Vijay Aski, Jenq-Neng Hwang, Thomas Lin, Ivan Tarapov, Matthew P. Lungren, Mu Wei: MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging. CoRR abs/2410.06542 (2024) Wen-wai Yim, Yujuan Fu, Zhaoyi Sun, Asma Ben Abacha, Meliha Yetisgen, Fei Xia: DermaVQA: A Multilingual Visual Question Answering Dataset for Dermatology. MICCAI (5) 2024: 209-219 Model catalog and collections in Azure AI Foundry portal https://learn.microsoft.com/en-us/azure/ai-studio/how-to/model-catalog-overview Image Search Series Image Search Series Part 1: Chest X-ray lookup with MedImageInsight | Microsoft Community Hub Image Search Series Part 2: AI Methods for the Automation of 3D Image Retrieval in Radiology | Microsoft Community Hub Image Search Series Part 3: Foundation Models and Retrieval-Augmented Generation in Dermatology | Microsoft Community Hub Image Search Series Part 4: Advancing Wound Care with Foundation Models and Context-Aware Retrieval | Microsoft Community HubImage Search Series Part 4: Advancing Wound Care with Foundation Models and Context-Aware Retrieval
Introduction Wound assessment and management are central tasks in clinical practice, requiring accurate documentation and timely decision-making. Clinicians and nurses often rely on visual inspection to evaluate wound characteristics such as size, color, tissue composition, and healing progress. However, when seeking comparable cases (e.g., to inform treatment choices, validate assessments, or support education), existing search methods have significant limitations. Traditional keyword-based systems require precise terminology, which may not align with the way wounds are described in practice. Moreover, textual descriptors cannot fully capture the variability of visual wound features, resulting in incomplete or imprecise retrieval. Recent advances in computer vision offer new opportunities to address these challenges through both image classification and image retrieval. Automated classification of wound images into clinically meaningful categories (e.g., wound type, tissue condition, infection status) can support standardized documentation and assist clinicians in making more consistent assessments. In parallel, image retrieval systems enable search based on visual similarity rather than textual input alone, allowing clinicians to query databases directly with wound images and retrieve cases with similar characteristics. Together, these AI-based functionalities have the potential to improve case comparison, facilitate consistent monitoring, and enhance clinical training by providing immediate access to relevant examples and structured decision support. The Data The WoundcareVQA dataset is a new multimodal multilingual dataset for Wound Care Visual Question Answering. The WoundcareVQA dataset is available at https://osf.io/xsj5u/ [1] Table 1 summarizes dataset statistics. WoundcareVQA contains 748 images associated with 447 instances (each instance/query includes one or more images). The dataset is split into training (279 instances, 449 images), validation (105 instances, 147 images), and test (93 instances, 152 images). The training set was annotated by a single expert, the validation set by two annotators, and the test set by three medical doctors. Each query is also labeled with wound metadata, covering seven categories: anatomic location (41 classes), wound type (8), wound thickness (6), tissue color (6), drainage amount (6), drainage type (5), and infection status (3). Table 1: Statistics about the WoundcareVQA Dataset We selected two tasks with the highest inter-annotator agreement: Wound Type Classification and Infection Detection (cf. Table 2). Table 3 lists the classification labels for these tasks. Table 2: Inter-Annotator Agreement in the WoundcareVQA Dataset Table 3: Classification Labels for the Tasks: Infection Detection & Wound Type Classification Methods 1. Foundation-Model-based Image Search This approach relies on an image similarity-based retrieval mechanism using a medical foundation model, MedImageInsight [2-3]. Specifically, it employs a k-nearest neighbors (k-NN) search to identify the top k training images most visually similar to a given query image. The image search system operates in two phases: Index Construction: Embeddings are extracted from all training images using a pretrained vision encoder (MedImageInsight). These embeddings are then indexed to enable efficient and scalable similarity search during retrieval. Query and Retrieval: At inference time, the test image is encoded to produce a query embedding. The system computes the Euclidean distances between this query vector and all indexed embeddings, retrieving the k nearest neighbors with the smallest distances. To address the computational demands of large-scale image datasets, the method leverages FAISS (Facebook AI Similarity Search), an open-source library designed for fast and scalable similarity search and clustering of high-dimensional vectors. 2. Vision-Language Models (VLMs) & Retrieval-Augmented Generation (RAG) We leverage vision-language models (e.g., GPT-4o, GPT-4.1), a recent class of multimodal foundation models capable of jointly reasoning over visual and textual inputs. These models can be used for wound assessment tasks due to their ability to interpret complex visual patterns in medical images while simultaneously understanding medical terminology. We evaluate three settings: Zero-shot: The model predicts directly from the query input without additional examples. Few-shot Prompting: A small number of examples (5) from the training dataset are randomly selected and embedded into the input prompt. These paired images and labels provide contextual cues that guide the model's interpretation of new inputs. Retrieval-Augmented Generation (RAG): The system first retrieves the Top-k visually similar wound images using the MedImageInsight-based image search described above. The language model then reasons over the retrieved examples and their labels to generate the final prediction. The implementation of the MedImageInsight-based image search and the RAG method for the infection detection task is available in our Samples Repository: https://aka.ms/healthcare-ai-examples rag_infection_detection.ipynb Evaluation We computed accuracy scores to evaluate the image search methods (Top-1 and Top-5 with majority vote), GPT-4o and GPT-4.1 models (zero-shot), as well as 5-shot and RAG-based methods. Table 4 reports accuracy for wound type classification and infection detection. Figure 1 presents examples of correct and incorrection predictions. Accuracy Image Search Top-1 Image Search Top-5 + majority vote GPT-4o (2023-07-01) GPT-4o (2024-11-20) GPT4.1 (2025-04-14) GPT4.1 5-shot Prompting GPT-4.1- RAG-5 Wound Type 0.7933 0.8333 0.4671 0.4803 0.5066 0.6118 0.7533 Infection 0.6800 0.7267 0.3947 0.3882 0.375 0.7237 0.7697 Table 4: Accuracy Scores for Wound Type Classification & Infection Detection Figure 1: Examples of Correct and Incorrection Predictions (GPT-4.1-RAG-5 Method) For wound type classification, image search with MedImageInsight embeddings performs best, achieving 0.7933 (Top-1) and 0.8333 (Top-5 + majority vote). GPT models alone perform substantially worse (0.4671-0.6118), while GPT-4.1 with retrieval augmentation (RAG-5), which uses the same MedImageInsight-based image search method to retrieve the Top-5 similar cases, narrows the gap (0.7533) but does not surpass direct image search. This suggests that categorical wound type is more effectively captured by visual similarity than by case-based reasoning with vision-language models. For infection detection, the trend reverses. Image search reaches 0.7267 (Top-5 + majority vote), while RAG-5 achieves the highest accuracy at 0.7697. In this case, the combination of visually similar cases with VLM-based reasoning outperforms both standalone image search and GPT prompting. This indicates that infection assessment depends on contextual or clinical cues that may not be fully captured by visual similarity alone but can be better interpreted when enriched with contextual reasoning over retrieved cases and their associated labels. Overall, these findings highlight complementary strengths: foundation-model-based image search excels at categorical visual classification (wound type), while retrieval-augmented VLMs leverage both visual similarity and contextual reasoning to improve performance on more nuanced tasks (infection detection). A hybrid system integrating both approaches may provide the most robust clinical support. Conclusion This study demonstrates the complementary roles of vision-language models in wound assessment. Image search using foundation-model embeddings shows strong performance on categorical tasks such as wound type classification, where visual similarity is most informative. In contrast, retrieval-augmented generation (RAG-5), which combines image search with case-based reasoning by a vision-language model, achieves the best results for infection detection, highlighting the value of integrating contextual interpretation with visual features. These findings suggest that a hybrid approach, leveraging both direct image similarity and retrieval-augmented reasoning, provides the most robust pathway for clinical decision support in wound care. References Wen-wai Yim, Asma Ben Abacha, Robert Doerning, Chia-Yu Chen, Jiaying Xu, Anita Subbarao, Zixuan Yu, Fei Xia, M Kennedy Hall, Meliha Yetisgen. Woundcarevqa: A Multilingual Visual Question Answering Benchmark Dataset for Wound Care. Journal of Biomedical Informatics, 2025. Noel C. F. Codella, Ying Jin, Shrey Jain, Yu Gu, Ho Hin Lee, Asma Ben Abacha, Alberto Santamaría-Pang, Will Guyman, Naiteek Sangani, Sheng Zhang, Hoifung Poon, Stephanie L. Hyland, Shruthi Bannur, Javier Alvarez-Valle, Xue Li, John Garrett, Alan McMillan, Gaurav Rajguru, Madhu Maddi, Nilesh Vijayrania, Rehaan Bhimai, Nick Mecklenburg, Rupal Jain, Daniel Holstein, Naveen Gaur, Vijay Aski, Jenq-Neng Hwang, Thomas Lin, Ivan Tarapov, Matthew P. Lungren, Mu Wei: MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging. CoRR abs/2410.06542 (2024) Model catalog and collections in Azure AI Foundry portal https://learn.microsoft.com/en-us/azure/ai-studio/how-to/model-catalog-overview Image Search Series Image Search Series Part 1: Chest X-ray lookup with MedImageInsight | Microsoft Community Hub Image Search Series Part 2: AI Methods for the Automation of 3D Image Retrieval in Radiology | Microsoft Community Hub Image Search Series Part 3: Foundation Models and Retrieval-Augmented Generation in Dermatology | Microsoft Community Hub Image Search Series Part 4: Advancing Wound Care with Foundation Models and Context-Aware Retrieval | Microsoft Community Hub
How Microsoft Dragon Copilot Uses The Azure Health Data Services De-Identification Service
Empowering physician productivity through secure AI Microsoft developed Dragon Copilot to revolutionize real-time clinical documentation. Using clinically adapted generative AI, it listens to patient-clinician conversations and automatically generates draft clinical notes, freeing physicians to focus on what matters most: their patients. Dragon Copilot also allows clinicians to get the information they need when they need it and automates many other tasks such as initiating orders or writing draft patient after-visit summaries. The tool eliminates the burden of manual note-taking and multiple other clicks in the EMR, boosting efficiency, and reducing burnout, all of which are critical challenges in healthcare. With strong market traction across hospitals and physician practices across the USA, Dragon Copilot, previously known as Dragon Ambient eXperience (DAX) Copilot, has become a trusted productivity engine for healthcare organizations. In a field where protecting patient data is critical , privacy is paramount. Dragon Copilot’s deep commitment to data privacy, however, requires a strategic partner like the de-identification service to support safe and responsible AI development at scale. How the Azure Health Data Services de-identification service empowers Dragon Copilot Dragon Copilot operates at the intersection of audio capture, natural language generation (NLG), and clinical workflows. Its data pipelines include highly sensitive patient health information. As a result, Microsoft has invested in the Azure Health Data Services de-identification service to de-identify millions of patient transcripts and notes to uphold strict privacy standards and deliver secure, scalable clinical documentation. De-identifying unstructured text like clinical notes is particularly challenging due to the complexity and variability of how Protected Health Information (PHI) appears in real-world clinical documentation. References to dates like “Christmas” or “New Year’s Eve,” names, locations, and other identifiers are often embedded in free text in unpredictable ways. The Azure Health Data Services de-identification service is purpose-built to handle these nuances. It accurately identifies and replaces patient names while distinguishing them from doctors’ names, and it can also detect and tag the names of family members or close contacts mentioned in the clinical narrative. The service also retains the format of the dates presents in clinical notes, shifting them by a random number within a 45-day window and surrogates holidays with replacements close in seasonality. A key strength of the de-identification service is its use of surrogation, where sensitive terms are replaced with realistic, context-appropriate substitutes. This approach, used in services like Dragon Copilot, helps ensure clinical notes remain readable and useful while concealing real PHI in plain sight, strengthening privacy without sacrificing usability. Connecting to Microsoft Fabric for scalable analytics Once Dragon Copilot generates draft clinical notes, the data can be securely ingested into Microsoft Fabric, a unified data platform built for analytics and governance. Within Fabric, healthcare organizations can centralize and manage de-identified data using OneLake, making it accessible for advanced analytics, operational reporting, and research. Azure Health Data Services play a critical role in this ecosystem by ensuring that sensitive PHI is de-identified before analysis, allowing healthcare agents to extract meaningful insights, identify trends, and optimize care delivery without compromising patient privacy. Use Cases unlocked through partnering with the Azure Health Data Services de-identification service Azure Health Data Services de-identification has become a critical component of the Dragon Copilot data ingestion pipeline. Our service supports several teams within Dragon Copilot: Research Enablement: De-identified data fuels AI model building, success tracking, and product improvement—without exposing sensitive patient data. AI Model Quality & Evaluation: De-identified data supports safe iteration and experimentation while preserving important context (i.e. gender, timeline, and more). What makes Azure Health Data Services de-identification service stand out Dragon Copilot builds on the consistency, robustness, and seamless integration offered by Azure Health Data Services' de-identification capabilities. This service is purpose-built for healthcare and plays a critical role in enabling Dragon Copilot to uphold the highest privacy standards while continuing to innovate. Key strengths of the service include: Context Preservation: Maintains formatting and context alignment, which are essential for clinical accuracy. Surrogation Support: Replaces PHI with realistic pseudonyms to ensure de-identified data remains useful for model training. Beyond HIPAA Compliance: De-identifies 27 categories of PHI, surpassing HIPAA’s 18 identifiers, to support more comprehensive privacy protection. This foundation allows Dragon Copilot to scale responsibly, ensuring both compliance and usability in real-world clinical settings. Looking Ahead: Where Dragon Copilot is going with de-identification As Dragon Copilot expands and continues to add new capabilities, Azure Health Data Services de-identification service will continue to be a foundational piece of their AI development lifecycle. For Dragon Copilot, de-identification isn’t just a checkbox, it’s a catalyst for innovation. Learn more about the Azure Health Data Services De-identification service
