Cancer diagnosis and treatment planning involve many parts. Imaging data like CT scans, MRIs, and pathology slides give pictures of tumors’ size, location, and shape. Genomic data shows mutations and markers that help guide treatments. Clinical notes and patient history in electronic health records (EHRs) add important details about past treatments, allergies, and other illnesses.
Usually, these types of data are kept separate. Doctors have to collect and understand information from different systems themselves. This can cause interruptions in workflow, inefficiency, and can take a long time—often between 1.5 to 2.5 hours for each patient. Cancer cases are complex, so sometimes it takes over three hours just to put patient histories in order.
Multimodal AI means computer systems that can use and combine different types of health data. These include medical images, genetic information, and clinical notes. This lets the system look at the patient’s condition from many angles at once.
Deep learning and machine learning methods help these models pick out useful details from mixed data. They can analyze images to find tumor features, study genetics for mutations, and read clinical documents using language processing. When this data is combined, it helps predict how the disease will progress, improve cancer staging, and find the best personalized treatments.
A big step forward is the creation of platforms that bring together many AI systems. Each AI specializes in one kind of data. For example, Microsoft Azure AI Foundry has an open platform that integrates imaging (DICOM format), pathology slides, genetic tests, and EHRs.
This setup cuts down repeated manual work and helps tumor boards meet quicker by shrinking hours of review into minutes.
Many top US cancer centers like Stanford Health Care, Johns Hopkins, Providence Genomics, the University of Wisconsin, and Mass General Brigham are testing and using these AI systems. Dr. Mike Pfeffer, CIO at Stanford Health Care, said that AI summaries are helping tumor board meetings by giving doctors quick access to all patient information safely stored on Azure.
Dr. Joshua Warner from UW Health said that this technology changes long patient reviews into shorter ones. This lets medical teams spend more time on tough clinical decisions and less on paperwork.
Healthcare practices in the US benefit by improving teamwork among oncologists, radiologists, pathologists, and researchers. AI helps find hard-to-spot data like clinical trial options, treatment guidelines, and real-world evidence that doctors might miss in busy settings.
These AI tools also work smoothly with popular platforms like Microsoft Teams and Word. This fits well with existing work habits, reduces the need for extra training, and is easier to use in big medical centers and smaller hospitals alike.
Even though multimodal AI shows promise, there are still problems to solve in US healthcare:
To meet these challenges, the healthcare agent orchestrator supports standards like Fast Healthcare Interoperability Resources (FHIR) and Microsoft Fabric. It also focuses on explainability so doctors can trace AI conclusions back to patient records.
Healthcare workers want to reduce paperwork so doctors can spend more time with patients. AI tools like Simbo AI’s phone automation handle calls, appointment booking, and routing. This lowers phone traffic for staff and improves patient contact—very important in cancer care where quick communication matters.
Inside clinics, AI automates tasks like data pulling, clinical documentation, and reports. Multimodal AI agents together cut down repeated jobs like reviewing charts, collecting lab results, and entering data. For example, the patient history agent reduces hours of chart work to a few minutes. Radiology and pathology agents provide fast second opinions. This helps diagnosis without holding up care.
These improvements speed up tumor boards because relevant clinical data and guidelines are ready ahead of time. Tools connected to Microsoft Teams allow real-time chats supported by AI that can answer questions, find studies, and suggest treatments on the spot.
For practice leaders and IT managers, AI means better operational flow, smarter use of staff, and shorter wait times for patients needing treatment decisions.
Personalized medicine in cancer needs detailed knowledge of each patient’s genes, tumor biology, and treatment past. Multimodal AI helps by combining imaging that shows tumor traits, genomic info about drug targets, and clinical notes about treatments used.
This combined view lets doctors:
The future of cancer care in the US depends on these tools. They should help increase the number of patients getting personalized plans, which now is less than 1%.
Several US institutions are working on multimodal AI and using it in cancer care. Tech companies like Microsoft help by providing cloud systems and AI tools to speed up research and use.
These efforts are often supported by government funds and industry partnerships. This shows growing interest in the US healthcare system to improve results and workflow using multimodal AI.
Medical practice administrators, IT managers, and owners in US cancer care see multimodal AI models as an important step. Even with technical and legal challenges, ongoing work is making these tools easier to access, combine, and trust in clinical work.
Investing in AI systems that link imaging, genetics, and EHRs can cut review times, improve teamwork, and back personalized care plans. Adding front-office AI like Simbo AI tools also streamlines operations, improves patient communication, and uses resources better.
By using these new tools, US healthcare groups can better handle the growing need for cancer management while improving care quality and how smoothly their systems work.
The healthcare agent orchestrator is a platform available in the Azure AI Foundry Agent Catalog designed to coordinate multiple specialized AI agents. It streamlines complex multidisciplinary healthcare workflows, such as tumor boards, by integrating multimodal clinical data, augmenting clinician tasks, and embedding AI-driven insights into existing healthcare tools like Microsoft Teams and Word.
It leverages advanced AI models that combine general reasoning with healthcare-specific modality models to analyze and reason over various data types including imaging (DICOM), pathology whole-slide images, genomics, and clinical notes from EHRs, enabling actionable insights grounded on comprehensive multimodal data.
Agents include the patient history agent organizing data chronologically, the radiology agent for second reads on images, the pathology agent linked to external platforms like Paige.ai’s Alba, the cancer staging agent referencing AJCC guidelines, clinical guidelines agent using NCCN protocols, clinical trials agent matching patient profiles, medical research agent mining medical literature, and the report creation agent automating detailed summaries.
By automating time-consuming data reviews, synthesizing medical literature, surfacing relevant clinical trials, and generating comprehensive reports efficiently, it reduces preparation time from hours to minutes, facilitates real-time AI-human collaboration, and integrates seamlessly into tools like Teams, increasing access to personalized cancer treatment planning.
The platform connects enterprise healthcare data via Microsoft Fabric and FHIR data services and integrates with Microsoft 365 productivity tools such as Teams, Word, PowerPoint, and Copilot. It supports external third-party agents via open APIs, tool wrappers, or Model Context Protocol endpoints for flexible deployment.
Explainability grounds AI outputs to source EHR data, which is critical for clinician validation, trust, and adoption especially in high-stakes healthcare environments. This transparency allows clinicians to verify AI recommendations and ensures accountability in clinical decision-making.
Leading institutions like Stanford Medicine, Johns Hopkins, Providence Genomics, Mass General Brigham, and University of Wisconsin are actively researching and refining the orchestrator. They use it to streamline workflows, improve precision medicine, integrate real-world evidence, and evaluate impacts on multidisciplinary care delivery.
Multimodal AI models integrate diverse data types — images, genomics, text — to produce holistic insights. This comprehensive analysis supports complex clinical reasoning, enabling agents to handle sophisticated tasks such as cancer staging, trial matching, and generating clinical reports that incorporate multiple modalities.
Developers can create, fine-tune, and test agents using their own models, data sources, and instructions within a guided playground. The platform offers open-source customization, supports integration via Microsoft Copilot Studio, and allows extension using Model Context Protocol servers, fostering innovation and rapid deployment in clinical settings.
The orchestrator is intended for research and development only; it is not yet approved for clinical deployment or direct medical diagnosis and treatment. Users are responsible for verifying outputs, complying with healthcare regulations, and obtaining appropriate clearances before clinical use to ensure patient safety and legal compliance.
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