Microsoft is rolling out new AI-powered tools for the healthcare industry. The company unveiled healthcare-specific models and an AI assistant service aimed at boosting efficiency and enhancing patient care.
The new "healthcare assistant service" allows healthcare organizations to create custom AI agents for patients or medical staff. Microsoft says these can be used for tasks like scheduling appointments, managing clinical trials, or triaging patients.
According to Microsoft, the service aims to address healthcare challenges like staffing shortages, rising costs, and growing demands on patient care. Automating administrative work and supporting decision-making could reduce burdens on clinical staff.
Video: Microsoft
New AI models for medical imaging
Microsoft also introduced a set of AI models for medical imaging. These allow healthcare groups to develop AI solutions tailored to their needs without training models from scratch, the company says.
The new models include MedImageInsight for image analysis and classification, MedImageParse for image segmentation and CXRReportGen for generating structured reports from X-ray images.
These models aim to help healthcare and life sciences organizations experiment with AI despite limited resources. They cover various imaging types and capabilities, from finding similar images to developing specialized adapters and integrating large language models like GPT-4o for cross-modality insights.
"The development of foundational AI models in pathology and medical imaging is expected to drive significant advancements in cancer research and diagnostics," says Dr. Carlo Bifulco, Chief Medical Officer at Providence Genomics.
Healthcare data solutions in Microsoft Fabric
In addition, Microsoft announced the general availability of healthcare data solutions in its Microsoft Fabric platform. This allows organizations to combine healthcare data from multiple sources into a unified data store for analytics and AI.
The solution supports industry standards like FHIR and DICOM. It provides tools for data transformation and enrichment, as well as visualization and patient cohort analysis capabilities.
