Microsoft Foundry for Medical Imaging & Multimodal Data

Microsoft via Coursera Professional Certificate

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Healthcare AI is transforming radiology, clinical informatics, and care coordination; the professionals who can evaluate, manage, and govern these systems are in high demand. This program develops the practical AI skills healthcare administration professionals need to work confidently with medical imaging data, configure AI models, and uphold responsible data practices in governed clinical environments using Microsoft Foundry. In this professional certificate, you will learn to evaluate multimodal AI workflows connecting radiology images, clinical notes, and patient data; apply machine learning and computer vision models to imaging prediction tasks; extract clinical text using NLP; design healthcare data pipelines; assess CT and MRI imaging datasets for AI readiness; and implement data governance and privacy controls aligned with HIPAA requirements. This program is non-programming and scenario-based, with no coding experience required. All projects are completed using guided scenario materials and AI-graded activities, ensuring every learner can produce professional, portfolio-ready artifacts regardless of live platform access.

Syllabus

Course 1: AI and Vision Techniques
Course 2: Healthcare Data and Informatics
Course 3: Imaging Modalities and Analysis
Course 4: Operational Excellence and Governance
Course 5: Launch Your Healthcare AI Career

Courses

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Build a foundation through applied skills for working with multimodal AI and computer vision in Microsoft Foundry (formerly Azure AI Studio). Start by explaining how multimodal AI workflows connect radiology images, clinical notes, and patient data, then review guided supervised-learning and deep-learning workflow examples, identify key configuration choices, interpret model-output patterns, and analyze computer vision outputs within bounded healthcare-use constraints. You will evaluate prepared AI workflows for operational readiness, identify failure modes in imaging model outputs, and analyze visual model results to distinguish valid findings from artifact-related misreads. A hands-on capstone project and a GenAI literacy module round out the course. By the end of this course, you will be able to evaluate multimodal AI workflows for relevance, traceability, and fit; assess pretrained deep learning model configurations; analyze computer vision outputs across CT and MRI studies; and apply generative AI tools responsibly to accelerate imaging workflow analysis. No ML coding experience required.
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Develop the data skills needed to work with multimodal healthcare datasets in Microsoft Foundry (formerly Azure AI Studio). Apply NLP workflows to extract clinical text elements from radiology reports, analyze healthcare data flows across care systems, design data pipelines for medical imaging AI workflows, and integrate multimodal healthcare datasets. Close with a hands-on project integrating all four data and informatics skills. You will produce professional artifacts, including NLP extraction evaluations, data flow maps, pipeline design documents, and integration gap reports, that reflect real deliverables in healthcare AI coordination roles. The course closes with an integrative capstone project in which you build and evaluate a complete multimodal healthcare data package. No coding experience required.
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Develops the imaging literacy and modality-specific analytical skills needed to work effectively with CT, MRI, and other radiology data in Microsoft Foundry (formerly Azure AI Studio) workflows. Review and validate AI clinical imaging outputs, explain medical imaging data characteristics, interpret MRI sequence data, and evaluate CT imaging protocols, building the modality-aware judgment that underpins reliable AI-assisted imaging analysis. No clinical radiology background is required. All activities focus on imaging data readiness, modality characteristics, and workflow suitability, not independent clinical interpretation or diagnosis. Clinically meaningful outputs always remain subject to qualified clinician and radiologist review. A capstone project integrates all four skills into a portfolio-ready imaging dataset readiness assessment.
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This short course helps learners translate their Microsoft Foundry (formerly Azure AI Studio), multimodal healthcare-data, imaging-readiness, and governance skills into career action for healthcare AI coordination, clinical informatics, health IT, data-governance, and AI implementation-support roles. You’ll learn how to highlight your technical AI skills on your resume, confidently discuss healthcare data and governance capabilities in interviews, and identify roles and organizations where Microsoft Foundry (formerly Azure AI Studio) and clinical informatics skills are in demand.
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Healthcare AI projects fail governance reviews — not because models underperform, but because data, privacy controls, and platform configurations don't hold up to scrutiny. This course gives you the skills to make sure yours do. You'll apply data governance controls in Microsoft Foundry (formerly Azure AI Studio) to classify, document, and permission multimodal medical datasets. You'll evaluate dataset lineage and access decisions for auditability and policy alignment. You'll apply de-identification and minimum-necessary practices to imaging metadata, report text, and patient-linked attributes, and evaluate re-identification exposure before a workflow proceeds. You'll review Microsoft Foundry project-configuration scenarios, analyze deployment pathway options, and evaluate monitoring and cost-performance tradeoffs for medical imaging AI workloads. By the end, you'll produce governance artifacts, privacy risk assessments, and configuration review summaries that reflect real healthcare AI governance responsibilities. A working understanding of healthcare data concepts and basic cloud platform familiarity is helpful; no prior governance engineering background is required.