AI and Vision Techniques

Microsoft via Coursera

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Overview

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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.

Syllabus

Multimodal AI Workflows: How Foundation Models Connect Imaging and Text
- This module introduces how multimodal AI systems ingest and relate radiology images, clinical notes, and structured patient data to generate outputs for healthcare administration use cases such as triage support, case summarization, and imaging-driven care coordination. Hands-on activities in this course require access to a Microsoft Foundry / Azure AI Studio environment with the necessary permissions, including active Contributor permissions to configure and run Vision Workflows
Multimodal AI Workflows: Analyzing Use Cases for Foundation-Model Assistance
- This module develops the analytical skill of distinguishing AI-assisted tasks from tasks that require clinician judgment or administrative controls, using real medical imaging workflow scenarios as the frame for decision-making.
Multimodal AI Workflows: Evaluating AI-Generated Outputs
- This module builds the evaluation skills needed to assess AI-generated outputs for quality, traceability, and suitability for deployment in governed healthcare environments, using prepared Microsoft Foundry output scenarios and defined acceptance criteria.
ML for Imaging: Run a Supervised Learning Workflow
- This module covers how to configure and run a supervised learning workflow in Microsoft Foundry (formerly Azure AI Studio) using labeled multimodal datasets—connecting imaging studies, clinical metadata, and patient records—to generate imaging-related prediction outputs.
ML for Imaging: Analyze Feature Contributions
- This module develops the skill of interpreting model behavior by examining how different multimodal input types—imaging-derived variables, report text signals, and structured health data—contribute to prediction outcomes in a bounded healthcare use case.
ML for Imaging: Evaluate Model Performance
- This module builds the ability to interpret ML model performance outputs using healthcare-relevant metrics—precision, recall, F1, ROC-AUC—and to identify error patterns that affect deployment readiness for imbalanced imaging classification tasks.
Pretrained Imaging Models: Configure Deep Learning Workflows
- This module covers how to select pretrained deep learning architectures and configure training settings in Microsoft Foundry (formerly Azure AI Studio) for bounded medical imaging tasks—without building models from first principles.
Pretrained Imaging Models: Analyze Deep Learning Failure Modes
- This module develops the ability to identify and analyze failure modes in deep learning outputs—specifically missed targets, false-positive signals, and modality-specific weaknesses—using prepared Microsoft Foundry output comparison scenarios.
Pretrained Imaging Models: Evaluate Transfer-Learning Strategies
- This module builds the judgment to evaluate transfer-learning options in Microsoft Foundry—determining when a pretrained model can be used as-is versus when additional fine-tuning is needed for a bounded medical imaging workflow.
Imaging Model Outputs: Configure Computer Vision Workflows
- This module covers how to configure computer vision prompts and model settings in Microsoft Foundry to support bounded detection, classification, and localization tasks for radiology images—within defined clinical-use constraints and without independent clinical interpretation.
Imaging Model Outputs: Analyze Visual Model Outputs
- This module builds the analytical skill of reviewing computer vision outputs to distinguish between valid image-level findings, regional cues, and artifact-related misreads—a critical competency for healthcare administrators who review or approve AI-assisted radiology workflow outputs.
GenAI Module: Generative AI in Medical Imaging Workflows
- This module helps learners apply generative AI tools to the multimodal AI and computer vision skills developed in LC 1. It covers how to use AI-assisted prompting to accelerate imaging workflow analysis, how to evaluate generative AI outputs for accuracy and clinical appropriateness, and how to identify where GenAI augmentation adds value versus where human review remains essential.
Project Module: Evaluate a Multimodal AI Workflow for a Healthcare Use Case
- This project integrates the four core skills—multimodal AI workflow explanation, ML prediction task application, deep learning output analysis, and computer vision output review—in a single realistic healthcare AI evaluation scenario. Learners produce a professional AI workflow evaluation report that mirrors the kind of analysis a healthcare administration professional would prepare before a deployment review meeting.

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