This module develops the evaluative skills needed to assess whether medical imaging studies meet the quality and completeness thresholds required for multimodal model use in Microsoft Foundry (formerly Azure AI Studio), focusing on artifact identification, series completeness, and low-information input detection. Learners apply a structured readiness evaluation framework to a provided set of sample imaging studies and produce a readiness evaluation summary with clinician/MD review flags.

This module introduces the modality-specific data characteristics of CT, MRI, and related imaging types and explains how those characteristics affect multimodal AI processing and interpretation boundaries in Microsoft Foundry (formerly Azure AI Studio) workflows. Learners will be able to document a modality characteristics reference that identifies how CT and MRI study attributes influence AI processing, comparability, and interpretation scope. Understanding modality-specific data characteristics is the conceptual foundation for every downstream imaging readiness, pipeline design, and model evaluation task in a healthcare AI workflow.

This module develops the analytical skills needed to examine the core DICOM hierarchy—Patient, Study, Series, and Instance—and to review the metadata and linked workflow artifacts associated with an imaging study, including annotations, segmentations, radiology reports, orders, and encounter context. Learners will be able to produce a structural review document for a provided imaging scenario dataset, correctly separating each structural component and identifying downstream reporting dependencies. Understanding study structure is essential for any healthcare AI role responsible for verifying that imaging data is correctly organized before entering a multimodal pipeline or model evaluation workflow.

This module covers how to interpret MRI sequence characteristics in the context of Microsoft Foundry (formerly Azure AI Studio) multimodal workflows, explaining what MRI sequence types are, how they vary, and how those variations affect model inputs, dataset comparability, and downstream use in bounded clinical AI scenarios. Learners will be able to generate a sequence-characteristics summary for a provided MRI dataset that documents how sequence variations influence multimodal workflow inputs. Understanding MRI sequence characteristics is prerequisite knowledge for evaluating MRI dataset suitability and designing reliable AI-ready data preparation workflows.

This module develops the evaluative skills needed to assess whether an MRI dataset meets the suitability requirements for bounded AI training or inference tasks in Microsoft Foundry (formerly Azure AI Studio), focusing on sequence completeness, motion artifact presence, and protocol consistency. Learners will be able to apply a structured MRI suitability evaluation framework to a provided scenario dataset, identify issues that may degrade model performance or require clinician/MD sign-off, and produce a documented suitability assessment. Evaluating MRI dataset suitability before data enters a training or inference workflow prevents quality-related model failures and supports responsible clinical AI adoption.

This module covers how to interpret the key acquisition and reconstruction characteristics of CT imaging studies, such as slice thickness, contrast phase, and reconstruction choices, and how those characteristics determine whether CT inputs are suitable for Microsoft Foundry (formerly Azure AI Studio) AI workflows. Learners will be able to document a CT acquisition and reconstruction characteristics summary for a provided scenario dataset, correctly identifying how each characteristic affects AI-ready input quality. Understanding these characteristics is a prerequisite for evaluating CT protocol consistency and designing reliable AI inference workflows.

This module develops the evaluative skills needed to assess CT study consistency for multimodal analysis in Microsoft Foundry (formerly Azure AI Studio), focusing on protocol variation, incomplete series, and artifact patterns that may degrade bounded inference workflow reliability. Learners will be able to apply a structured CT protocol review framework to a provided scenario dataset, identify consistency issues across all three dimensions, and produce a CT protocol review document with findings and recommendations. Evaluating CT study consistency before data enters a multimodal inference workflow is a critical quality gate that protects output reliability and supports responsible AI deployment.

In this project, learners produce an imaging dataset readiness assessment, a professional artifact that integrates image analysis, medical imaging data features, MRI sequence interpretation, and CT protocol evaluation for a single realistic healthcare AI scenario. Learners will be able to apply four skills to evaluate, document, and recommend a complete imaging dataset readiness position for a bounded Microsoft Foundry (formerly Azure AI Studio) multimodal AI workflow. Producing this kind of integrated readiness assessment is a core professional output for healthcare AI coordination roles, where responsibility for imaging data quality spans multiple modalities, quality dimensions, and clinical review requirements.