This module develops the analytical and evaluative skills needed to assess whether NLP-extracted clinical concepts are accurately linked to corresponding imaging cases and whether extraction outputs meet domain-specific quality standards in Microsoft Foundry (formerly Azure AI Studio). Learners will be able to identify report-to-image alignment gaps, including contradictions, missing context, and weak linkage, and evaluate extraction quality using criteria such as terminology fidelity, summary completeness, and hallucination risk. These skills are essential for healthcare AI professionals responsible for verifying that multimodal datasets are trustworthy before entering model training or clinical evaluation workflows.

This module covers how health informatics principles guide the organization of multimodal patient data—linking encounters, imaging orders, radiology reports, and imaging identifiers into coherent datasets for Microsoft Foundry (formerly Azure AI Studio) workflows. Learners will be able to apply a structured data organization approach to a provided scenario dataset, correctly linking data elements across the encounter, order, report, and imaging identifier dimensions. Understanding how multimodal patient data is organized at the informatics layer is essential before attempting to analyze data flows or evaluate dataset fitness for AI use.

This module develops the analytical skills to trace multimodal clinical data flows—by following how imaging results, narrative reports, and patient records move through healthcare administration processes—and to evaluate whether linked datasets are suitable for Microsoft Foundry (formerly Azure AI Studio) use cases. Learners will be able to produce a clinical data-flow map and an information-model fit assessment verifying patient context, timing, and care-process relevance. These skills are critical for healthcare AI professionals who must verify that data reaching a model evaluation workflow is complete, timely, and clinically meaningful.

This module covers how to design data pipelines that connect imaging studies, clinical text, metadata, and governed datasets for bounded medical imaging AI workflows in Microsoft Foundry (formerly Azure AI Studio).

This module focuses on evaluating whether a designed or configured data pipeline is ready to support a medical imaging AI project in Microsoft Foundry (formerly Azure AI Studio), using a structured readiness framework covering completeness, modality coverage, transformation logic, and downstream evaluation needs.

This module covers how to apply data integration methods to link imaging data, radiology reports, patient metadata, and precision-medicine context into a unified multimodal dataset for Microsoft Foundry workflows. Learners will be able to apply a structured integration approach to a provided scenario dataset, correctly executing linkage across all four data types and documenting the integration logic. Producing a well-integrated multimodal dataset is the final data preparation step before model evaluation, and the quality of that integration directly determines the reliability of everything downstream.

This module develops the analytical skills to identify and document integration gaps in multimodal healthcare datasets before they enter model evaluation workflows in Microsoft Foundry. Learners will be able to apply a structured gap analysis approach to a provided integrated dataset, identifying missing identifiers, weak report-to-image linkage, inconsistent metadata, and context loss, and produce a gap analysis report with remediation recommendations. Catching integration gaps at this stage, rather than after model training has begun, protects data quality, model reliability, and project governance across the full multimodal AI workflow.

In this project, learners produce a multimodal healthcare data package. This is a professional artifact that integrates NLP extraction, data flow analysis, pipeline design, and integration gap review for a single realistic healthcare AI scenario. Learners will be able to apply four skills to evaluate, document, and recommend a comprehensive multimodal data-preparation approach for a bounded Microsoft Foundry (formerly Azure AI Studio) workflow. Producing this kind of integrated data package is a core professional output for healthcare AI coordination roles, where responsibility for data quality spans multiple sources, tools, and governance requirements.