Data Standard Framework for AI-based Diagnostics

Executive summary

A robust data framework requires collective stewardship across the data lifecycle to ensure equitable, transparent, and impactful AI-driven diagnostics for all communities.

This Data Framework for AI-Based Diagnostics is designed to guide ministries of health, implementing partners and digital health developers by providing a comprehensive, structured blueprint to support the ethical, equitable, and technically sound implementation of AI technologies in healthcare diagnostics. This framework addresses the full data lifecycle—ranging from data collection to annotation, validation, sharing, monitoring, and reuse—while embedding governing principles of privacy, interoperability, and inclusivity.

The primary objective of this framework is to close the digital gap in healthcare, especially in low- and middle-income settings, by ensuring that AI-based diagnostic systems are built and deployed based on high-quality, diverse, and representative datasets that are managed responsibly and transparently.

This framework is rooted in global standards and principles such as FAIR (Findable, Accessible, Interoperable, and Reusable), and allows inclusion of ethical data governance norms, including data privacy, consent, equity, and responsible reuse. While technically comprehensive, the framework is also grounded in a human-centered approach that prioritizes data equity, diversity, and transparency.

Structured across six core pillars and governing principles, the framework provides guidance on:

Governing Principles: Covering secured data storage, infrastructure, governance, and privacy aligned with legal and ethical norms.

Data Collection: Promoting demographic and clinical diversity, inclusive data types, and establishing standardized processes for digitization, terminology, and language localization.

Data Cleaning and Validation: Implementing automated pipelines for data quality checks, error reporting, and integration with health data systems.

Data Annotation and Structuring: Enabling supervised learning through interoperable formats, annotation tools, and controlled vocabularies.

Data Integration and Harmonization: Encouraging multi-modal data merging using APIs and principles of FAIR to improve data utility and comparability.

Data Sharing and Reuse: Ensuring responsible access, licensing, and version control to support open science while protecting sensitive information.

Continuous Monitoring and Feedback: Tracking data drift, maintaining model performance, and fostering feedback loops to support ongoing improvement.

This framework serves as a strategic reference for shaping national and institutional approaches to AI in diagnostics. Though developed for AI-based diagnostics, its principles are broadly applicable across sectors and geographies, particularly in advancing data justice and digital health equity.

Governing Principles

The Governing Principles align actions with core values, protect stakeholders’ rights, and build trust in technology.

The governing principles of a data framework for AI-based diagnostics are designed to ensure data quality, security, ethical use, and long-term sustainability.

These include robust storage and versioning strategies that combine hybrid (cloud and local) storage systems, encryption, regular backups, access logs, and dataset versioning protocols to maintain data integrity over time.

Data governance and privacy are equally critical, involving mechanisms such as informed consent, anonymization, and de-identification protocols, access controls, and alignment with national and international data protection laws.

Ethical oversight, data ownership clarity, and the presence of review boards further strengthen responsible data use.

Infrastructure readiness—including reliable connectivity, data centers, hardware, power supply, supply chain logistics, and technical support—ensures the operability and scalability of AI systems in real-world healthcare settings.

Together, these principles establish a secure, compliant, and equitable environment for developing and deploying AI in diagnostics, especially in resource-limited settings as shown in Figure 1.

FIGURE 1     Governing Principle

Data Framework for AI-based Diagnostics

We have provided a form at the end of this page for receiving your feedback on the information we have presented.

Now that we’ve walked through the governing principles that lay the foundation, let’s move forward and explore the core structure of the data framework itself.

The data framework for AI-driven diagnostics is built on six interconnected pillars that ensure ethical, efficient, and globally aligned data practices. It begins with Data Collection, emphasizing inclusivity, diverse sources, and standardized formats aligned with international standards. Next, Data Cleaning and Validation ensures quality through automated checks, validation pipelines, and real-time feedback mechanisms. Data Integration and Harmonization consolidate multi-source data using FAIR principles and tools like APIs and data lakes, creating rich, multi-modal datasets. Data Sharing and Reuse promotes sustainability through secure access controls, licensing frameworks, and versioned repositories. Finally, Continuous Monitoring and Feedback safeguards long-term performance by tracking data drift, enabling retraining, and maintaining live performance dashboards. Together, these pillars create a robust, adaptive system that supports trustworthy and scalable AI in healthcare diagnostics.

Figure 2: Data framework