The AI Data Lifecycle Project is necessary to promote the use of standards that improve the trust and quality of interoperable data used in AI models. Standards are needed for the development and implementation of AI systems in healthcare to ensure that the data used to train and receive output from these systems are of consistently high quality, interoperable (uses data that involve standard terminologies such as (e.g. FHIR, SNOMED, CPT), transparent, and ethically sound, and used for the purpose intended ( e.g. "answers the question"). The rationale for needing standards regarding these attributes is expanded below: 1. Consistency: Standards ensure that data are collected, annotated, and processed in a consistent and standardized manner. This consistency is essential for training machine learning models, as it ensures that the data is of high quality and can be used to produce reliable and consistent results. 2. Interoperability: Standards enable interoperability between different systems and technologies, allowing different AI systems to share data and work together seamlessly. This is particularly important in healthcare, where different organizations and systems often need to share patient data to provide the best possible care. 3. Transparency: Standards promote transparency in the development and implementation of AI systems by ensuring that data collection and processing methods are well-defined and clearly documented. This transparency is essential for building trust in AI systems, particularly in healthcare where the stakes are high and the consequences of errors can be severe. 4. Ethical considerations: Standards can help ensure that AI systems are developed and implemented in an ethical and responsible manner. For example, standards can help prevent bias in training data sets by ensuring that data is collected from diverse populations and that bias is actively mitigated during the development process. Within the construct of these rationales, the Life Cycle of Data used in AI for healthcare involves several critical stages, each of which requires specific standards.

Patients EHR, PHR Clinical end users Health Care IT Developers Health Care Organization Information Officers (e.g. CMIOs, CNIOs, CHIOs) Equipment Vendors Emergency Services Quality Reporting Agencies Clinical Decision Support Systems Health Information Exchanges Local and State Departments of Health Healthcare Institutions (hospitals, long term care, home care, mental health) Courts and Legal Representatives Clinical Research Patient Advocacy Communities Healthcare Payers and Insurers