Project Summary for AI Data Lifecycle (Jira PSS-2168)
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Project Details
Number | 1814 View Ballot Items List (with NIBs) |
---|---|
Name | AI Data Lifecycle (Jira PSS-2168) |
Sponsor(s) | Electronic Health Records Work Group |
Co-Sponsor(s) | |
Steering Division | N/A |
Description | Artificial Intelligence (AI), to include Machine Learning (ML), depends on data quality. This project considers how to capture, render and share the attributes of provenance, accountability [e.g. audit trails], trustworthiness, context, structure, patterns, annotation and annotation history at each step in the life cycle of the data. The goals are to provide implementation guidance for AI/ML projects to leverage discoverable patterns and annotations provided by standards-based interoperable datasets, provide a roadmap for AI/ML experts to take advantage of interoperability standards to combine data from multiple, disparate data sources; and, in turn, articulate the return on investment of using interoperable, HL7-conformant data sets to create AI/ML solutions that are trusted by clinicians. |
Project Facilitator | Mark Janczewski |
Status | Active Project (Resources assigned to pjt) |
SD Approval Date | Awaiting Approval |
TSC Approval Date | Jul 11, 2023 |
Type | Ballot - STU to Normative |
Objectives / Deliverables | White Paper - Comment Only Ballot - Target: 2023 Sept Ballot Cycle |
Next Milestone Date | 2024 January WGM |
Project End Date | 2024 January WGM |
Project Intent | |
Project Need | 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. |
Implementers | Entities interested in using our guidance to leverage interoperable data. |
Security Risks | No |
External Drivers | |
Common Names / Keywords/ Aliases | AI Data Life Cycle |
Lineage | |
Dependancies | |
Project Document Repository |
https://confluence.hl7.org/pages/viewpage.action?pageId=154995452 PSS: https://jira.hl7.org/browse/PSS-2168?src=confmacro |
Backwards Compatibility | N/A |
External Vocabularies | No |
Products | |
Joint Copyright? | No |
External Pjt Collaborators | |
Realm | Realm Specific - Enter "U.S." or name of HL7 Affiliate below |
HL7 Affiliate | |
Stakeholders | |
Vendors | |
Providers | |
Ballot Cycle Info | |
Misc Notes | Other Stakeholders: Patients, Payer/Third Party Administrator, Pharmaceutical/Biotech, Providers, Regulatory Agency, Standards Development Organizations (SDOs), Vendor/Manufacturer |
U.S. Govt Interest? | |
USRSC Approval | Pending |
FMG Approval | Not Needed |
ARB Approval | Not Needed |
Start Date | May 23, 2023 |
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