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Ethical Standards I

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From Data Stewardship to Model Stewardship: Extending Governance Frameworks for AI Era Health Data Use.

Leon Rozenblit^1,2, Steven Labkoff^2,3, Charles Safran^2,4

¹Q.E.D. Institute, New Haven, CT, United States.

Journal of Medical Internet Research

|June 5, 2026

Summary

This summary is machine-generated.

Existing data governance is inadequate for artificial intelligence (AI) model training. The study proposes extending data stewardship to model stewardship to address ethical challenges in AI development using electronic health records.

Keywords:

AI governance clinical tropism data stewardship decontextualization electronic health records health data ethics model stewardship secondary use

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Area of Science:

Health Informatics
Artificial Intelligence Ethics
Clinical Data Governance

Background:

Electronic health record (EHR) data is increasingly used for artificial intelligence (AI) development.
Current governance frameworks for secondary data use are insufficient for AI model training.
AI model training generates persistent artifacts encoding clinical patterns.

Purpose of the Study:

To highlight the ethical challenges at the intersection of EHR data and AI development.
To propose an extended governance framework addressing the limitations of existing models.
To introduce the concept of model stewardship for AI artifacts.

Main Methods:

Review of existing data stewardship frameworks.
Analysis of the unique challenges posed by AI model training.
Development of a proposal for extending governance to model stewardship.

Main Results:

Existing governance frameworks are categorically insufficient for AI model training.
AI model training creates unique deployable artifacts.
A new model stewardship concept is proposed.

Conclusions:

Governance must evolve beyond data stewardship to encompass AI model stewardship.
Extended frameworks are necessary to ethically manage AI development using clinical data.
Model stewardship is crucial for addressing persistent deployable artifacts in AI.