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High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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Establishing and implementing a responsible artificial intelligence framework: a 1-year review.

Ada H Tsoi1, Gary Gartner1, Steven W Cotten2

  • 1ISD-EADS, UNC Health, Morrisville, NC 27560, United States.

Journal of the American Medical Informatics Association : JAMIA
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

Implementing a responsible artificial intelligence (RAI) framework in healthcare showed challenges with fairness and vendor transparency. The framework requires resources and cooperation to effectively assess AI tools for trustworthiness.

Keywords:
artificial intelligenceethicsgovernancehealthcareregulation

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Area of Science:

  • Healthcare AI
  • Responsible AI Implementation
  • Clinical Informatics

Background:

  • The increasing integration of artificial intelligence (AI) in healthcare necessitates robust frameworks for responsible implementation.
  • Ensuring fairness, transparency, accountability, and trustworthiness in healthcare AI is critical for patient safety and ethical practice.

Purpose of the Study:

  • To highlight the successes and challenges of implementing a novel responsible artificial intelligence (RAI) framework within a healthcare setting.
  • To emphasize the interdisciplinary healthcare expertise required for operationalizing RAI frameworks.

Main Methods:

  • Development of a 21-question RAI intake survey at UNC Health to evaluate AI solutions.
  • Alignment of survey questions with institutional goals for fairness, transparency, accountability, and trustworthiness.
  • Evaluation of AI solutions by a multidisciplinary team of clinical, analytical, and operational experts.

Main Results:

  • Twelve AI solution evaluations using the RAI framework were conducted.
  • Low fairness scores were identified in the assessed AI solutions.
  • 83% of AI solutions received conditional approval pending further review or mitigation.

Conclusions:

  • Key learnings include the critical need for representative training datasets and systematic evaluation of vendor models.
  • Challenges encountered were infrequent demographic-stratified analyses, limited vendor transparency, and reliance on volunteer engagement.
  • The RAI framework offers a roadmap for healthcare AI assessment but faces barriers such as resource constraints and vendor cooperation; future iterations should consider risk-based tiered evaluations and member engagement for scalability.