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Updated: Jan 16, 2026

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Toward Patient-Centered AI Fact Labels: Leveraging Extrinsic Trust Cues.

Dong Whi Yoo1, Austin M Stroud2, Xuan Zhu2

  • 1Indiana University Indianapolis, Indianapolis, Indiana, USA.

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|September 29, 2025
PubMed
Summary
This summary is machine-generated.

Patients need clear information and trust signals for healthcare AI adoption. This study explores patient perspectives on AI documentation in cardiology, highlighting needs for detailed explanations and regulatory trust cues.

Keywords:
AI DocumentationAI Facts LabelPatientTrust

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Last Updated: Jan 16, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Area of Science:

  • Healthcare technology
  • Human-computer interaction
  • Medical artificial intelligence

Background:

  • Artificial intelligence (AI) in healthcare offers significant potential but faces challenges in patient understanding, trust, and adoption.
  • Existing AI documentation frameworks (e.g., model cards) primarily focus on technical aspects, with limited exploration of patient perspectives in healthcare settings.

Purpose of the Study:

  • To investigate patient perspectives on AI documentation within cardiology, a field with increasing use of high-stakes AI tools.
  • To identify patient needs and preferences for understanding and trusting AI technologies in their healthcare.
  • To inform the design of patient-centered AI documentation.

Main Methods:

  • Development of AI documentation prototypes based on existing frameworks.
  • Gathering qualitative feedback from 18 participants on these prototypes within a cardiology context.
  • Analysis of patient feedback to identify key themes related to understanding, trust, and adoption.

Main Results:

  • Patients require more comprehensive information regarding healthcare AI technologies.
  • Extrinsic trust cues, such as regulatory status, are crucial for building patient confidence.
  • Integration of AI documentation into existing clinical workflows is essential for adoption.

Conclusions:

  • Patient-centered design is vital for effective healthcare AI documentation.
  • Leveraging extrinsic trust cues can significantly enhance the design and acceptance of AI documentation.
  • This research amplifies the patient voice in HCI, providing actionable insights for healthcare AI development.