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Related Experiment Videos

Explainable Artificial Intelligence (AI) for Medical Imaging: A Framework for Bridging the AI Trust Gap.

Cody H Savage1, Jeremias Sulam2,3, Chien-Ming Huang2,4

  • 1Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD.

AJR. American Journal of Roentgenology
|May 13, 2026
PubMed
Summary

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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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Explainable AI (XAI) in healthcare needs to be robust, user-adapted, and clinically aligned to build trust. Current methods are insufficient, requiring a new framework for trustworthy AI adoption in medical imaging.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Explainable AI (XAI)

Background:

  • Artificial intelligence (AI) adoption in healthcare is rising, yet faces significant trust barriers among clinicians and patients.
  • Existing Explainable AI (XAI) methods often fail to provide transparent, interpretable, and clinically actionable insights, hindering effective integration.
  • Trust is a critical factor for the successful implementation of AI tools in clinical settings, particularly in medical imaging.

Purpose of the Study:

  • To propose guiding principles for developing clinically useful and trustworthy Explainable AI (XAI) in medical imaging.
  • To introduce a conceptual framework for XAI design and deployment that addresses current limitations.
  • To foster shared responsibilities among AI developers, vendors, and healthcare institutions for successful AI adoption.

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Main Methods:

  • The study presents a perspective on current XAI limitations and proposes a conceptual framework.
  • Key principles for effective XAI include technical robustness, end-user adaptation, and clinical task alignment.
  • The framework emphasizes a shift from one-size-fits-all solutions to task- and user-centered design.

Main Results:

  • Current XAI methods are inadequate for clinical utility and trust-building in healthcare.
  • Three core principles—robustness, user adaptation, and clinical alignment—are essential for effective XAI.
  • A conceptual framework is introduced to guide the development and deployment of trustworthy XAI.

Conclusions:

  • Explainable AI (XAI) must be technically robust, adapted to end-users, and aligned with clinical tasks to be effective in medical imaging.
  • A new conceptual framework is proposed to guide the design and deployment of XAI, emphasizing user-centered approaches.
  • Implementing these principles and framework can enhance trust and facilitate the adoption of AI in healthcare.