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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Patient Engagement

Background:

  • Patient engagement in radiology artificial intelligence (AI) occurs in data sharing for AI development and AI use in patient care.
  • While patients support data sharing for research and the common good, privacy risks and lack of trust are significant concerns.
  • Patients generally accept AI as a radiologist's assistant but distrust unsupervised AI, citing concerns about liability, loss of human connection, and algorithmic bias.

Purpose of the Study:

  • To explore patient perspectives on engagement with artificial intelligence in radiology.
  • To identify barriers and facilitators to patient trust in AI for data sharing and clinical use.
  • To provide recommendations for radiologists to foster patient trust in AI.

Main Methods:

  • The abstract does not specify the methods used.
  • The findings are based on patient perspectives regarding data sharing and AI in medical care.

Main Results:

  • Patients support sharing deidentified data but worry about privacy and re-identification.
  • Trust is a barrier to data sharing, and patients desire involvement in the process.
  • Patients are concerned about unsupervised AI, liability, loss of empathy, and algorithmic bias.
  • Transparency, explainability, security, and privacy are crucial for building AI trust.

Conclusions:

  • Radiologists can enhance patient trust in AI through transparency, explainability, and security measures.
  • Implementing data-sharing agreements and AI disclosure guidelines empowers patients.
  • Proactive communication and maintaining strong patient relationships are key as AI adoption grows in radiology.