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Radiologists must adopt best practices for generative artificial intelligence (AI) and large language models (LLMs) to ensure patient safety. Addressing regulatory, data privacy, and bias issues proactively prevents AI pitfalls in radiology workflows.

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Area of Science:

  • Radiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Generative artificial intelligence (AI) and large language models (LLMs) are increasingly integrated into radiology.
  • Potential risks and pitfalls associated with AI deployment in clinical settings are often identified post-release.
  • There is a critical need for proactive measures to ensure the safe and effective use of AI in radiology.

Purpose of the Study:

  • To summarize best practices for the safe integration of LLMs and generative AI in radiology.
  • To highlight key areas prone to pitfalls: regulatory issues, data privacy, and bias.
  • To provide actionable guidelines for radiologists, departments, and vendors.

Main Methods:

  • Review of best available evidence.
  • Incorporation of experiences from field leaders.
  • Focus on preventive measures for AI deployment.

Main Results:

  • Identified three critical areas for safe AI use: regulatory compliance, data privacy, and bias mitigation.
  • Emphasized the importance of examining potential failure modes and ensuring vendor transparency.
  • Developed a framework for preventing problems associated with generative AI in radiology.

Conclusions:

  • Proactive identification and mitigation of risks are essential for safe AI implementation in radiology.
  • Radiologists, departments, and vendors must collaborate to establish robust safety protocols.
  • Adherence to best practices will minimize patient risk and optimize AI benefits in clinical workflows.