Clinical Report: Best Practices for Safe Use of AI Models in Radiology

Overview

This review outlines best practices for integrating AI models into radiology, emphasizing regulatory frameworks, data privacy, and bias mitigation.

Background

The integration of artificial intelligence (AI) into radiology is rapidly evolving, necessitating a thorough understanding of its implications for clinical practice. As AI models, particularly large language models (LLMs), become more prevalent, ensuring their safe and effective use is critical to maintaining diagnostic accuracy and patient safety. This review addresses the regulatory, privacy, and bias challenges associated with AI in radiology.

Data Highlights

No numerical data provided in the source material.

Key Findings

• Regulatory standards based solely on accuracy are insufficient; additional metrics are needed.
• Data privacy concerns arise from proprietary LLMs transmitting data to external servers.
• Bias in AI models can reproduce inaccurate medical tropes and demographic disparities.
• Mitigation strategies include local deployment of models and demographic stress testing.

Clinical Implications

Healthcare professionals should be aware of the potential risks associated with AI models, including data privacy issues and biases in outputs.

Conclusion

The review highlights the importance of addressing regulatory, privacy, and bias challenges.

Related Resources & Content

1. Paul H. Yi, MD, et al., Radiology, 2023 -- Best Practices for Safe Use of AI Models in Radiology
2. European Radiology — OpenRad: a curated repository of open-access AI models for radiology
3. European Radiology — A Comprehensive Guide to the Role of Artificial Intelligence in Thoracic Imaging: Insights from the European Society of Thoracic Imaging (ESTI)
4. European Radiology — Embracing Artificial Intelligence in Radiology: Balancing Its Potential Benefits with Current Limitations in Clinical Practice
5. npj Digital Medicine — Unique Visual Preferences Influence Medical Imaging Diagnoses in Humans and AI
6. Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations.
7. OpenRad: a curated repository of open-access AI models for radiology
8. A Comprehensive Guide to the Role of Artificial Intelligence in Thoracic Imaging: Insights from the European Society of Thoracic Imaging (ESTI)
9. Embracing Artificial Intelligence in Radiology: Balancing Its Potential Benefits with Current Limitations in Clinical Practice
10. AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial | Nature Medicine
11. Guidelines for Reporting Studies on Large Language Models in Radiology: An International Delphi Expert Survey | Radiology