Introduction

The integration of Artificial Intelligence (AI) in medical imaging offers transformative potential, enhancing diagnostic accuracy, efficiency, and patient care. However, ensuring AI systems are trustworthy requires adherence to ethical principles, transparency, and robust validation processes. These guidelines aim to establish best practices for the safe and effective implementation of AI in clinical imaging.

Main Content

• Reliability & Generalisability
  AI models must be trained on diverse datasets to ensure consistent performance across different populations and clinical settings. Rigorous validation and continuous monitoring are essential to mitigate biases and maintain accuracy.
• Explainability & Transparency
  AI algorithms should be interpretable, allowing clinicians to understand and trust AI-driven decisions. Clear documentation of model development, training data, and decision-making processes fosters accountability.
• Ethical & Regulatory Compliance
  AI in medical imaging must align with established ethical standards, including patient privacy, data security, and regulatory approvals. Ensuring fairness and minimizing unintended biases are key to responsible AI deployment.

Conclusion

Developing trustworthy AI in medical imaging requires a multidisciplinary approach, combining technological advancements with ethical, regulatory, and clinical considerations. By following these guidelines, we can ensure AI enhances medical imaging while maintaining safety, reliability, and trust.