The narrative review outlines best practices for responsibly deploying AI in healthcare as part of the FAIR-AI framework, focusing on validation, transparency, equity, and usefulness. Key practices include adhering to guidelines like TRIPOD for model reporting, thoroughly assessing performance metrics (including AUC, calibration, and F-scores), and conducting real-world validation studies. Transparency is vital to prevent biases and maintain trust, requiring clear communication about AI’s use and limitations. Stakeholder engagement is critical, with insights from health leaders, AI developers, and patients informing the framework’s design. FAIR-AI comprises foundational requirements, risk evaluation processes, and guidelines for safe implementation, categorizing solutions as low, moderate, or high risk based on potential impacts and compliance with ethical standards. Continuous monitoring and user education are mandated, especially for high-risk AI tools, ensuring their alignment with intended use and regulatory compliance. This comprehensive framework supports innovation while prioritizing patient safety and ethical considerations.
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