Comprehensive Summary
This article introduces FAIR-AI, which stands for Framework for Appropriate Implementation and Review of Artificial Intelligence, to be used in healthcare settings. FAIR-AI is a structured guide on how to safely and effectively implement AI into various healthcare settings. The authors utilized literature reviews, interviews with patients, interviews with stakeholders, design workshops, and more to identify some of the gaps present in AI implementation that exist currently. They found some of the gaps to be a lack of standardized evaluation, inconsistent oversight, and poor monitoring post-implementation. FAIR-AI now addresses those issues by outlining a stepwise process that has a pre-implementation risk categorization (low, medium, or high), a structures documentation system ("Safe AI Plan"), and a constant performance auditing. The goal of this framework is the put an emphasis on transparency, data quality, safety, and human oversight. By creating clear guides and processes, FAIR-AI provides hospitals and over healthcare settings a practical tool that can asses whether AI used is safe, effective, and meeting correct expectations before widespread implementation.
Outcomes and Implications
FAIR-AI provides the opportunity to redefine how clinical, hospital administration, and the healthcare system as a whole approaches AI integration. By creating a transparent and reproducible framework that can evaluate both the technical and ethical concerns, it allows AI tools to correctly be tested and surveyed before being implemented and creating any issues. This prevents patient outcomes from being compromised while also allowing healthcare to remain at the forefront of innovation by still being able to introduce AI into different models into the field. Adopting this model can help standardize the review process, reduce liability and increase trust in these AI models to ensure that the focus is always enhancing patient outcomes. For developers, FAIR-AI offers a clear plan for meeting the requirements of institutions and promoting collaboration between technical and clinical team members. Moreover, the widespread use of this stepwise process could be used to improve patient safety, hold AI processes accountable by ensuring their ethical compliance, and providing a path to ensure that medicine remains innovative without jeopardizing patients or clinicians.