AI is a cornerstone of healthcare innovation, transforming everything from patient interactions to diagnostic processes. Its adoption has more than doubled since 2017 and the global AI market is expected to exceed $1 trillion by 2028. However, as AI is integrated into more and more medical practices, the importance of transparency and trust grows. Dr. Brian Anderson, CEO of the Coalition for Health in AI (CHAI), said “The successful implementation and impact of AI technology in healthcare hinges on our commitment to responsible development and deployment.”

For digital health solution developers, establishing trust in AI requires adopting robust guidelines and frameworks that prioritize accountability and transparency. CHAI has taken steps to meet these needs by releasing the “Blueprint for Trustworthy AI Implementation Guidance and Assurance for Healthcare,” which offers recommendations to increase trustworthiness, ensure high-quality care, and promote ethical AI use in solutions.

CHAI is continuing to build trust by setting up labs to test the safety of healthcare AI products and creating a testing and evaluation framework. This framework measures how well AI performs and shares the results with patients and providers to keep the process transparent. Using this collaborative approach, CHAI ensures that AI development meets the needs of different communities, reducing bias and leading to safer, more reliable healthcare innovations. As AI in healthcare evolves, safety checks like these are crucial for developers when building reliable and trustworthy solutions.

Trust in AI comes down to five key pillars: data quality, transparency, partner reliability, quality control, and human oversight. High-quality data, which reduces bias and improves model accuracy, is fundamental to building reliable AI. Solution developers should prioritize transparency by clearly documenting how AI models reach their conclusions, enabling users to understand and trust the results. Partner reliability is equally important—developers should work with experienced partners with access to diverse data sets to ensure models are robust and accurate. Regular updates and third-party audits are essential to maintain quality while human oversight ensures AI models remain tools that support, rather than replace, clinical judgment.

It’s clear that building trust in AI will continue to take time and effort. Mayo Clinic Platform is committed to helping solution developers develop and integrate trustworthy AI by establishing rigorous standards and best practices. Through continuous monitoring, model validation, and a focus on transparency, we enable developers to deliver solutions that healthcare providers can trust.

To learn more about how Mayo Clinic Platform supports companies’ development of trustworthy AI systems, reach out to us today.