Mayo Clinic sought to deploy AI that could improve diagnostic accuracy and clinical decision-making across its multi-campus system while meeting the extraordinary evidentiary standards that govern medical care. The institution's electronic health record system contained decades of clinical data — including 26 petabytes encompassing over 3 billion laboratory tests, 1.6 billion clinical notes, and 6 billion medical images — but inconsistent documentation practices, free-text clinical notes, and evolving diagnostic coding standards across its Rochester, Phoenix, and Jacksonville campuses made data harmonisation laborious.
Clinician scepticism toward algorithmic recommendations, rooted in legitimate concerns about patient safety and liability, required evidence of clinical validity that far exceeded typical enterprise AI thresholds. Regulatory compliance with FDA software-as-medical-device frameworks added layers of validation, documentation, and post-market surveillance, while HIPAA and state-level health-information-exchange statutes imposed granular access-control requirements that complicated federated model-training architectures.
Mayo Clinic established a rigorous AI clinical-validation programme subjecting every diagnostic model to prospective clinical trials designed with pharmaceutical-grade study rigour, generating peer-reviewed evidence of safety and efficacy before deployment. Natural-language-processing pipelines extract structured clinical features from decades of free-text physician notes, pathology reports, and radiology interpretations, creating enriched patient representations that enhance model accuracy beyond what structured EHR fields alone permit.
In partnership with Google Cloud under a 10-year strategic collaboration, Mayo developed federated learning infrastructure that trains diagnostic models across campuses without centralising protected health information. A clinician-in-the-loop deployment model presents AI-generated insights as decision-support overlays within existing clinical workflows, preserving physician autonomy while enriching their information environment with over 200 AI projects in various stages of maturity.
Continuous post-deployment monitoring tracks model performance across demographic subgroups, triggering automatic alerts when prediction calibration degrades. Mayo also launched Mayo Clinic Digital Pathology with NVIDIA and Aignostics, training its Atlas pathology foundation model on more than 1.2 million histopathology whole-slide images. In 2025, the institution integrated 22 Mayo Clinic Platform-driven solutions into clinical practice, enhancing AI-enabled care and streamlining workflows.
This is an industry case study based on publicly available information. Mayo Clinic is not a Pertama Partners client.
We help enterprises across Southeast Asia design and deliver AI transformation programs. Let’s talk about what’s possible for your team.
