Unlocking medical AI: a scalable, privacy-preserving annotation platform for clinical and physiological data

The National Institute of Environmental Health Sciences awarded Mountain Biometrics, Inc. $306,721 on August 1, 2026, under the Medical Library Assistance program (CFDA 93.879) to develop a privacy-preserving annotation platform for clinical and physiological data.

The recipient will build an advanced platform to address gaps in annotated datasets for medical artificial intelligence, specifically targeting complex clinical time-series data from wearables, bedside monitors, and electronic health records. Phase 1 focuses on technical feasibility: enhancing existing tools to achieve sub-second latency for review of irregularly sampled multimodal data, designing privacy frameworks for HIPAA-compliant handling of sensitive health information, and validating usability through engagement with clinicians, researchers, and industry experts. Phase 2 will extend validation to real-world healthcare settings by building scalable data architectures for concurrent users, implementing standardized EHR integration using FHIR protocols, and deploying the product for diverse healthcare applications.

Performance is located in Park City, Utah, with completion targeted for January 31, 2027.

Abstract

This project addresses a critical challenge in medical artificial intelligence (AI): the lack of high-quality, annotated physiological datasets necessary for developing robust and generalizable models. Current methods for annotating clinical time-series data, such as signals from wearables, bedside monitors, and electronic health records, are insufficient due to irregular sampling rates, frequent missing data, and multi-modal complexity. Our goal is to develop and rigorously evaluate an advanced annotation platform that enables efficient and secure labeling of complex clinical datasets while ensuring compliance with hipaa and interoperability standards. By addressing key gaps in data quality, scalability, and reproducibility, this platform will accelerate the development of AI-driven healthcare solutions. In phase 1, we will focus on demonstrating technical feasibility by solving key challenges related to multi-signal data visualization, privacy-preserving annotation workflows, and user-centric design. Specifically, we will enhance existing tools to enable sub-second latency for efficient review of irregularly sampled, multimodal data. We will also design and implement robust privacy frameworks to ensure secure handling of sensitive health information. Finally, we will engage clinicians, researchers, and industry experts to identify critical platform features and vali- date the usability of our solution. This phase will lay the foundation for a scalable, clinically validated annotation platform capable of supporting diverse healthcare applications. In phase 2, we will extend these efforts to validate the platform's scalability and utility in real-world healthcare settings. This will involve building scalable data architectures to support concurrent users, implementing standard- ized EHR integration using fhir protocols, and deploying production-grade annotation workflows with advanced compliance controls. Pilot deployments at clinical sites will be conducted to evaluate platform performance, US- ability, and its impact on AI model development. The outcomes will provide robust evidence for the platform's effectiveness in generating high-quality datasets critical for AI innovation. By creating an advanced framework for medical data annotation, this project will contribute to improving the reproducibility and quality of AI models used in healthcare. The platform's innovative ability to generate reliable datasets will support breakthroughs in predictive analytics, real-time monitoring, and personalized medicine, ultimately driving better patient outcomes and more efficient healthcare delivery.