AI Training Data Transparency

Scienza Health, Inc. is committed to transparency in how our clinical screening models are developed, trained, and validated. This disclosure is provided in accordance with California Assembly Bill 2013 (AB 2013), which requires providers of generative AI systems to disclose information about training data.

What We Build

Scienza Health develops digitalhumanOS™, a clinical screening platform, and GIA®, a Digital Human® that screens patients for 46 cognitive, behavioral, and neurological conditions through natural conversation. Our platform uses Voice AI, Computer Vision, and Speech Biomarker analysis to identify clinical risk factors.

Training Data Sources

Our clinical screening models are trained and validated using the following data categories:

• —De-identified clinical records — sourced from licensed data partnerships with electronic health record platforms covering post-acute and long-term care settings. All data is de-identified in compliance with HIPAA Safe Harbor and Expert Determination methods.
• —Peer-reviewed clinical research — published studies on speech biomarkers, cognitive assessment, and neurological screening from institutions including Beth Israel Deaconess Medical Center, the National Institutes of Health (NIH), and the Massachusetts Institute of Technology (MIT). See our clinical research page for the full citation list.
• —Validated clinical assessment instruments — established screening tools including MoCA, MMSE, BIMS, PHQ-9, GAD-7, and AIMS, used as reference standards for model calibration and accuracy benchmarking.
• —Proprietary clinical interaction data — data generated through clinical deployments with informed patient consent, used to refine screening accuracy and expand condition coverage.

Data Scale

Data Governance

All training data is subject to the following governance controls:

• —HIPAA compliance — all patient data is de-identified before use in model training. No protected health information (PHI) is used in training datasets.
• —Bias monitoring — models are evaluated across demographic groups including age, sex, race, ethnicity, and primary language to identify and mitigate performance disparities.
• —Human-in-the-loop — all screening results require clinician review before any clinical action is taken. Our models assist clinicians; they do not replace clinical judgment.
• —510(k) registration — our platform is registered with the FDA, subject to regulatory requirements for clinical software.
• —AES-256 encryption — all data at rest and in transit is encrypted. Access is controlled through role-based permissions with audit logging.

Model Accuracy and Validation

Our screening models have been validated against peer-reviewed clinical benchmarks. Published accuracy figures include:

What Our Models Do Not Do

• —They do not diagnose. They screen and flag risk for clinician review.
• —They do not make treatment decisions.
• —They do not operate autonomously without clinician oversight.
• —They do not use personally identifiable patient information in training.

Contact

For questions about our training data practices, model governance, or this disclosure, contact us at support@scienzahealth.com with the subject line “AI Transparency Inquiry.”

This disclosure was last updated in March 2026. Scienza Health, Inc. · Newport Beach, California.