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Development of secure infrastructure for advancing generative artificial intelligence research in healthcare at an academic medical center

  • 0Department of Medicine, Stanford University, Stanford, CA 94305, United States.
Journal of the American Medical Informatics Association : Jamia +

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January 21, 2025

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January 21, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers developed a secure, HIPAA-compliant infrastructure for using generative AI (artificial intelligence) like large language models (LLMs) in healthcare research, protecting patient data while enabling innovation.

Area Of Science

  • Healthcare Informatics
  • Artificial Intelligence in Medicine
  • Health Data Security

Background

  • Generative AI, particularly large language models (LLMs), offers significant potential for enhancing healthcare delivery and operational efficiency.
  • Regulatory hurdles concerning data security and patient privacy complicate the adoption of LLMs in healthcare settings.
  • Ensuring Health Insurance Portability and Accountability Act (HIPAA) compliance is paramount for the ethical and legal use of AI in healthcare.

Purpose Of The Study

  • To develop and assess a secure infrastructure enabling researchers to utilize LLMs safely within healthcare environments.
  • To ensure HIPAA compliance and foster equitable AI development and deployment in medical research.
  • To address the challenges of data security and patient privacy when implementing advanced AI tools in clinical settings.

Main Methods

  • Implementation of a private Azure OpenAI Studio with secure, API-enabled endpoints for research access.
  • Exploration of two distinct use cases: fall detection from electronic health records (EHR) and bias assessment in mental health predictions.
  • Utilization of fairness-aware prompts to evaluate and mitigate bias in AI-driven mental health assessments.

Main Results

  • The developed framework successfully provided secure, HIPAA-compliant API access to LLMs, facilitating safe handling of sensitive patient data.
  • Both use cases demonstrated the infrastructure's capability to safeguard protected health information (PHI) while supporting innovative AI applications.
  • The secure environment enabled researchers to conduct studies involving sensitive health data without compromising privacy or compliance.

Conclusions

  • The centralized platform offers a scalable, secure, and HIPAA-compliant solution for healthcare organizations seeking to integrate LLMs into their research endeavors.
  • This infrastructure empowers healthcare institutions to leverage the power of LLMs responsibly, balancing innovation with stringent data protection requirements.
  • The successful implementation paves the way for broader adoption of secure AI technologies in clinical research, enhancing both patient care and operational efficiency.

Keywords:

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