MediGuard: Protecting Sensitive Healthcare Data with Privacy-Preserving Language Models.

Area of Science:

• Artificial Intelligence
• Digital Health
• Medical Informatics

Background:

• Large language models (LLMs) offer potential for improved healthcare access but raise significant privacy concerns due to sensitive data handling.
• Current LLM applications in healthcare risk unauthorized data exposure and regulatory non-compliance.
• Widespread adoption of AI in medicine is hindered by the need for robust patient data protection.

Purpose of the Study:

• To introduce MediGuard, a novel privacy-preserving framework for LLM-based medical consultations.
• To address the critical challenge of protecting sensitive health information within AI-driven healthcare systems.
• To enable secure and reliable medical advice delivery via LLMs without compromising user privacy.

Main Methods:

• MediGuard utilizes adaptive information obfuscation to protect sensitive healthcare data.
• The framework incorporates secure access protocols and auditing mechanisms for data governance.
• It processes only non-sensitive information while maintaining semantic integrity for accurate medical inference.

Main Results:

• MediGuard demonstrated superior privacy protection compared to existing methods in extensive testing.
• The framework achieved high clinical accuracy in medical question-answering tasks, even under strict privacy constraints.
• Performance evaluations across multiple datasets confirmed MediGuard's effectiveness in balancing privacy and utility.

Conclusions:

• MediGuard offers a safe, trustworthy, and clinically reliable solution for AI-powered medical consultations.
• The framework establishes a new benchmark for privacy-aware artificial intelligence in the healthcare sector.
• Implementation of MediGuard can facilitate the secure integration of LLMs into digital healthcare ecosystems.