Patient Privacy in Medical AI Systems: PrivMeSA Solution

Artificial intelligence technology has become a critical problem in the field of medicine, while maintaining patient information. Especially in clinical applications that use large language models (LLM), the risks of data privacy even if local systems need to consult more powerful remote models. The researchers developed a privacy-oriented multi-factor system called PrivMeSA for this problem.

The basic features of the system can be summarized as follows:

• Local LLM agents give advice decisions and can access patient information from description to models
• Indirect privacy risks such as semi-representative data (quasi-identifiers) are prevented from conflicting advisors and repeated visits
• The system becomes more effective in time by developing itself
• Even if open identification information is deleted, it provides protection against the risk of redefined patients with the merger of multiple data parts

It is extremely difficult to establish balance between security and functionality in medical AI applications. PrivMeSA protects both sensitive health data by solving this difficulty and increases the quality of clinical decision support. The system offers a new model for safe operation of LLM-based agents to be distributed in hospital environments.

This research represents an important step in the safe development and implementation of medical AI.