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Area of Science:

  • Healthcare technology
  • Artificial Intelligence in Medicine
  • Digital Health

Background:

  • Large Language Models (LLMs) like GPT-4, Gemini, Claude, and Llama are transforming healthcare delivery.
  • AI applications in healthcare span patient care, medical research, and administrative efficiency.
  • Current AI adoption faces significant security and ethical hurdles, including data privacy concerns.

Purpose of the Study:

  • To propose a secure and ethical framework for deploying AI-powered healthcare chatbots.
  • To address the challenges of data privacy, security, and user trust in AI healthcare applications.
  • To enhance the resilience and reliability of AI systems in sensitive medical contexts.

Main Methods:

  • Integration of advanced privacy-preserving techniques into AI chatbot pipelines.
  • Implementation of continuous security assessments for AI healthcare systems.
  • Development of ethical guidelines tailored for generative AI in medical settings.

Main Results:

  • The proposed pipeline enhances data privacy and security for AI healthcare chatbots.
  • Continuous security assessments improve the resilience of AI systems against potential threats.
  • The framework aims to foster greater user trust in AI-driven healthcare solutions.

Conclusions:

  • A secure and ethical pipeline is crucial for the responsible deployment of AI in healthcare.
  • Addressing security and ethical considerations is paramount for realizing the full potential of AI chatbots.
  • The proposed approach provides a robust model for trustworthy AI integration in patient care and medical research.