Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Area of Science:

• Artificial Intelligence in Medicine
• Healthcare Technology
• Digital Health

Background:

• Advancements in artificial intelligence (AI) are revolutionizing healthcare.
• Generative AI models, including generative adversarial networks and large language models, show significant promise for medical applications.

Purpose of the Study:

• To explore generative AI applications in healthcare, such as diagnostics, drug discovery, and patient care.
• To identify security and privacy threats associated with generative AI throughout its lifecycle.
• To propose strategies for mitigating these risks and ensuring safe adoption.

Main Methods:

• Literature review and analysis of current generative AI applications in healthcare.
• Examination of security and privacy vulnerabilities across data collection, model development, and implementation phases.
• Synthesis of findings to inform best practices and future development.

Main Results:

• Generative AI presents opportunities in medical diagnostics, drug discovery, virtual assistants, research, and clinical decision support.
• Key security and privacy threats include data breaches, unauthorized access, and ethical concerns.
• Mitigation strategies are essential for responsible integration into healthcare infrastructure.

Conclusions:

• Addressing security and privacy is paramount for the safe and effective use of generative AI in healthcare.
• Understanding both benefits and risks is crucial for healthcare organizations.
• This study contributes to AI ethics, security, and data privacy discussions in healthcare.