Robust and Secure AI for Healthcare Applications: Challenges and Opportunities

EasyChair Preprint 13462

Abstract

Robust and secure artificial intelligence (AI) has the potential to transform healthcare applications, enabling improved diagnosis, personalized treatment, and streamlined administrative processes. However, numerous challenges must be addressed to ensure the reliability and security of AI in healthcare. This abstract explores the challenges and opportunities associated with robust and secure AI for healthcare applications.

The primary challenges include data privacy and security concerns, ethical considerations, data quality and reliability issues, adversarial attacks, and regulatory compliance. Data privacy and security are critical to protect patient confidentiality and ensure secure data storage and transmission. Ethical considerations encompass addressing bias and fairness, promoting accountability and transparency, and obtaining informed consent. Data quality and reliability challenges arise from noisy and incomplete data, data integration and interoperability issues, and data bias and representativeness concerns. Adversarial attacks, such as data poisoning and model manipulation, pose threats to the integrity and performance of AI systems. Regulatory compliance, including adherence to regulations like HIPAA and GDPR, and addressing liability and intellectual property rights, are additional challenges.

Keyphrases: AI systems, Enhanced patient care, Enhanced patient monitoring, Predictive Analytics, Remote Patient Monitoring, early detection

@booklet{EasyChair:13462,
  author    = {Edwin Frank and Godwin Olaoye},
  title     = {Robust and Secure AI for Healthcare Applications: Challenges and Opportunities},
  howpublished = {EasyChair Preprint 13462},
  year      = {EasyChair, 2024}}