Cybersecurity for AI in Digital Health

Are you building AI- and ML-enabled digital health solutions like SaMD, precision medicine, mobile apps or telemedicine?

Have you considered security risks of adaptive AI and ML technologies in healthcare?

Did you know that your medical ML systems could be attacked, compromised, and manipulated?

Are you aware of the newer regulatory requirements from FDA and IMDRF for SaMD in the age of artificial intelligence?

In this webinar, you will learn about:

1. Emerging cybersecurity threats in AI based SaMD and their impacts on patients’ safety and healthcare accessibility

2. Growing need for AI Security

3. Regulations for AI cybersecurity in healthcare

4. How AIShield and CyberActa can help AI-/ML-based healthcare businesses

You will be introduced to Software as a Medical Device (SaMD), cybersecurity requirements, patient centricity and patients’ privacy issues, regulatory issues, digital health partnerships, and other topics.

Abstract

Artificial intelligence (AI)- and machine learning (ML)-based technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day. One of the greatest benefits of AI/ML in software resides in its ability to learn from real-world use and experience, and its capability to improve its performance. However, the ability for AI/ML software to learn from real-world feedback (training) and improve its performance (adaptation) makes these technologies uniquely susceptible to advanced techniques to subvert otherwise-reliable machine-learning systems—so called adversarial attacks.

These challenges are compounded in the medical context with a potential for medical ML systems to compromise patient safety and healthcare accessibility. With advanced adversarial techniques, medical ML systems are prone to manipulation for financial gains by players with competing financial interests in US’ healthcare insurance system. This unique risk has compelled regulatory bodies such as FDA, IMDRF to put special emphasis on improving the robustness and resilience of AI/ML based SaMD through regulations to ensure patient safety, security, privacy, and effectiveness of healthcare systems.

AI security brings in promising interventions and principled approach to protect against adversarial attacks in the health care context - one which builds the groundwork for resilience without crippling rollout and sets ethical and legal standards for line-crossing behavior.