Summary
Healthcare has continued to evolve from the paper-and-pen world to a digital environment. The opportunities for high-quality, safe and effective care have increased exponentially with this change. Integral to these opportunities is the harnessing of increasing computer power and the revolutionary impact of artificial intelligence (AI) and machine learning (ML). AI/ML could impact every aspect of healthcare, from diagnosis, treatment decisions, predictive analysis, and even administrative functions such as coding and billing.
The promise of AI/ML, however, comes at a price: artificial intelligence systems, especially those dependent on machine learning (ML), can be vulnerable to intentional attacks that involve evasion, data poisoning, model replication, and exploitation of traditional software flaws to deceive, manipulate, compromise, and render them ineffective. Yet too many organizations adopting AI/ML systems are unaware of their vulnerabilities. This potential outcome is the basis of this whitepaper.
Audience
This paper is intended for an audience of senior technical leaders within the CIO/CTO chain of command. It assumes fundamental knowledge about software programming and application engineering with an associated capability to translate technical concepts into practical business, operations, and clinical privacy and cybersecurity risk.
