Artificial intelligence (AI) and machine learning (ML) are increasingly used in critical systems but introduce new risks through adversarial AI, which manipulates AI models to produce incorrect outputs. These attacks exploit the decision-making processes of AI, leading to serious implications such as failures in self-driving technology and biases in fraud detection. The various types of adversarial attacks—including prompt injection and data poisoning—highlight the complexity of safeguarding AI systems against such threats. Healthcare professionals must be aware of these risks as they adopt AI technologies, ensuring robust cybersecurity measures to protect sensitive data and maintain the integrity of AI-driven decisions.
