Key Focus Areas
- Data Security: AI introduces new security risks, such as model leakage and adversarial AI attacks, which endanger sensitive patient data. Healthcare organisations need advanced cybersecurity measures tailored to AI.
- Algorithmic Bias
- Biased training data can lead to discriminatory outcomes. Documenting bias-reduction strategies is critical for providers and users.
- Explainability:
- High-risk AI systems require explainability to meet regulations and build trust. Employing Explainable AI (XAI) ensures decision-making transparency for clinicians and patients.
- Regulatory Compliance:
- AI regulations are constantly evolving. For instance, the EU recently updated its AI Act with new rules for generative AI. Compliance teams must adapt, ensuring their systems align with these shifting legal requirements.
Leveraging Scalable Solutions
Managing AI risks manually is impractical for organisations with complex systems. Scalable solutions, such as automated tools for quality assurance and compliance monitoring, streamline workflows and provide continuous oversight. These tools enhance efficiency and mitigate risks proactively, ensuring AI systems remain trustworthy.
