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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 18·5 min read
Generated Context versus Governed State: Functional Conditions for Accountable Longitudinal Clinical Reasoning
TL;DR
This paper critiques the use of large language models in clinical AI, emphasizing the need for a governed representation of patient states. It introduces a framework for auditing clinical AI systems based on accountability and governance standards.
✦ Why It Matters
Evaluate your clinical AI systems against the proposed governance framework to enhance accountability.
Key Takeaways
- Assess clinical AI systems using the proposed governance standards.
- Differentiate between generated context and governed state in AI applications.
- Implement an immutable evidence ledger for better accountability.
- Utilize the six-level maturity framework to evaluate AI system capabilities.
How It Works
The paper introduces a tiered governance standard that includes four key components: an immutable evidence ledger that tracks changes over time, a belief state that is separate from evidence, a model for processing observations, and a system for causal typing of claims. This structured approach allows for better accountability in clinical AI by ensuring that the reasoning process is transparent and verifiable.