Algorithmic Authority and the Clinical Standard of Care
Aizierjiang Aiersilan
Why It Matters
What makes this one worth your time
As AI becomes more prevalent in healthcare, understanding its implications on clinical standards and liability is crucial for both practitioners and developers.
The paper proposes a governance framework for AI-physician collaboration in clinical diagnostics.
Summary
The paper discusses the integration of AI in clinical medicine, highlighting the tension between algorithmic reasoning and physician intuition, and proposes a new governance framework for AI-physician collaboration in diagnostics.
Key contributions
- Introduces a dialectical standard of care for AI-physician collaboration.
- Applies Lessig's 'Code is Law' framework to medical regulation.
- Identifies parallels between AI failures and human cognitive biases.
Notable insights
- Reframing AI hallucinations as similar to human cognitive biases offers a new perspective on governance in clinical settings.
- The proposal for a dialectical standard of care emphasizes the need for a collaborative approach between AI and human expertise.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2606.00044v1 Announce Type: cross Abstract: The integration of artificial intelligence into clinical medicine creates a fundamental tension between algorithmic probabilistic reasoning and the experiential intuition of expert physicians; applying Lawrence Lessig's \enquote{Code is Law} framework, I argue that the architecture of clinical AI systems already functions as de facto medical regulation, reshaping liability and the standard of care. Reframing AI \enquote{hallucination} as structurally analogous to well-documented human cognitive failures such as confirmation bias and premature diagnostic closure, I show that both failure modes demand a unified governance response. I therefore propose a dialectical standard of care that treats the integrated AI-physician dyad as the singular responsible diagnostic entity, mandating the synthesis of algorithmic precision with human interpretive authority within robust data governance and patient privacy frameworks.