# Algorithmic Authority and the Clinical Standard of Care

arXiv: https://arxiv.org/abs/2606.00044 · HTML: https://mlsanity.com/paper/2606.00044/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Legal Alignment for Safe and Ethical AI](https://mlsanity.com/paper/2601.04175/) (2026)
- [Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner](https://mlsanity.com/paper/2412.18086/) (2024)
- [SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning](https://mlsanity.com/paper/2506.21355/) (2025)
- [Automation Bias in AI-Assisted Medical Decision-Making under Time Pressure in Computational Pathology](https://mlsanity.com/paper/2411.00998/) (2024)
- [Protocol Futuring: Speculating Second-Order Dynamics of Protocols in Sociotechnical Infrastructural Futures](https://mlsanity.com/paper/2512.06108/) (2025)
- [The Great Data Standoff: Researchers vs. Platforms Under the Digital Services Act](https://mlsanity.com/paper/2505.01122/) (2025)
- [The Vibe-Check Protocol: Quantifying Cognitive Offloading in AI Programming](https://mlsanity.com/paper/2601.02410/) (2026)
- [Neuro-Oracle: A Trajectory-Aware Agentic RAG Framework for Interpretable Epilepsy Surgical Prognosis](https://mlsanity.com/paper/2604.14216/) (2026)
- [Gender Bias in LLMs: Preliminary Evidence from Shared Parenting Scenario in Czech Family Law](https://mlsanity.com/paper/2601.05879/) (2026)
- [When Is Self-Disclosure Optimal? Incentives and Governance of AI-Generated Content](https://mlsanity.com/paper/2601.18654/) (2026)

## Abstract (arXiv)

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 enquoteCode 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 enquotehallucination 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.