# How Clinicians Think and What AI Can Learn From It

arXiv: https://arxiv.org/abs/2601.12547 · HTML: https://mlsanity.com/paper/2601.12547/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 65 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Causal machine learning for predicting treatment outcomes](https://mlsanity.com/paper/2410.08770/) (2024)
- [People construct simplified mental representations to plan](https://mlsanity.com/paper/2105.06948/) (2021)
- [A Survey of Constraint Formulations in Safe Reinforcement Learning](https://mlsanity.com/paper/2402.02025/) (2024)
- [Lexicographic Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2212.13769/) (2022)

## Built on by (most cited first, 1 of 1)

- [NEURON: A Neuro-symbolic System for Grounded Clinical Explainability](https://mlsanity.com/paper/2605.01189/) (2026)

## Abstract (arXiv)

Clinical artificial intelligence increasingly builds high-dimensional representations of patients, yet every finite clinical model is an abstraction. The key question is not only how accurately a model predicts, but which distinctions need to be represented, at what resolution, for the decision at hand. We argue that purposive clinical AI should use decision-sufficient abstraction: model detail should be conditioned by the objective structure of the decision and limited by the evidence available to support that detail. We further argue that this objective structure should often be ordinal-first. The primitive objects are objective-threshold propositions that may recur at progressively finer levels. For example, mortality below 10 percent may appear early and mortality below 5 percent later. We formalize ordered threshold refinement, a set-valued decision relation, and threshold-sufficient abstraction. A model needs only enough resolution to preserve the survivor set induced by the thresholds reached so far. Once an earlier threshold resolves the choice, further refinement of later objectives has no decision value for that choice. When a potentially decisive threshold is unresolved, additional information or model complexity should be targeted to that threshold rather than added globally. The framework links clinical reasoning, model abstraction, causal inference, lexicographic optimization, constrained learning, and human-AI collaboration. Its central claim is that clinical AI should pursue purpose before fidelity, order before abstraction, and refinement until decision-sufficient rather than reality-complete.