# High-Stakes Decisions with Language Models: Insights from Emergency Triage

arXiv: https://arxiv.org/abs/2608.01361 · HTML: https://mlsanity.com/paper/2608.01361/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning](https://mlsanity.com/paper/2406.00922/) (2024)
- [Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs](https://mlsanity.com/paper/2502.08640/) (2025)
- [Limitations of Large Language Models in Clinical Problem-Solving Arising from Inflexible Reasoning](https://mlsanity.com/paper/2502.04381/) (2025)
- [Evaluating language models as risk scores](https://mlsanity.com/paper/2407.14614/) (2024)
- [What Are the Odds? Language Models Are Capable of Probabilistic Reasoning](https://mlsanity.com/paper/2406.12830/) (2024)
- [Generative Value Conflicts Reveal LLM Priorities](https://mlsanity.com/paper/2509.25369/) (2025)
- [When Agents Say One Thing and Do Another: Validating Elicited Beliefs from LLMs](https://mlsanity.com/paper/2602.06286/) (2026)
- [AI as Decision-Maker: Ethics and Risk Preferences of LLMs](https://mlsanity.com/paper/2406.01168/) (2024)
- [When Do LLM Preferences Predict Downstream Behavior?](https://mlsanity.com/paper/2602.18971/) (2026)
- [Steering Risk Preferences in Large Language Models by Aligning Behavioral and Neural Representations](https://mlsanity.com/paper/2505.11615/) (2025)
- [Can Revealed Preferences Clarify LLM Alignment and Steering?](https://mlsanity.com/paper/2605.08556/) (2026)
- [Where Do Apparent LLM Clinical Triage Failures Arise? Localizing the Multiple-Choice Format Effect](https://mlsanity.com/paper/2605.29889/) (2026)

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

- [Training Language Models To Be Coherent Decision-Makers](https://mlsanity.com/paper/2610.09164/) (2026)

## Abstract (arXiv)

High-stakes decisions under uncertainty, such as medical emergency triage, require more than accurate predictions. They depend on estimating the likelihood of alternative outcomes while explicitly weighing the consequences of different actions, principles that have long formed the foundation of medical diagnosis and decision making. Yet language models are increasingly used for high-stakes clinical recommendations without explicit specification of the utilities governing these decisions. Here we show that emergency triage with language models can be understood within a probabilistic decision framework, providing a case study of a broader decision-analytic paradigm for steering, evaluating, and deploying language models in high-stakes settings. Using clinical vignettes from a structured evaluation of a consumer triage system, we analyze recommendations for treatment under alternative utility functions that specify the relative costs of missed emergencies and unnecessary escalation. We find that capable language models adjust recommendations in response to stated utilities, revealing that the same underlying predictions can support markedly different decision policies. These findings show that effective deployment depends not only on improving predictions but also on making decision objectives explicit. More broadly, they suggest that language models for high-stakes applications should be understood and evaluated as probabilistic decision systems whose recommendations depend jointly on predictive performance and explicit utilities.