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AI

A hybrid Monte Carlo–LLM framework for synthetic-patient simulations

2026-08-01

A hybrid Monte Carlo–LLM framework for synthetic-patient simulations
Photo: ben.dracup (CC BY 2.0)

Authors: Hyun Ji Woo and Min-Gul Kim. Venue: Scientific Reports; peer reviewed, early unedited version. Contribution: numerical anchors constrain outcomes while eight closed-weight LLMs generate subjective distress and adherence, exposing model- and architecture-dependent variability. Significance: simulation studies should report model sensitivity, not treat synthetic patients as stable ground truth. Limitations: author-reported simulation findings; no real-patient validation or independent reproduction. Paper.

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