[Submitted on 4 Aug 2026]
Title:Large Language Models are Approximate Survival Estimators
View a PDF of the paper titled Large Language Models are Approximate Survival Estimators, by Juan M Zambrano Chaves and 7 other authors
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Abstract:Survival analysis estimates time-to-event outcomes from patient covariates and is widely used for medical risk assessment. Patients seeking prognostic information after a diagnosis may turn to large language models (LLMs), now readily accessible through consumer applications. However, whether LLMs can provide accurate survival predictions has not been rigorously evaluated. We introduce Survprompt, a framework that converts structured patient covariates into free-text clinical vignettes and prompts pre-trained LLMs to predict survival zero-shot. We benchmark Survprompt against conventional survival models, including random survival forests (RSF), across two multi-institutional pan-cancer cohorts: the publicly available MSK-CHORD cohort and a newly curated cohort from the Providence St. Joseph Health Network constructed using an LLM-based medical abstraction framework. We report censored mean absolute error (cMAE) and concordance index (c-index) and conduct feature ablations to identify variables influencing LLM predictions. Frontier LLMs achieved surprisingly competitive cMAE for individual survival times. For example, GPT-5.6-Sol achieved cMAE within 10% of state-of-the-art RSF models specifically trained for survival prediction for several cancer types and lower cMAE than RSF for prostate cancer in MSK-CHORD. Feature ablations revealed that LLMs prioritized clinical variables similarly to specialized survival models. However, LLMs showed inconsistent accuracy across cancer types and institutions and poorly discriminated between high- and low-risk patients (lower c-index). Zero-shot LLMs can generate surprisingly accurate prognostic estimates without specialized training, but their variable performance across cancer types and institutions remains an important limitation for clinical use.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.38181 [cs.CL]
(or arXiv:2609.38181v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.38181
arXiv-issued DOI via DataCite
Submission history
From: Juan Manuel Zambrano Chaves [view email] [v1] Tue, 4 Aug 2026 01:53:42 UTC (6,729 KB)
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