New Study Links LLMs, Recourse, and Bandits for Personalized Medicine

Global: New Study Links LLMs, Recourse, and Bandits for Personalized Medicine

Unified Framework for High‑Stakes Decision‑Making

A team of researchers has introduced a unified framework that integrates algorithmic recourse, contextual bandits, and large language models (LLMs) to improve sequential decision‑making in high‑risk domains such as personalized medicine.

Defining the Recourse Bandit Problem

The authors formalize a “recourse bandit” problem, wherein a decision‑maker must simultaneously choose a treatment action and a minimal, feasible adjustment to mutable patient characteristics, ensuring that recommended actions remain clinically viable.

Generalized Linear Recourse Bandit (GLRB)

To address this problem, the authors develop a generalized linear recourse bandit algorithm (GLRB) that extends traditional linear contextual bandits by incorporating constraints on permissible changes to patient features.

Language‑Model Informed Recourse (LIBRA)

Building on GLRB, the study presents LIBRA, a language‑model‑informed bandit recourse algorithm that leverages LLMs for domain knowledge while retaining statistical rigor. LIBRA offers three theoretical guarantees: a warm‑start guarantee that reduces initial regret when LLM suggestions are near‑optimal; an LLM‑effort guarantee that limits LLM queries to O(log^2 T) times over a horizon T; and a robustness guarantee that ensures performance never falls below that of a pure bandit approach when the LLM is unreliable.

Theoretical Bounds and Near‑Optimality

The authors establish matching lower bounds for the recourse bandit problem, demonstrating that the proposed algorithms achieve near‑optimal performance relative to these fundamental limits.

Empirical Evaluation

Empirical results on synthetic environments and a real‑world hypertension‑management case study show that both GLRB and LIBRA outperform standard contextual bandits and LLM‑only baselines in terms of regret, treatment quality, and sample efficiency.

Implications for Personalized Care

These findings suggest that incorporating recourse constraints and LLM insights can enhance the safety and effectiveness of automated decision‑support tools in personalized medicine, offering a pathway toward more trustworthy AI‑assisted clinical interventions.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via arXiv.