Treatment, evidence, imitation, and chat

TL;DR

This paper explores the potential and challenges of using large language models to assist in medical decision-making, emphasizing ethical considerations and the distinction between imitation and true treatment solutions.

Contribution

It analyzes how large language models can be trained for medical treatment decisions, highlighting ethical issues and the limitations of imitation in clinical contexts.

Findings

Imitation alone cannot solve the true treatment problem.

Ethical challenges are central to training LLMs for medical decisions.

Discussion centered on cholesterol medications, statins.

Abstract

Large language models are thought to have the potential to aid in medical decision making. This work investigates the degree to which this might be the case. We start with the treatment problem, the patient's core medical decision-making task, which is solved in collaboration with a clinician. We discuss different approaches to solving it, including, within evidence-based medicine, experimental and observational data. We then discuss the chat problem, and how this differs from the treatment problem -- in particular with respect to imitation (and how imitation alone cannot solve the true treatment problem, although this does not mean it is not useful). We then discuss how a large-language-model-based system might be trained to solve the treatment problem, highlighting that the major challenges relate to the ethics of experimentation and the assumptions associated with observation. We finally discuss how these challenges relate to evidence-based medicine and how this might inform the efforts of the medical research community to solve the treatment problem. Throughout, we illustrate our arguments with the cholesterol medications, statins.