Treatment, evidence, imitation, and chat
This work addresses the application of AI in healthcare decision-making, but it is incremental as it primarily discusses existing concepts and challenges without presenting new solutions or results.
The paper examines the potential of large language models to assist in medical decision-making, focusing on the treatment problem and highlighting challenges related to evidence-based medicine and imitation.
Large language models are thought to have potential to aid in medical decision making. We investigate this here. We start with the treatment problem, the patient's core medical decision-making task, which is solved in collaboration with a healthcare provider. We discuss approaches to solving the treatment problem, including -- within evidence-based medicine -- trials and observational data. We then discuss the chat problem, and how this differs from the treatment problem -- in particular as it relates to imitation. We then discuss how a large language model might be used to solve the treatment problem and highlight some of the challenges that emerge. We finally discuss how these challenges relate to evidence-based medicine, and how this might inform next steps.