Simulated Misuse of Large Language Models and Clinical Credit Systems

Abstract

Large language models (LLMs) have been proposed to support many healthcare tasks, including disease diagnostics and treatment personalization. While AI models may be applied to assist or enhance the delivery of healthcare, there is also a risk of misuse. LLMs could be used to allocate resources based on unfair, inaccurate, or unjust criteria. For example, a social credit system uses big data to assess trustworthiness in society, punishing those who score poorly based on evaluation metrics defined only by a power structure (corporate entity, governing body). Such a system may be amplified by powerful LLMs which can rate individuals based on high-dimensional multimodal data - financial transactions, internet activity, and other behavioural inputs. Healthcare data is perhaps the most sensitive information which can be collected and could potentially be used to violate civil liberty via a clinical credit system, which may include limiting or rationing access to standard care. This report simulates how clinical datasets might be exploited and proposes strategies to mitigate the risks inherent to the development of AI models for healthcare.