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.

The authors have declared no competing interest.

This work was supported by the NIH Center for Interventional Oncology and the Intramural Research Program of the National Institutes of Health, National Cancer Institute, and the National Institute of Biomedical Imaging and Bioengineering, via intramural NIH Grants Z1A CL040015 and 1ZIDBC011242.Work also supported by the NIH Intramural Targeted Anti-COVID-19 (ITAC) Program, funded by the National Institute of Allergy and Infectious Diseases. The participation of HH was made possible through the NIH Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, Genentech, the American Association for Dental Research, the Colgate-Palmolive Company, and other private donors.

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