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This paper investigates the risks of using LLMs in healthcare, specifically focusing on memorization, prompt inference errors, and retrieval hazards. The study compares GPT-4, MedPaLM, and ClinicalBERT, finding that general-purpose models like GPT-4 exhibit higher memorization and inference risks compared to domain-specific models. The authors propose recommendations for safe LLM integration in healthcare, including data governance, prompt validation, and retrieval safeguards, and outline a framework for risk mitigation.
GPT-4's general knowledge comes at a cost: it memorizes more sensitive data and makes more prompt inference errors than specialized healthcare LLMs.
This study examines the risks associated with the deployment of large language models (LLMs) in healthcare, focusing on memorization, prompt inference errors, and retrieval hazards. LLMs, such as GPT-4, MedPaLM, and fine- tuned clinical models like ClinicalBERT, are increasingly used in clinical decision support, diagnostic assistance, and administrative automation. While these models offer significant potential in improving healthcare delivery, they also present privacy and safety risks. The study investigates how these models memorize sensitive data, generate incorrect or unsafe responses due to prompt errors, and retrieve irrelevant or confidential information through external knowledge bases. The findings reveal that GPT-4, a general-purpose model, exhibits higher memorization and inference risks compared to domain-specific models like MedPaLM and ClinicalBERT, which showed improved performance in healthcare tasks and reduced memorization tendencies. The study also emphasizes the importance of prompt engineering, the potential hazards of retrieval-augmented generation (RAG) systems, and the necessity of privacy-preserving techniques. Based on these findings, the paper proposes a set of practical recommendations for safe LLM integration in healthcare, including data governance practices, prompt validation protocols, and retrieval safeguards. Finally, the study outlines a framework for risk mitigation and suggests directions for future research, including longitudinal studies on model drift, cross-institutional validation of risk profiles, and human-in-the-loop interventions for real-world deployment. The findings provide essential insights for clinicians, AI researchers, and policymakers working to safely deploy AI in healthcare.
