Integrating Patient-Specific Data for Enhanced Hallucination Detection in Clinical Language Models

Abstract

In recent years, the application of language models within clinical settings has promised significant advancements in healthcare delivery and diagnostics. However, the phenomenon of hallucination, where models generate plausible yet incorrect or unrelated information, poses substantial risks. This paper explores the integration of patient-specific data as a novel approach to enhance the detection and mitigation of hallucinations in clinical language models. By leveraging individualized patient records, including demographics, medical history, and lab results, our approach aims to contextualize model outputs more accurately, thereby reducing the incidence of erroneous information generation.

We propose a framework that incorporates patient-specific data into the model's inference process, facilitating a more personalized and context-aware language generation. This integration is achieved through a dual mechanism: first, by embedding patient-specific variables directly into the model's input layer, and second, by employing a post-processing filter tailored to the patient's clinical context. The framework is evaluated using a comprehensive dataset comprising diverse patient profiles, allowing for rigorous assessment across different medical specialties and conditions.

Our results demonstrate a marked improvement in hallucination detection rates, with a significant reduction in false-positive outputs when compared to traditional, non-personalized models. The incorporation of patient-specific data not only enhances the model's factual accuracy but also fosters a more trustworthy interaction between clinicians and AI systems. This approach underscores the potential of personalized data integration as a critical component in the development of reliable clinical language models.

The findings advocate for a paradigm shift in the design of clinical AI systems, emphasizing the importance of context-aware mechanisms that align closely with patient-centered care. Future work will extend this methodology to explore the scalability and adaptability of the proposed framework across various healthcare environments, ultimately aiming to refine clinical decision-making processes.