Optimizing Hallucination Detection in Clinical Decision Support Systems

Abstract

In recent years, Clinical Decision Support Systems (CDSS) have emerged as pivotal tools in enhancing healthcare delivery by providing evidence-based recommendations to clinicians. However, an inherent challenge within these systems is the phenomenon of hallucination, where the system generates outputs that appear plausible but are factually incorrect. This paper addresses the critical need to optimize hallucination detection mechanisms within CDSS to ensure the reliability and safety of clinical recommendations.

To achieve this objective, we propose a multi-faceted approach that integrates advanced natural language processing techniques with domain-specific knowledge representation. The proposed framework employs deep learning models, specifically transformer-based architectures, to analyze and predict potential hallucinations by detecting anomalies in output patterns. By leveraging domain ontology and structured medical data, the system is further enhanced to cross-verify the generated recommendations against established medical guidelines and empirical evidence.

Our methodology includes a rigorous evaluation protocol, utilizing both synthetic and real-world datasets, to measure the effectiveness of the hallucination detection model. Key performance metrics, such as precision, recall, and F1-score, are used to assess the accuracy and reliability of the system in various clinical scenarios. Preliminary results demonstrate a significant improvement in the detection of hallucinations, with a notable reduction in false positives, thereby enhancing the overall trustworthiness of the CDSS outputs.

In conclusion, optimizing hallucination detection in CDSS represents a critical advancement in medical informatics. By employing sophisticated analytical techniques and integrating robust verification processes, the proposed system aims to mitigate risks associated with erroneous clinical advice. This work lays the foundation for future research endeavors focused on enhancing the interpretability and transparency of decision support systems in healthcare.