Optimizing Hallucination Detection in Clinical Chatbots Using Deep Learning

Abstract

The proliferation of chatbots in clinical settings has ushered in new possibilities for healthcare delivery, yet it also introduces challenges, particularly the phenomenon of hallucination, where generative models produce inaccurate or nonsensical outputs. This paper explores the application of deep learning techniques to optimize hallucination detection in clinical chatbots, aiming to enhance the reliability and trustworthiness of these systems. 

We propose a novel framework that leverages transformer-based architectures to identify and mitigate hallucinations in real-time interactions. The model incorporates a dual-stage validation process where contextual coherence and medical accuracy are cross-verified against a curated dataset of medical dialogues. By incorporating domain-specific knowledge through fine-tuning on medical corpora, the model achieves improved sensitivity and specificity in detecting hallucinated outputs compared to baseline approaches.

Our empirical analysis demonstrates the efficacy of the proposed framework across multiple evaluation metrics, showcasing a significant reduction in false-positive and false-negative rates. The integration of attention mechanisms allows for dynamic adjustment to the conversational context, thereby enhancing the model's adaptability to diverse clinical scenarios. Furthermore, the implementation of an attention-based feedback loop facilitates continuous learning, enabling the model to evolve with emerging medical knowledge and conversational nuances.

The findings underscore the potential of deep learning methodologies in refining the operational efficiency of clinical chatbots, ensuring that they remain robust against the generation of misleading information. This research contributes to the development of more reliable digital health tools, with implications for patient safety and the broader adoption of AI-driven solutions in healthcare environments. Future work will explore the scalability of this approach and its applicability across various medical domains, providing a pathway for the implementation of intelligent and trustworthy conversational agents in clinical practice.