Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models (2504.19061v1)

Abstract: Clinical summarization is crucial in healthcare as it distills complex medical data into digestible information, enhancing patient understanding and care management. LLMs have shown significant potential in automating and improving the accuracy of such summarizations due to their advanced natural language understanding capabilities. These models are particularly applicable in the context of summarizing medical/clinical texts, where precise and concise information transfer is essential. In this paper, we investigate the effectiveness of open-source LLMs in extracting key events from discharge reports, such as reasons for hospital admission, significant in-hospital events, and critical follow-up actions. In addition, we also assess the prevalence of various types of hallucinations in the summaries produced by these models. Detecting hallucinations is vital as it directly influences the reliability of the information, potentially affecting patient care and treatment outcomes. We conduct comprehensive numerical simulations to rigorously evaluate the performance of these models, further probing the accuracy and fidelity of the extracted content in clinical summarization.

Assessment of Hallucinations and Key Information Extraction in Medical Texts Using Open-Source LLMs

The paper "Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source LLMs" offers a critical examination of the application of open-source LLMs in the domain of medical text summarization. With a focus on hospital discharge summaries, the authors investigate the dual challenges of key information extraction and hallucination generation, emphasizing the importance of these issues in the context of clinical workflows and patient safety.

Key Information Extraction

The extraction of pertinent clinical events from lengthy and complex medical documents is fundamental to enhancing healthcare delivery. The paper reports on the capacity of various open-source LLMs, including models like LLaMA, Phi, and Qwen, to distill essential information such as admission reasons, key hospitalization events, and follow-up recommendations from discharge summaries. Remarkably, the investigation reveals considerable variability in performance across LLMs, with Qwen2.5 and DeepSeek-v2 demonstrating higher efficacy in extracting admission reasons, whereas Phi and MistralLite display superiority in capturing details concerning hospitalization events.

Indeed, while certain models proficiently summarize reasons and events with relatively high accuracy, the ability to effectively convey necessary follow-up actions poses a challenge, with only Phi reaching a satisfactory level of performance in this aspect. Interestingly, unlike traditional summarization techniques constrained by text length limitations, the paper identifies that the shortcomings in capturing key details are not solely attributed to the character count restriction but rather to the prioritization mechanisms inherent in these models.

Hallucination Generation

An integral section of the paper explores the phenomenon of hallucinations, where LLMs inadvertently generate misleading or incorrect information that is either unsupported by or contradicts the source text. The authors classify hallucinations into unsupported facts and incorrect/contradicted facts, reporting significant instances in summaries generated by models such as Phi and DeepSeek-v2. The observation that models can introduce erroneous ages or incorrect treatment details underscores the potential risks hallucinations pose to clinical decision-making and patient safety.

In particular, it is noteworthy that no model was free from the generation of hallucinated content, indicating the pervasive nature of this issue across different LLM architectures. The challenge of ensuring factual consistency and adherence to the original medical text highlights the need for refined validation mechanisms and enhanced model fine-tuning procedures.

Implications and Future Directions

The implications of this research extend both practically and theoretically. Practically, employing these LLMs in clinical settings demands a cautious approach, ensuring robust validation and cross-checking mechanisms are in place to mitigate the risks posed by hallucinated content. The application of LLMs in medical text summarization holds promise for improving patient comprehension and clinical workflow efficiency, yet necessitates advancements in reliability to become a truly indispensable tool in healthcare.

Moving forward, further research is warranted to address the challenges identified in this study. The authors suggest ongoing refinements in model architecture and fine-tuning strategies as potential pathways to improving the accuracy and fidelity of LLM-generated summaries. Additionally, exploring the integration of domain-specific knowledge bases could impart enhanced factual grounding to these models, ensuring alignment with established medical practices and terminologies.

By providing a detailed evaluation of open-source LLMs in the context of medical text summarization, this paper lays the groundwork for advancing safe and effective AI applications in healthcare. It encourages a trajectory toward improved model design and validation methodologies, ensuring that the theoretical advancements in natural language processing translate into tangible benefits for clinical practice.