## 4 Discussion  
The present study aimed to evaluate the performance of LLMs in extracting patient characteristics from free-text clinical records, addressing a critical gap in the existing literature [51]. Accurate and efficient extraction of patient characteristics is crucial for various healthcare applications, including clinical trial recruitment, personalized medicine, and epidemiological research [52]. Our comprehensive evaluation framework, which assessed LLM performance across various dimensions, including semantic textual similarity, factual consistency, coherence, prevalence, fluency, and consistency, provides valuable insights into the strengths and limitations of different models and their potential to advance the field of patient characteristics extraction. One of the key contributions of our work is the focus on a diverse set of patient characteristics, including age with 336 patients ranging from 0 to 90 years old, BMI with 43 patients among 3 categories, disease with 280 patients, gender with 341 patients, WBC count with 37 patients, and platelets count with 61 patients. Previous studies have often focused on a limited set of characteristics or have not provided detailed evaluations across multiple dimensions [53]. By assessing LLM performance on this diverse set of characteristics, we provide a more comprehensive understanding of their capabilities and limitations in extracting clinically relevant information from free-text records. This is particularly important given the heterogeneity of clinical data and the need for models that can handle a wide range of patient characteristics [52]. Our results demonstrate that GPT4, particularly when feed on clean datasets, exhibits superior performance in accurately extracting patient characteristics compared to other models like GPT3.5, Ilama2, and Bard. This finding builds upon previous work that has highlighted the potential of LLMs for clinical information extraction [54] and extends it by providing a more nuanced evaluation of model performance across different characteristics and dataset conditions. The strong performance of GPT4 suggests that it could be a valuable tool for automating patient characteristics extraction in clinical settings, potentially saving time and resources compared to manual chart review [55]. The use of large language models (LLMs) for data annotation in healthcare raises critical privacy and ethical considerations. One primary concern is the handling of sensitive patient data. While LLMs offer significant efficiency improvements in data extraction, they must comply with regulatory frameworks such as HIPAA and GDPR to ensure that patient confidentiality is maintained. Moreover, there is a risk of model-generated biases, as LLMs may reflect and propagate existing disparities present in training data, potentially leading to inaccurate or inequitable annotations. Ethical deployment of LLMs in healthcare necessitates ongoing human oversight to validate outputs, mitigate biases, and ensure transparency in decision-making processes. Furthermore, researchers and practitioners must implement stringent data anonymization techniques and secure processing environments to prevent unauthorized access or misuse of annotated clinical data. Addressing these ethical challenges is crucial to fostering trust and ensuring that LLM-driven data annotation aligns with principles of fairness, accountability, and patient-centric care [56]. However, our study also highlights the challenges and considerations involved in applying LLMs to patient characteristics extraction. We found that data quality, as reflected in the use of clean versus raw datasets, had a significant impact on model performance, particularly for complex characteristics like disease and platelets count. This echoes previous findings on the importance of data preprocessing and quality control in clinical natural language processing [57] and underscores the need for careful data curation when applying LLMs to patient characteristics extraction.