Network pharmacology has gained significant traction as a tool for identifying the mechanisms and therapeutic effects of herbal medicines. However, despite the usefulness of these approaches, their diversity underscores the critical need for a systematic evaluation to ensure consistency and reliability. We aimed to evaluate the network pharmacological analyses, focusing on identifying the mechanisms and therapeutic effects of herbal medicines. We employed a comprehensive approach involving systematic data retrieval, network construction, and analysis. Herbal compounds and their targets were meticulously extracted from five distinct network pharmacology databases to ensure extensive coverage and high data reliability. Advanced network-based methods were used to identify key herbal targets and predict therapeutic effects, thereby enriching the depth and breadth of the analysis. Experimental validation was performed on prostate cancer models to substantiate the computational predictions. The results of the recapitulating task for known herbal ingredient targets revealed distinct patterns in performance and coverage based on network construction and aggregation methods. We performed the same analysis to identify herbal targets and found that network centrality, path counts, and downweighted path counts had their own pros and cons. By comparing network-based methods, we found that considering the impact on the multiscale interactome yielded the highest accuracy in discriminating known therapeutic effects. Using optimal conditions, we successfully identified new indications for herbal medicines and validated these findings through follow-up in vitro and in vivo experiments. This study presents the first comprehensive and critical evaluation of the current network pharmacology analyses in the field of herbal medicine and provides valuable guidance for continued advances in the elucidation of the mechanisms and therapeutic effects.

Artificial intelligence (AI) is increasingly applied in medical education, but its role in fostering interactive clinical competencies remains underexplored. This pilot study aimed to compare the feasibility and educational impact of an AI chatbot–based simulation with traditional peer role-play (PRP) for Objective Structured Clinical Examination (OSCE) preparation, and to share practical lessons from implementing a novel AI tool in a trial setting. Nineteen final-year Korean medicine students were randomly assigned to either an AI chatbot group (n = 9) or a PRP group (n = 10) after a baseline knowledge test. Both groups underwent a 30-minute physical examination practice session, followed by a one-hour clinical interview training session specific to their group. The AI chatbot group practiced with a GPT-4o/Claude 3.5-based chatbot providing scenario-driven responses and automated feedback, while the PRP group practiced in pairs under tutor supervision. All participants then completed two OSCE stations (dizziness and shoulder pain). Performance was assessed using a structured checklist covering four domains: history taking, physical examination, patient education, and physician-patient interaction. Post-study questionnaires evaluated the learning experience. Although the differences in OSCE scores between the groups did not reach statistical significance, several interesting and complementary trends were observed. For example, the PRP group tended to score higher in history taking (mean 74.4 vs. 66.2 in dizziness scenario; Hedges’ g = -0.68, mean 58.6 vs. 54.5 in shoulder pain scenario; Hedges’ g = -0.21), while the AI chatbot group showed a tendency towards higher scores in patient education (32.5 vs. 22.2 in dizziness scenario Hedges’ g = 0.44, 85.0 vs. 66.7 in shoulder pain scenario; Hedges’ g = 0.99). Survey results reflected these following trends. The PRP group valued the authenticity of the interaction and the exam-like environment. In contrast, the AI chatbot group reported higher satisfaction with the autonomy, opportunity for repetitive practice, and structured feedback. In this pilot study, AI chatbot–based training and PRP demonstrated complementary strengths for OSCE preparation. While PRP appears effective for developing performance-based procedural and communication skills in a realistic setting, AI chatbots show potential for fostering clinical reasoning in a self-paced, reflective learning environment. These complementary strengths suggest a blended learning model, combining both methods, may be optimal for holistic clinical skills development. Further research is needed to validate these preliminary findings.

Herbal formulae (HFs) are representative interventions in Korean medicine (KM) for the prevention and treatment of various diseases. Here, we proposed a machine learning-based approach for HF recommendation with enhanced interpretability and applicability. A dataset consisting of clinical symptoms, Sasang constitution (SC) types, and prescribed HFs was derived from a multicenter study. Case studies published over 10 years were collected and curated by experts. Various classifiers, oversampling methods, and data imputation techniques were comprehensively considered. The local interpretable model-agnostic explanation (LIME) technique was applied to identify the clinical symptoms that led to the recommendation of specific HFs. We found that the cascaded deep forest (CDF) model with data imputation and oversampling yielded the best performance on the training set and holdout test set. Our model also achieved top-1 and top-3 accuracies of 0.35 and 0.89, respectively, on case study datasets in which clinical symptoms were only partially recorded. We performed an expert evaluation on the reliability of interpretation results using case studies and achieved a score close to normal. Taken together, our model will contribute to the modernization of KM and the identification of an HF selection process through the development of a practically useful HF recommendation model.

Despite the importance of accurate Sasang type diagnosis, a unique form of Korean medicine, there have been concerns about consistency among diagnoses. We investigate a data-driven integrative diagnostic model by applying machine learning to a multicenter clinical dataset with comprehensive features. Extremely randomized trees (ERT), support vector machines, multinomial logistic regression, and K-nearest neighbor were applied, and performances were evaluated by cross-validation. The feature importance of the classifier was analyzed to understand which information is crucial in diagnosis. The ERT classifier showed the highest performance, with an overall f1 score of 0.60 ± 0.060. The feature classes of body measurement, personality, general information, and cold–heat were more decisive than others in classifying Sasang types. Costal angle was the most informative feature. In pairwise classification, we found Sasang type-dependent distinctions that body measurement features played a key role in TE-SE and TE-SY datasets, while personality and cold–heat features showed importance in SE-SY dataset. Current study investigated a comprehensive diagnostic model for Sasang type using machine learning and achieved better performance than previous studies. This study helps data-driven decision making in clinics by revealing key features contributing to the Sasang type diagnosis.

Objectives: This study aimed to develop an AI-based CPX (Clinical Performance Examination) practicing chatbot for Korean medicine education, implementing automated quantitative scoring and qualitative feedback systems to enhance individualized learning experiences.Methods: Building upon a previously developed CPX practicing chatbot, we integrated a quantitative scoring system and a qualitative text feedback system using Large Language Models (LLMs). Scoring prompts and feedback prompts were designed based on standardized CPX scenarios. We compared the performance of OpenAI's GPT-4 and Anthropic's Claude-3.5-Sonnet models in terms of scoring accuracy, feedback quality, consistency, latency, and fluency. Three sample chat histories representing varying levels of student performance were created for evaluation.Results: Claude-3.5-Sonnet demonstrated perfect accuracy in quantitative scoring across all samples and faster execution times (average 43.2 seconds) compared to GPT-4. In qualitative feedback generation, GPT-4 provided more specific and actionable feedback, while Claude-3.5-Sonnet produced more natural and supportive language. By combining the strengths of both models, we implemented an optimized system that first uses GPT-4 for detailed feedback generation and then refines the language with Claude-3.5-Sonnet, resulting in accurate scoring and high-quality feedback.Conclusions: The study successfully developed an LLM-based CPX practicing chatbot that offers automated scoring and individualized feedback for Korean medicine education. The optimized system enhances the learning experience by providing accurate assessments and specific, supportive feedback, addressing limitations in current CPX education practices and resource constraints.

Objectives: In the context of competency-based education emphasized in Korean Medicine, this study aimed to develop a pilot version of a CPX (Clinical Performance Examination) Practicing Chatbot utilizing large language models with prompt engineering.Methods: A standardized patient scenario was acquired from the National Institute of Korean Medicine and transformed into text format. Prompt engineering was then conducted using role prompting and few-shot prompting techniques. The GPT-4 API was employed, and a web application was created using the gradio package. An internal evaluation criterion was established for the quantitative assessment of the chatbot's performance.Results: The chatbot was implemented and evaluated based on the internal evaluation criterion. It demonstrated relatively high correctness and compliance. However, there is a need for improvement in confidentiality and naturalness.Conclusions: This study successfully piloted the CPX Practicing Chatbot, revealing the potential for developing educational models using AI technology in the field of Korean Medicine. Additionally, it identified limitations and provided insights for future developmental directions.