Traditional clinical research has long relied on statistical software such as SAS, Stata, and SPSS for data analysis. These tools are often costly, rigid in workflow, and limited in handling complex, multi-source, and unstructured medical data. With the rapid advancement of artificial intelligence, clinical research is undergoing a fundamental methodological transformation. AI not only automates data analysis processes but also handles structured and unstructured data through machine learning models, enabling a shift from investigating "whether there is an association" to "predicting future outcomes." This talk aims to systematically elaborate on the application logic, methodological framework, and implementation pathway of artificial intelligence in clinical research.

This talk systematically reviews the AI methodological workflow applicable to clinical research, covering two common types of outcome variables: binary and continuous. For binary outcomes, a machine learning pipeline is constructed, including the following stages: data preprocessing (missing value imputation, feature standardization), model selection (logistic regression, random forest, XGBoost, SVM, etc.), hyperparameter tuning (grid search, random search, Bayesian optimization), model calibration, and interpretability analysis (SHAP, PDP, ALE). For continuous outcomes, a corresponding regression modeling workflow is proposed, emphasizing validation strategies (cross-validation, nested validation), overfitting control, and the reproducibility and clinical deployment pathway of results.

Through systematic exposition and graphical illustration, this talk establishes a structured AI clinical research framework, including: Standardized Data Processing and Modeling: Clarifies suitable AI methods for different variable types (categorical, ordinal, continuous) and research questions (difference, association, prediction). Comprehensive Methodological Guidance: Provides actionable technical pathways and considerations from data cleaning and model training to validation and deployment, such as handling class imbalance, preventing overfitting, and model interpretation. Visualization and Interpretation Tools: Emphasizes the importance of tools like ROC curves, calibration curves, feature importance plots, and SHAP values in model evaluation and clinical interpretation. Reproducibility and Clinical Integration: Proposes methods such as setting random seeds, documenting hyperparameters, and using version control, and designs a clinical deployment flowchart to support the transition from model to clinical decision-making.

Artificial intelligence is profoundly transforming the methodological landscape of clinical research, enabling automation, intelligence, and interpretability across the entire pipeline from data preprocessing to model deployment. This study systematically summarizes the methodological framework of AI in analyzing both binary and continuous variables, emphasizing the three principles of standardization, interpretability, and clinical applicability. Moving forward, clinical researchers should actively integrate AI methods to enhance research efficiency and scientific impact, thereby advancing data-driven medical decision-making.