## 2 Materials and Methods  
### Establishing Model Clinical Credibility Through Standardized Validation  
Establishing clinical credibility in ML models requires following a validation framework that adheres to regulatory standards. The validation process encompasses five interconnected domains as illustrated in Figure 1: model description, data description, model training, model evaluation, and life-cycle maintenance. These components form a structured pathway for ensuring model reliability and clinical applicability in healthcare settings. The model description phase establishes foundational elements by specifying model inputs, outputs, architecture, and parameter definitions. This transparency enables proper assessment of a model's theoretical underpinnings and computational approach. The framework then progresses to data description, where training datasets undergo rigorous characterization to ensure relevance and reliability. Particular attention must be directed toward data collection methodologies, annotation processes, and potential sources of algorithmic bias that could compromise model performance across diverse patient populations. Model training represents a critical validation component requiring detailed documentation of learning methodologies, performance metrics, and hyperparameter optimization. This documentation establishes computational reproducibility and enables independent verification of model development processes. As outlined in FDA guidance [29], train ing procedures should incorporate appropriate strategies to prevent overfitting while maintaining model generalizability across diverse clinical scenarios. The model evaluation phase introduces stringent requirements for testing with independent datasets that were not utilized during development. This separation between training and testing data constitutes a fundamental validation principle that reveals a model's true clinical utility. Models demonstrating high performance exclusively on development datasets yet failing with independent test data manifest what regulatory frameworks identify as deceptively high accuracy--signaling inadequate clinical validation. The evaluation process must include comprehensive metrics with confidence intervals, uncertainty quantification, and a systematic assessment of limitations and potential biases. Additionally, the careful consideration of sample size and statistical power is essential for meaningful clinical validation of ML models, as underscored by recent sample size analyses in the field [30]. Life-cycle maintenance completes the validation framework by establishing protocols for longitudinal performance monitoring, model updates, and risk-based oversight. This phase acknowledges the dynamic nature of clinical environments and ensures sustained model credibility through structured retraining procedures and regulatory reporting mechanisms. According to FDA guidance, models deployed in clinical settings require ongoing performance verification to maintain credibility as patient populations and clinical practices evolve. Figure 1: Standardized validation framework for establishing and maintaining clinical credibility in healthcare machine learning models. The framework presents five interconnected domains (model description, data description, model training, model evaluation, and life-cycle maintenance) with sequential validation steps within each domain. Arrows indicate the logical progression within domains (solid lines) and dependencies between domains (dashed lines). This structured approach addresses critical FDA regulatory requirements for AI credibility assessment, ensuring comprehensive documentation of model development, rigorous evaluation with independent test data, and continuous performance monitoring throughout the model life cycle. In the context of Figure 1, the \"Data Description\" domain is pivotal for ensuring that the model is developed on datasets that are both representative and reliable for the intended clinical application.