## 1 Introduction
Nonetheless, as demonstrated in this article, the absence of adequate external data makes it even more pivotal to establish robust ML training protocols and to scrutinize the underlying learning dynamics. In particular, a close examination of learning curves, reflecting the model's behavior during training and validation, can serve as an essential diagnostic tool. When learning curves behave as desired, with a close alignment between training and validation performance, there is greater assurance that the model has captured generalizable patterns; conversely, abnormal or \"bad-behaving\" learning curves can signal overfitting or data leakage, thus foreshadowing inferior performance on unseen external data. This emphasis on thorough training evaluation underscores the importance of reliable training practices and serves as an effective surrogate for external validation when such data is not readily available. Recent literature has increasingly emphasized the importance of standardization and trust in the development and validation of AI models for healthcare. For example, [PERSON] et al. provide a comprehensive review of approaches to standardizing medical descriptions for clinical entity recognition, highlighting the implications for AI implementation [26]. [PERSON] et al. discuss the value of standardized health datasets in enabling robust AI-based applications [27], while [PERSON] et al. examine trust management from a standardization perspective [28]. These studies underscore the necessity of adopting standardized frameworks and data practices to ensure the reliability, generalizability, and clinical acceptance of ML models. Our work builds on these insights by proposinga validation methodology that not only aligns with these emerging standards but also addresses the specific challenges of clinical utility and external validation in real-world healthcare settings. _Aims, Contributions, and Organization_  
Recent advances in ML have led to a proliferation of studies reporting high accuracy in medical classification tasks; however, many of these models lack standardized validation and do not adequately reflect clinical utility when deployed beyond their development settings. This work addresses the specific challenge of bridging the gap between promising in-domain ML performance and the need for robust, clinically meaningful validation protocols that can ensure generalizability and regulatory alignment for real-world healthcare applications. The primary goal of this article is to present a standardized validation framework for developing clinically actionable healthcare ML models, specifically demonstrated through the case study of knee osteoarthritis grading. Central to our approach is the integration of credibility assessment by analyzing training dynamics (e.g., learning curves and overfitting diagnostics) alongside traditional performance metrics, highlighting the necessity of protocols and evaluation methods that align model performance with true clinical utility and external validity. Our original contributions are threefold: (1) we synthesize and implement a practical, FDA-aligned validation framework applicable to real-world clinical ML workflows; (2) we propose and evaluate composite clinical utility metrics to capture clinically meaningful model performance; and (3) we demonstrate it with a case study on knee osteoarthritis grading, highlighting the importance of robust training assessment and clinically oriented evaluation for improving model generalizability and patient safety. The remainder of this article is organized as follows: Section 2 details the materials and methods, including the proposed validation framework and evaluation protocols; Section 3 presents experimental results, including in-domain and cross-domain validation; Section 4 discusses the clinical implications of our findings; and Section 5 summarizes the key insights and recommendations for future research and clinical practice.