## 4 Discussion
### Clinical Implications of the Composite Utility Metric
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Metric** & **Equation** & **Clinical Relevance** \\ \hline Overall Model Score (Equation (20)) & \(\text{Score}(m_{\theta})=\alpha_{1}\cdot\text{AUC}(m_{\theta})+\alpha_{2}\cdot \text{Sensitivity}(m_{\theta})+\alpha_{3}\cdot\text{Specificity}(m_{\theta})\) & Integrates global discriminative performance with sensitivity and specificity to ensure that both the ability to detect true positive cases and to correctly identify negatives are balanced, reflecting the dual clinical need to avoid missed diagnoses and false alarms. \\ \hline Clinical Utility Score (Equation (25)) & Clinical Utility \(=\omega_{1}\cdot\text{PPV}+\omega_{2}\cdot\text{NP}+\omega_{3}\cdot\text{F1}\) & Consolidates predictive positive value, negative predictive value, and the F1 score to directly connect model performance with patient management outcomes, thereby mitigating diagnostic ambiguity in clinical practice. \\ \hline Weighted Endpoint Accuracy (WEAS) (Equation (30)) & \(\text{WEAS}=\frac{w\cdot\text{Acc}_{0}+\sum_{i=1}^{n-1}\text{Acc}_{i}+w\cdot \text{Acc}_{n}}{2w+(n-1)}\) & Emphasizes the accurate classification of critical endpoints (e.g., healthy vs. severely pathological cases) by assigning extra weight to these classes, which is relevant when prioritizing patient safety in high-stakes clinical decisions. \\ \hline Composite Utility Metric (Equation (32)) & \(U=\lambda\cdot\text{WEAS}-(1-\lambda)\cdot\text{Penalty}\) & Combines the weighted endpoint accuracy (WEAS) with a penalty for misclassifications, thereby quantifying not only the model’s success in critical classifications but also the clinical cost of errors—a balance central to safe and effective patient care. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Suggested clinical evaluation metrics for ML in healthcare. intermediate stages. Consequently, \(U\) serves as a relevant tool for clinicians and stakeholders by aligning the performance evaluation with clinical priorities, thus supporting safer and more effective patient management decisions. The clinical prioritization of endpoint accuracy through weighting (\(w=2\) for classes 0 and 4 vs. intermediate classes) mirrors established medical protocols where clear differentiation between normal and severely affected states drives treatment pathways. However, the additional penalty mechanism acknowledges that even smaller misclassifications between adjacent grades (e.g., class 2 mislabeled as class 3) accumulate operational costs through unnecessary referrals, repeated imaging, or delayed interventions. This dual focus, prioritizing critical distinctions while disincentivizing all errors proportionally, creates performance benchmarks that better reflect clinical workflows compared to conventional accuracy metrics. Ultimately, composite scoring methodologies address a fundamental disconnect between ML evaluation and medical practice. By translating statistical performance into risk-adjusted utility measures, they provide frameworks for model assessment that account for both diagnostic priorities and the asymmetric consequences of different error types. This alignment is essential for validating AI systems in healthcare, where computational metrics must be subordinated to patient safety considerations and clinical decision-making realities. A critical consideration in the clinical deployment of ML models is the selection and justification of weighting coefficients for composite evaluation metrics. The optimal balance between sensitivity and specificity is not universal, but rather context-dependent and should be tailored to the intended clinical application. For example, in population-level screening programs (e.g., national mammography campaigns or infectious disease surveillance) the primary objective is to identify as many true-positive cases as possible, even at the expense of increased false positives.