A Novel Machine Learning-Optimized Framework for Systematic Analysis of Foundation Models in Healthcare: Comprehensive Algorithm Optimization With Governance-Driven Predictive Modeling

Abstract

This paper presents an innovative computational framework that combines systematic literature review methodology with machine learning techniques to analyze Foundation Models (FMs) deployment in healthcare systems. We developed a novel approach integrating PRISMA 2020 guidelines with Latent Dirichlet Allocation (LDA) topic modeling, analyzing 92 peer-reviewed studies (2021-2025). Through systematic benchmarking of eight optimization algorithms, Particle Swarm Optimization (PSO) emerged as optimal for LDA hyperparameter tuning, achieving superior topic coherence (fitness: -0.89546 on negative coherence scale, where lower values indicate better model performance). Our analysis revealed two dominant implementation paradigms: clinical practice applications (44.57%) and image-based diagnostic systems (55.43%). In the experimental extension, we developed a hybrid classification framework. This framework captures governance-related factors influencing FM adoption using both binary and multilabel approaches. Binary classification achieved AUC=0.956 with Logistic Regression, while multilabel classification (10 thematic clusters) using Gradient Boosting achieved Hamming loss of 0.071, revealing that 71% of papers exhibit multi-domain characteristics, with an overall average of 3.12 thematic cluster assignments per document across the entire 92-paper corpus. LIME-based interpretability revealed distinct regulatory patterns across application domains. Notably, while safety and bias concerns appear in >70% of studies, critical dimensions like accountability (8.7%) and patient-centered design (12.0%) remain underrepresented. The framework demonstrates robust performance across multiple independent runs, providing a replicable methodology for analyzing emerging AI technologies. All code and annotation guidelines are available upon request, supporting reproducibility and extension of this interdisciplinary approach.

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