Metaheuristics for Explainable Medical Diagnosis Systems: A Comprehensive Synthesis of Algorithmic Foundations, Clinical Applications, and Comparative Evaluation

Abstract

Medical errors are estimated to cause approximately 250,000 deaths annually in the United States alone, making diagnostic safety a public-health priority. Artificial intelligence (AI) promises substantial improvements in diagnostic accuracy, yet its clinical adoption remains constrained by the opacity of deep-learning models, by regulatory requirements for Software as a Medical Device (SaMD), and by the European Union General Data Protection Regulation's right to an explanation. This paper synthesises more than two decades of research on metaheuristic optimization as applied to explainable medical diagnosis, organised around three contributions. First, we provide a structured taxonomy of four algorithmic families (evolution-based, swarm-based, physics/human-based, and hybrid) together with three explainability paradigms (LIME, SHAP, Grad-CAM). Second, we evaluate fifteen metaheuristic algorithms on eight benchmark medical datasets spanning tabular electronic health records (WDBC, PIMA, SPECT, Hepatitis, MIMIC-III), medical imaging (HAM10000), and biosignal processing (MIT-BIH ECG), applying a uniform protocol of 30 independent runs, 5-fold stratified cross-validation, Friedman ranking, and Wilcoxon signed-rank tests with Holm-Bonferroni correction. The proposed hybrid GA-BPSO-LIME-SHAP algorithm achieves the best mean accuracy on seven of the eight benchmarks, with 99.27 percent accuracy on WDBC using only 9 of 30 features (AUC 0.994), and attains the lowest Friedman rank of 1.7. An ablation study confirms that all seven algorithmic components contribute meaningfully (9.5-27.6 percent degradation when removed). Third, we critically examine five limitations of the field and articulate a five-point research roadmap covering surrogate-assisted metaheuristics, adaptive operator selection, preference learning, federated medical optimization, and reproducibility infrastructure.