Comparative Analysis of Artificial Intelligence Methods in Clinical Implementation: A Review of Techniques, Validation Strategies, and Success Metrics

Abstract

Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into clinical workflows, yet evidence comparing their real-world effectiveness remains fragmented. This review systematically evaluates AI/ML methods deployed in healthcare, focusing on implementation strategies, validation rigor, and performance metrics. To identify the most frequently implemented AI/ML techniques, assess their clinical success rates, and analyze workflow integration challenges across specialties. We reviewed PubMed articles (2019–2024) describing AI/ML clinical applications with quantitative outcomes. Ten studies met inclusion criteria, covering radiology, oncology, and pediatrics. Data were extracted on AI methods, validation types, performance metrics (e.g., sensitivity, AUC), and workflow integration. Descriptive statistics summarized findings. Logistic regression and deep learning (e.g., atlas-matching) were the most specified methods. Logistic regression achieved 71% sensitivity and 77% PPV in epilepsy screening, matching clinician performance. Deep learning models showed >90% retrospective acceptability in radiotherapy planning but lacked prospective metrics. Only 40% of studies reported quantitative outcomes; others emphasized usability or frameworks. Workflow integration (e.g., EHR embedding) was critical but inconsistently detailed. While both traditional and advanced AI methods demonstrate clinical utility, heterogeneous reporting and limited head-to-head comparisons hinder definitive conclusions. Future research should prioritize standardized performance metrics and prospective multi-method evaluations to guide evidence-based adoption.

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