Artificial Intelligence in Healthcare: A Comprehensive Systematic Review of Predictive Analytics, Medical Imaging, Cybersecurity, Resource Allocation, and Clinical Decision Support

Abstract

Artificial intelligence is moving from experimental use toward routine healthcare infrastructure, yet evidence remains fragmented across clinical, operational, and security domains. This systematic review synthesizes 35 peer-reviewed studies addressing five interconnected applications: predictive analytics, medical imaging, cybersecurity, resource allocation, and clinical decision support. Searches were conducted across PubMed/MEDLINE, IEEE Xplore-indexed records, publisher databases, and Google Scholar for English-language studies published from 2016 through June 2026. Eligible studies were assessed for clinical relevance, validation quality, transparency, bias, implementation readiness, and patient-safety implications. The evidence shows that predictive models can support earlier risk identification, readmission prevention, antimicrobial-resistance forecasting, and population-health surveillance. Imaging systems frequently achieved specialist-level discrimination in constrained datasets, particularly for retinal, dermatologic, breast, lung, and brain imaging, although external validation and workflow integration remained uneven. Cybersecurity research demonstrated value in anomaly detection, federated learning, privacy-preserving analytics, and adversarial-risk management, while also revealing that medical AI creates new attack surfaces. Resource-allocation studies supported demand forecasting, patient-flow management, supply-chain resilience, and treatment optimization. Clinical decision-support systems offered the strongest practical benefit when recommendations were explainable, calibrated, integrated into existing workflows, and subject to clinician oversight. Across all domains, performance declined when models encountered population shifts, incomplete data, weak governance, or poorly designed interfaces. The review concludes that healthcare AI should be evaluated as a sociotechnical intervention rather than a standalone algorithm. Sustainable value depends on prospective validation, equity auditing, cybersecurity-by-design, economic evaluation, transparent reporting, and continuous post-deployment monitoring. These requirements provide a unified roadmap for safer, more effective, and accountable adoption.