Deployable AI Systems for Healthcare, Industry, Business, and Smart Infrastructure: A Cross-Domain Review

Abstract

Artificial intelligence has progressed from laboratory benchmark systems to operational tools embedded in healthcare diagnostics, industrial monitoring, business analytics, smart infrastructure, agriculture, cybersecurity, and assistive technologies. However, the transition from a high-performing model to a deployable AI system remains a persistent challenge: clinical, industrial, and organizational deployment requires far more than predictive accuracy, demanding workflow integration, data interoperability, real-time feasibility, explainability, privacy, security, governance, and sustained post-deployment maintenance. This structured critical review synthesizes using a six-axis deployment-centered taxonomy encompassing application domain, data modality, architecture family, deployment pathway, deployment-readiness concern, and decision-support function. Seven application domains are examined: healthcare and biomedical AI, human-centered and assistive AI, industrial monitoring and cyber-physical systems, smart infrastructure and IoT, agriculture and sustainability, business and enterprise analytics, and cybersecurity and distributed intelligence. Eight architecture families are characterized, from conventional machine learning and CNNs through vision transformers, graph neural networks, Bayesian physics-guided models, generative AI, and federated learning systems, each associated with distinct deployment constraints and trustworthiness requirements. Synthesis identifies recurrent cross-domain deployment gaps including validated explainability, uncertainty quantification, privacy-preserving inference at scale, lightweight edge deployment, evidence maturity, and governance-aligned reporting. A structured future research agenda addresses these gaps with actionable directions and evaluation requirements. This review provides researchers, engineers, and practitioners with a deployment-focused roadmap for building AI systems that are not only capable but trustworthy, sustainable, and ready for real-world use.