Artificial Intelligence Applications Across Critical Domains: A Comprehensive Survey of Recent Advances in Healthcare, Finance, Security, and Beyond

Abstract

Artificial intelligence (AI) has emerged as a transformative technology reshaping virtually every sector of modern society. From healthcare diagnostics and drug discovery to financial risk management, cybersecurity, autonomous transportation, and environmental sustainability, AI-driven methodologies have demonstrated remarkable capabilities in processing complex, heterogeneous data and generating actionable insights. This comprehensive survey examines 133 recent research contributions spanning the period from 2023 to 2026, systematically analyzing the state-of-the-art AI techniques employed across diverse application domains. We categorize these contributions into seven major thematic areas: (1) natural language processing and intelligent communication, (2) healthcare and biomedical sciences, (3) financial services and compliance, (4) cybersecurity and privacy preservation, (5) intelligent detection and pattern recognition, (6) explainable AI and cross-domain learning, and (7) document intelligence and sustainable development. For each domain, we discuss the key methodological innovations, evaluate reported performance metrics, identify common challenges, and highlight emerging research trends. Our analysis reveals that deep learning architectures, federated learning frameworks, and differential privacy mechanisms represent the most frequently adopted technical foundations, while multi-modal data fusion and graph-based learning are gaining increasing attention. We conclude by identifying critical open challenges including model interpretability, fairness-aware AI, and the need for robust evaluation benchmarks, and propose directions for future research that can bridge the gap between theoretical advances and practical deployment.