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

Abstract

Artificial intelligence (AI) has emerged as a transformative force across virtually every sector of modern society, fundamentally reshaping how we approach complex challenges in healthcare, financial services, cybersecurity, environmental sustainability, and beyond. This comprehensive survey systematically examines the latest advances in AI-driven methodologies spanning diverse application domains, with particular emphasis on deep learning, natural language processing (NLP), reinforcement learning, federated learning, and privacy-preserving techniques. We analyze over 130 recent studies published between 2023 and 2026, covering topics ranging from medical question answering and drug discovery to financial fraud detection, autonomous driving, and policy analytics. Through a structured taxonomy, we identify common technical foundations, cross-domain synergies, and emerging trends that define the current landscape of applied AI research. Our findings reveal that while domain-specific innovations continue to advance, the convergence of multi-modal data fusion, explainable AI, and privacy-aware computing represents a unifying trajectory across disciplines. This survey aims to provide researchers and practitioners with a holistic understanding of the state-of-the-art and to highlight promising directions for future interdisciplinary collaboration.