Artificial Intelligence Applications Across Interdisciplinary Domains: A Comprehensive Survey of Methods, Challenges, and Future Directions

Abstract

The rapid advancement of artificial intelligence (AI) has catalyzed transformative innovations across a multitude of disciplines, ranging from healthcare and biomedicine to finance, cybersecurity, energy sustainability, and beyond. This comprehensive survey systematically reviews 133 recent studies published between 2023 and 2026 that collectively represent the state of the art in AI-driven methodologies and their real-world applications. We categorize the reviewed literature into nine principal domains: (1) healthcare and biomedical sciences, (2) finance and risk management, (3) privacy-preserving computing and data security, (4) natural language processing and communication, (5) transportation and logistics, (6) energy and environmental sustainability, (7) education and social impact, (8) media and advertising technology, and (9) emerging cross-domain applications. For each domain, we analyze the predominant AI techniques employed, including deep learning, federated learning, reinforcement learning, natural language processing, and graph-based methods, while highlighting their respective strengths and limitations. Furthermore, we present a taxonomic framework that elucidates the interconnections among these application domains and identify critical research gaps that warrant future investigation. Our analysis reveals several overarching trends, notably the increasing adoption of privacy-preserving paradigms, the integration of multimodal data fusion, and the growing emphasis on explainability and fairness in AI systems. This survey provides researchers and practitioners with a holistic reference for understanding the current landscape and future trajectory of applied AI research.