Most Cited AI Research (2024–2025): A Cross-Sector Review

Abstract

The blistering pace of generative and foundational AI models being deployed in 2024 and 2025 is transforming experiences in education, healthcare, science, sustainability and business. This narrative review consolidates findings from the 50 most cited peer reviewed publications in this time frame, providing a cross-cutting overview on the state of development, the application and the challenges concerning technology. We start by discussing the architectural origins behind both large language models, multimodal generators, as well as domain-specific foundation models including SpectralGPT and scGPT. Then, we evaluate their application on vertical-industrial applications of academic teaching, clinical diagnosis, supply chain operation, and environmental monitoring. The review discusses also important ethical and societal issues, including fairness, explainability, AI angst, and academic responsibility. We also discuss lingering technical challenges including hallucination, data privacy, and availability barriers despite recent progress made. Finally, we discuss some of the emerging frontiers that this work opens and their exciting implications, including controllable generation, symbolic-neural integration, and divergence between open and proprietary model ecosystems. This citation-motivated review provides a timely snapshot of how the most influential research is leading the development of generative AI across domains.