Multimedia multimodal artificial intelligence (MMAI): Foundations, challenges, and future directions - Son, Le Hoang; Damilola.I, Oni (Journal of Computer Science and Cybernetics, 2026)
Multimedia Multimodal Artificial Intelligence (MMAI) represents a transformational paradigm that enables machines to process and synthesize many modalities, e.g., text, image, audio, and video, to understand and generate complex multimedia content. This review provides an intensive exploration of multimedia multimodal artificial intelligence, which focuses on its basic models, major challenges, and future directions. Drawing insight from recent research trends and literature, this paper presents a comprehensive analysis of multimodal AI, fusion techniques, self-supervised learning strategies, and real-world applications such as healthcare, education, entertainment, and human-computer interactions. It also examines the theoretical foundation of MMAI, including multimodal representation, alignment, and fusion techniques, which are very important to integrate heterogeneous data sources while maintaining coherence and relevance. The review also mentions the role of self-supervised learning in reducing dependence on labeled datasets by taking advantage of the underlying structure of multimodal data. Additionally, this review highlights the ability of generic AI to create multimedia content, stretching the limits of what AI can do in creative and practical domains. Despite this progress, many challenges persist, including technical limitations like high computational costs, data inequality or heterogeneity, and model interpretability, as well as ethical concerns relating to privacy and bias. Finally, future research directions will be mapped out, including the development of scalable and efficient training methods, the integration of symbolic reasoning with deep learning, and the promotion of interdisciplinary collaboration. By synthesizing knowledge from leading studies and industry innovations, this review will be a blueprint for people, which aims to exploit the full potential of AI-driven multimedia technologies in an increasingly interconnected world.