Neuro-Symbolic AI for Advanced Signal and Image Processing: A Review of Recent Trends and Future Directions

Abstract

Neuro-Symbolic Artificial Intelligence (NSAI) is an emerging paradigm that combines neural networks with symbolic reasoning. This paper provides a comprehensive review of NSAI techniques and their applications in advanced signal and image processing. The paper begins by introducing the fundamentals of NSAI and highlighting how it bridges the gap between data-driven learning and knowledge-based reasoning. It then surveys several key application domains, such as biomedical, autonomous robotics, and IoT, in order to understand the benefits and challenges of that integration. For each domain, how NSAI methods improve upon traditional purely neural or purely symbolic approaches is illustrated. A comparative analysis with conventional AI techniques is presented, underscoring the advantages of NSAI in terms of interpretability, generalization, and flexibility. The challenges that arise in developing NSAI systems, such as computational complexity, integration of heterogeneous models, and ethical considerations, are also discussed. Finally, future research trends in NSAI for signal and image processing, as well as the path toward more explainable and generalizable AI, are synthesized.