Artificial Intelligence in Next-Generation Pattern Recognition and Image Analysis

Artificial Intelligence (AI) has become a foundational element of next-generation pattern recognition and image analysis, driving transformative advances across domains such as healthcare, autonomous systems, remote sensing, industrial inspection, and smart environments. Breakthroughs in deep learning, computer vision, and hybrid AI models have significantly enhanced the accuracy, scalability, and adaptability of modern image processing systems. However, increasing model complexity introduces critical challenges related to interpretability, robustness, trustworthiness, and real-time deployment.Contemporary pattern recognition systems must operate reliably under noisy, dynamic, and adversarial conditions while ensuring transparency and accountability in decision-making processes. Moreover, the convergence of data-driven learning with symbolic reasoning and domain knowledge is reshaping intelligent visual analysis. This Research Topic aims to explore methodological innovations and practical strategies that advance reliable, explainable, and efficient AI-driven image analysis for real-world applications.The primary goal is to advance the state of the art in artificial intelligence for next-generation pattern recognition and image analysis, with particular emphasis on robustness, interpretability, efficiency, and real-world deployment. Although deep learning approaches have achieved remarkable predictive performance, many current systems remain opaque, computationally demanding, and vulnerable to distribution shifts, adversarial perturbations, and data scarcity.These limitations hinder their adoption in safety-critical and high-stakes environments. This collection seeks contributions that address these challenges through innovative algorithms, theoretical developments, and application-driven solutions. We particularly encourage research on explainable AI techniques that enhance transparency in visual decision-making; robust and trustworthy AI frameworks capable of handling uncertainty and adversarial conditions; hybrid AI models integrating data-driven learning with symbolic reasoning or domain knowledge; and real-time, resource-aware vision systems suitable for edge and embedded platforms. Interdisciplinary perspectives that bridge methodological advances with practical validation are especially welcome. By fostering rigorous and responsible innovation, this Research Topic aims to promote AI-driven image analysis systems that are not only high-performing, but also reliable, interpretable, and societally aligned.This Research Topic welcomes high-quality contributions addressing theoretical advances, methodological innovations, and real-world applications of artificial intelligence in next-generation pattern recognition and image analysis. Submissions may explore, but are not limited to, the following themes:• Advanced deep learning architectures for image processing and visual understanding• Explainable AI approaches for computer vision and pattern recognition• Robust and trustworthy AI under uncertainty, distribution shifts, and adversarial conditions• Hybrid AI models combining data-driven learning with symbolic reasoning or domain knowledge• Real-time vision systems, edge AI, and resource-efficient implementations• Multimodal, cross-domain, and self-supervised pattern analysis• Novel datasets, benchmarks, and evaluation protocolsWe invite Original Research articles, Review papers, Methods papers, and Perspective contributions. Submissions should clearly articulate their methodological novelty, experimental validation, and relevance to the development of reliable, interpretable, and high-performance AI-driven image analysis systems. Topic editors: Sandra Jardim, Otilia Elena Dragomir, Ana Lopes Source: https://www.frontiersin.org/research-topics/79179/artificial-intelligence-in-next-generation-pattern-recognition-and-image-analysis