Explainable Deep Learning and Transformer Models for Applied AI: A Review Across Medical, Agricultural, Industrial, and Business Domains

Abstract

Explainable deep learning and transformer models are increasingly applied to consequential decisions in healthcare, agriculture, industry, business, smart infrastructure, and human-centered AI. Their widespread adoption is motivated not by architectural novelty alone, but by the recognition that deep representations must be interpretable, auditable, and trustworthy in domains where model outputs influence patient diagnoses, crop management, infrastructure safety, organizational strategy, and accessibility. This review identifies and critically examines direct explainable AI evidence—spanning post-hoc interpretability in oncological imaging, attention-based explanation in transformer-based disease classifiers, visual XAI in lightweight agricultural transformers, and ensemble post-hoc analysis in affective computing—alongside transformer evidence across Swin, MaxViT, EfficientFormer, and hybrid vision transformer architectures. It situates these within a broader applied-AI ecosystem including conventional ML baselines, graph neural networks, Bayesian physics-guided models, privacy-preserving systems, and enterprise AI. Synthesis reveals that while explainability mechanisms have diversified across visual, attentional, post-hoc, and knowledge-structured approaches, explanation validity, the degree to which explanations faithfully represent model reasoning—is rarely formally evaluated. A ten-direction research agenda addresses this gap alongside robustness, privacy, edge deployment, human oversight, and governance-aware reporting.