Decision-oriented explainable artificial intelligence: a PDR-based review of methods, applications, and emerging frontiers
Explainable artificial intelligence (XAI) is increasingly used in consequential decisions, but method selection still requires evidence about predictive reliability, explanatory fidelity, and stakeholder usefulness. This latent Dirichlet allocation (LDA)-assisted structured review used the OpenAlex Works metadata application programming interface (API) to retrieve 4,200 English-language records published from 2018 to 13 July 2026. Sequential DOI, exact-title, and fuzzy-title deduplication retained 3,166 records; title, abstract, and keyword screening retained 694; eligibility assessment retained 666; and 666 records entered the final document-term matrix. Candidate LDA models with k = 5 − 16 were compared using c_v and u_mass coherence, conventional perplexity, matched-topic stability, and Jensen–Shannon separation. The selected k = 8 solution achieved c_v = 0.5226, stability = 0.6809, and mean topic separation = 0.5824. Manual inspection of the top terms and documents identified themes concerning robust counterfactual generation, responsible and regulated decision support, interpretable clinical risk prediction, industrial, cybersecurity, and infrastructure XAI, causal and actionable algorithmic recourse, visual and multimodal medical explanation, clinical XAI adoption, trust, and workflow, and human-centered XAI evaluation and taxonomy. These evidence-derived themes are integrated with the adopted Predictive-Descriptive-Relevance (PDR) framework as a decision-oriented synthesis lens. The review contributes a reproducible literature map, a critical method comparison, a stakeholder-centered cross-domain matrix, and a research roadmap for causal, robust, uncertainty-aware, multimodal, generative, foundation-model XAI.