From algorithms to clinical execution: A cross-validated knowledge atlas of AI-enabled precision care (2015–2025)

Objective: Artificial intelligence (AI)-driven precision medicine is moving beyond standalone prediction toward individualized treatment strategies built on multimodal evidence. Yet the field's rapid, interdisciplinary growth has produced a fragmented knowledge structure, while most bibliometric syntheses remain single-database and therefore vulnerable to coverage bias and unstable thematic inference. A cross-validated, implementation-oriented knowledge map is needed to clarify how AI innovations are converging on tailored treatment pathways. Methods: We established a dual-database cross-validation framework using the Web of Science Core Collection (WoSCC) and Scopus (2015-2025). After parallel screening, preprocessing, and standardized metadata/keyword normalization, 810 WoSCC records and 999 Scopus records were analyzed independently. Science-mapping was performed across publication dynamics, collaboration networks, journal/reference co-citation structures, keyword clustering, burst detection, and longitudinal thematic evolution, followed by explicit cross-database consistency checks. Results: Across 1809 publications, both databases showed concordant structural patterns: the United States and China formed the central global hubs, while institution-level networks were more stable and coherent than author-level clusters, led by Harvard Medical School, Stanford University, and major Chinese universities/academies. The field evolved in stages from feasibility and data-source expansion to multimodal/multi-omics integration, then toward mechanism-informed interpretability and clinically oriented stratification. Recent frontiers converge on three axes: biologically grounded multimodal inference, explainable AI constrained by biological priors, and deployable decision systems integrating imaging AI, hybrid decision support, and emerging LLM-agent paradigms. Conclusion: This dual-database, cross-validated atlas delineates a robust trajectory toward clinically executable, AI-enabled personalized medicine. By linking thematic evolution to deployability and interpretability, it provides a transferable framework to prioritize patient stratification, therapy optimization, and workflow-integrated decision support in real-world settings.