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

Area of Science:

• Mapping the evolving landscape of artificial intelligence (AI) in precision medicine.
• Analyzing the convergence of AI innovations towards individualized treatment pathways.

Background:

• AI-driven precision medicine is shifting towards individualized treatment strategies using multimodal evidence.
• The rapid growth of AI in medicine has led to fragmented knowledge structures and limitations in existing bibliometric studies.
• A need exists for a cross-validated, implementation-focused knowledge map to guide AI's integration into personalized medicine.

Purpose of the Study:

• To develop a cross-validated knowledge map of AI in precision medicine.
• To clarify how AI innovations are converging on tailored treatment pathways using multimodal evidence.
• To provide a framework for prioritizing patient stratification and therapy optimization.

Main Methods:

• Established a dual-database cross-validation framework using Web of Science Core Collection (WoSCC) and Scopus (2015-2025).
• Analyzed 810 WoSCC and 999 Scopus records independently after parallel screening and metadata normalization.
• Performed science mapping including publication dynamics, collaboration networks, co-citation structures, keyword clustering, burst detection, and thematic evolution with cross-database consistency checks.

Main Results:

• Both databases revealed concordant structural patterns, with the United States and China as global hubs.
• The field evolved from feasibility and data expansion to multimodal/multi-omics integration, then to interpretability and clinical stratification.
• Current frontiers focus on biologically grounded inference, explainable AI with biological priors, and deployable decision systems integrating imaging AI, decision support, and LLM-agent paradigms.

Conclusions:

• A dual-database, cross-validated atlas demonstrates a robust trajectory for AI-enabled personalized medicine in clinical practice.
• The study links thematic evolution to deployability and interpretability, offering a transferable framework for real-world applications.
• Provides a roadmap for prioritizing patient stratification, therapy optimization, and integrated decision support systems.