Purpose This study aims to critically assess the maturity and readiness of artificial intelligence (AI) applications in personalized healthcare and to identify the barriers hindering their clinical integration. Design/methodology/approach Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a comprehensive analysis of 1,290 peer-reviewed studies sourced from PubMed, Scopus, Google Scholar and IEEE Xplore, spanning the years 2019–2024. The ROBINS-I tool was utilized to evaluate bias, and following a rigorous full-text review, 15 studies met the inclusion criteria. A novel maturity model was developed to categorize the studies. Findings The vast majority of studies (13/15) are concentrated at Stage 1 (technical validation) and Stage 2 (clinical efficacy), with a complete absence of studies at Stages 4–5. None have achieved full integration into clinical workflows. We identify a critical “implementation chasm” caused by a trust deficit (black box problem), an equity crisis (algorithmic bias) and a governance gap (data privacy and regulatory challenges). Practical implications The field must shift its focus from technical performance to addressing the systemic barriers identified. We propose a maturity model to guide future research and development towards sustainable and equitable integration. Originality/value This review moves beyond cataloging benefits and challenges by introducing a maturity model and critically examining the implementation chasm. It provides a novel framework for advancing AI in personalized healthcare.
Ahmad et al. (2026) studied this question.