Abstract
<title>Abstract</title> <p>Background: This systematic review evaluates the impact of ambient-AI-enabled and structured-by-design clinical documentation technologies on predictive modeling in healthcare. Building onthe expansion of electronic health records, the review synthesizes evidence from 52 studies span-ning rule-based templates, large language model (LLM) generated notes, and advanced ambient-AIdigital scribes. Structured documentation, whether generated via traditional rule-based systems,AI-driven pipelines, or ambient scribing, consistently enhances data completeness, semantic rich-ness, and uniformity, thereby improving downstream model performance for outcomes such asreadmission risk, mortality prediction, and diagnosis accuracy. Objective: To evaluate how structured documentation technologies influence data complete-ness, semantic richness, and the predictive performance of machine learning models across clinicaltasks. Methods: A systematic review of five major databases (PubMed, IEEE Xplore, Scopus, Webof Science, and Embase; 2010–2025) identified 52 relevant studies that evaluated documentationtools used as data sources for predictive modeling. Eligible articles reported quantifiable effectson model metrics, including accuracy, calibration, and reliability, for outcomes such as mortality,readmission, diagnosis prediction, and clinical risk assessment. Results: Structured documentation approaches consistently improved both data quality andmodel performance. Rule-based templates produced moderate gains in predictive accuracy (AU-ROC increases of roughly +0.03 to +0.06), whereas LLM-based narratives and multimodal doc-umentation achieved larger improvements by enriching semantic content. Ambient-AI systemsenhanced data completeness and workflow efficiency, though relatively few studies explored cali-bration, bias mitigation, or external validation. Conclusion: Structured clinical documentation operates as an upstream data-engineering mech-anism that can strengthen the reliability of machine learning models. Despite encouraging trends,key evidence gaps persist, particularly regarding external validation, fairness assessment, and er-ror propagation from AI-generated content. Broader standardization and prospective evaluation are2essential as ambient-AI documentation tools become more widespread in healthcare environments.</p>