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Healthcare quality improvement increasingly relies on routinely collected data to identify preventable harm, missed care opportunities, adverse outcomes, and variation in performance. Artificial intelligence predictive models may support earlier detection of quality risks and enable more proactive monitoring than retrospective audits alone. This systematic review examined artificial intelligence predictive models for healthcare quality improvement from 2017 to 2024. The review focused on patient safety events, care gaps, adverse clinical outcomes, and performance monitoring systems. A PRISMA 2020-compliant search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore for peer-reviewed English-language studies published from 2017 through 2024. Dual screening, structured data extraction, risk-of-bias assessment, and narrative synthesis were used. The evidence base showed growing use of machine learning for pressure injuries, sepsis, readmission, mortality, ICU transfer, and continuous monitoring. However, most studies remained retrospective model-development or validation studies, while fewer described deployment within formal quality improvement workflows. Technical progress in predictive modelling for quality improvement is substantial, but evidence of sustained improvement in care processes, safety outcomes, or organisational performance remains limited. Stronger prospective evaluation and clearer integration with improvement methods are needed.

Quality improvement reports distill incident narratives, safety classifications, root-cause analyses, and performance metrics into actionable learning documents. However, compiling these materials remains a manual, cognitively burdensome task that can delay organizational learning after safety events. Healthcare organizations often hold rich safety data across reporting systems, RCA documents, dashboards, and governance records. Yet these inputs are rarely transformed into standardized QI reports through a single coherent workflow. This article proposes a conversational artificial intelligence assistant that engages quality officers in a structured dialogue, retrieves relevant safety-event evidence, and generates a draft QI report following a pre-specified template. The assistant is conceptualized as a human-supervised system rather than an autonomous decision-maker. The proposed assistant includes an incident narrative NLP module, safety classification aligner, RCA note retriever, performance metric trend summarizer, and template-guided large language model. These components would support structured reporting while preserving human review and organizational accountability. The assistant could shorten the time from incident review to report drafting, improve reproducibility across QI documentation, and reduce administrative burden for patient safety teams. Its value would depend on careful grounding, privacy protection, verification workflows, and user trust. Conversational AI offers a pathway toward AI-augmented safety reporting that supports, rather than replaces, human expertise. The proposed model emphasizes structured synthesis, transparent evidence use, and a learning culture in healthcare quality improvement.