Systematic literature review examines machine learning applications in congenital heart disease across 432 references

This systematic literature review evaluates the application of machine learning within the context of congenital heart disease. The authors compiled and analyzed 432 references sourced from leading journals to provide a broad overview of the topic. The review serves as a narrative synthesis rather than a primary trial or quantitative meta-analysis.

The scope of the review covers the general utilization of machine learning in this specific medical domain. The authors did not report specific primary or secondary outcomes, nor did they detail adverse events or tolerability profiles for the technologies discussed. Consequently, the review focuses on the existence and scope of the literature rather than quantitative efficacy or safety metrics.

Limitations acknowledged by the authors include the lack of reported outcomes and the absence of a defined comparator group within the synthesized references. The review does not establish causality or provide definitive practice recommendations due to the observational nature of the included literature. Funding sources and potential conflicts of interest were not reported.

Given the absence of specific outcome data and safety information, the clinical relevance remains uncertain. Clinicians should interpret these findings as a broad survey of available literature rather than evidence supporting specific interventions. Further research is needed to clarify the practical utility and safety of these technologies.