A Bibliometric Analysis of Quantum-Enhanced Neural Network Applications in Rare Disease Diagnosis

Rare diseases encompass over 7,000 conditions and affect an estimated 350-400 million individuals worldwide. Despite their prevalence, research remains limited. This study presents a bibliometric analysis of Quantum-Enhanced Neural Network (QENN)-based models for rare disease prediction. A dataset of 516 peer-reviewed articles retrieved from the Scopus database was analysed to map publication trends, citation networks, co-Authorship linkages, keyword co-occurrence, and country-level contributions from inception to 2025. Results indicate a marked increase in publications over the past five years, driven by advances in quantum computing, bioinformatics, and artificial intelligence. Key research clusters were identified in quantum machine learning, biomedical prediction, and rare disease diagnostics. The findings provide a quantitative overview of the intellectual structure of this emerging interdisciplinary domain and highlight future directions for developing robust quantum-enhanced models in healthcare analytics.