Systematic Review of Quantum Machine Learning for Healthcare

• Matheus R. Teixeira FEI University Center
• Reinaldo A. C. Bianchi FEI University Center

DOI: https://doi.org/10.5753/eniac.2026.30337

Resumo

Quantum machine learning (QML) has been treated as a promising approach for various fields, among them biomedical and digital health. However, empirical evidence supporting its application in these fields remains limited and generally restricted to studies on simulators. Thus, a PRISMA-aligned systematic review of QML studies published between 2018 and 2025 is conducted, using a predefined PICOC framework, stepwise screening and quantitative quality assessment to evaluate run configurations, dataset characteristics, validation protocols, classical baselines and reproducibility. In essence, the review points out that QML in the healthcare sector remains promising, but is still in a preliminary methodological phase, with the evidence yet subject to the limitations of the NISQ era and the prevalence of studies conducted solely on simulators.

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