Quantum machine learning in diabetes and metabolism: Applications and translational challenges

Quantum machine learning is emerging as a promising extension of artificial intelligence, with potential advantages over classical approaches in handling complex biomedical data. This review aims to evaluate quantum machine learning applications in the detection, prediction, and personalized management of metabolic syndrome and type 2 diabetes mellitus.

We reviewed literature published between 1994 and 6 July 2026, with peer-reviewed journal articles and conference proceedings as the principal evidence base, complemented by selected preprints and technical sources. Quantum machine learning approaches, including quantum support vector machines, quantum neural networks, and quantum echo state networks, were classified and compared with classical counterparts across obesity and early metabolic dysregulation, diabetes diagnosis and glycemic management, and chronic complications.

Quantum machine learning and hybrid quantum-classical systems demonstrated potential benefits in small-sample and noisy environments typical of wearable and biomedical sensor data. Reported performance gains included improvements in accuracy, robustness, and scalability, though interpretability and reproducibility remain challenges. Hardware limitations associated with noisy intermediate-scale quantum devices, data encoding, and privacy considerations emerged as key barriers to clinical translation.

Preliminary studies highlight the promise of quantum machine learning for predictive and personalized management of metabolic syndrome and type 2 diabetes mellitus. However, successful clinical adoption will require robust validation pipelines, regulatory sandboxes, and harmonized compliance frameworks. Domain-specific evaluation metrics and transparent conformity assessments are essential to ensure trustworthy, scalable, and equitable deployment. A staged roadmap is proposed to bridge experimental progress with ethical and regulatory readiness.