ArticleNPJ digital medicine2025
A systematic review of quantum machine learning for digital health.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- AI In Leukemia Diagnostics: Complementing the Pathologist's Role.International journal of laboratory hematology · 2026Review
- H-QDCT: hierarchical quantum DCT for structural-textural feature fusion in medical imaging.International journal of computer assisted radiology and surgery · 2026Article
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- Systems-Level Support for Hybrid Quantum-Classical Learning: A Systematic Review with a Medical Imaging Translation Lens.Journal of imaging · 2026Review
- Quantum Machine Learning for Biomedical Classification Problems: A Feasibility Study on Real Quantum Hardware.Annals of biomedical engineering · 2026Article
- Prostate Cancer Classification Using Quantum Machine Learning on Multi-parametric MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Quantum-machine-assisted drug discovery.npj drug discovery · 2026Review
- PerioTwin-DeltaCRED: a validation-aware physiological digital twin for longitudinal periodontal treatment response prediction.Frontiers in physiology · 2026Article
- Quantum medicine: A quantum-mechanical framework for redox biology, disease and precision medicine.Clinical and translational medicine · 2026Review
- Quantum Cardiovascular Medicine: From Hype to Hope-A Critical Review of Real-World Applications.Journal of clinical medicine · 2025Review
- Integrative systems biology approaches for analyzing microbiome dysbiosis and species interactions.Briefings in bioinformatics · 2025Review
- How quantum computing can enhance biomarker discovery.Patterns (New York, N.Y.) · 2025Review
- A systematic review of quantum machine learning for digital health.NPJ digital medicine · 2025Article
- Population health management through human phenotype ontology with policy for ecosystem improvement.Frontiers in artificial intelligence · 2025Article
- Coalition of explainable artificial intelligence and quantum computing in precision medicine.Computational and structural biotechnology journal · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The growth in digitization of health data provides opportunities for using algorithmic techniques for data analysis. This systematic review assesses whether quantum machine learning (QML) algorithms outperform existing classical methods for clinical decisioning or health service delivery. Included studies use electronic health/medical records, or reasonable proxy data, and QML algorithms designed for quantum computing hardware. Databases PubMed, Embase, IEEE, Scopus, and preprint server arXiv were searched for studies dated 01/01/2015-10/06/2024. Of an initial 4915 studies, 169 were eligible, with 123 then excluded for insufficient rigor. Only 16 studies consider realistic operating conditions involving quantum hardware or noisy simulations. We find nearly all encountered quantum models form a subset of general QML structures. Scalability of data encoding is partly addressed but requires restrictive hardware assumptions. Overall, performance differentials between quantum and classical algorithms show no consistent trend to support empirical quantum utility in digital health.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.