Quantum Machine Learning for Digital Health? A Systematic Review
Abstract
With the digitization of health data, the growth of electronic health and medical records lowers barriers for using algorithmic techniques for data analysis. While classical machine learning techniques for health data approach commercialization, there is not yet clear evidence whether quantum machine learning (QML) will provide any empirical advantage for digital health data processing. In this systematic literature review we assess whether QML algorithms have the potential to outperform existing classical methods in efficacy or efficiency. We include digital electronic health/medical records (EH/MRs) and data considered to be a reasonable proxy to EH/MRs. Eligible QML algorithms must be designed for quantum computing hardware, as opposed to quantum-inspired techniques. PubMed, Embase, IEEE, Scopus and arXiv yielded 4915 studies between 2015 to 10 June 2024. After screening 169 eligible studies, most studies contained widespread technical misconceptions about QML and we excluded 123 studies for insufficient rigor in analysis. Of the remaining 46 studies, only 16 studies consider realistic QML operating conditions, either by testing algorithms on quantum hardware, or using noisy quantum circuits when assessing QML algorithms. We find QML applications in digital health focus primarily on clinical decision support rather than health service delivery or public health. Nearly all QML models are linear quantum models, and therefore represent a subset of general quantum algorithms.…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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