A systematic review of quantum machine learning in medical image analysis has concluded that, despite promising early results, the current evidence is not enough for clinical readiness. The review, published in the journal Quantum Machine Intelligence, evaluated studies of quantum and hybrid quantum-classical models applied to images of different medical conditions, comparing their performance against classical approaches and cataloguing the obstacles that still stand between laboratory demonstrations and hospital use.
The work followed the SPAR-4-SLR framework, a structured protocol for systematic literature reviews. The authors searched PubMed, Elsevier, MedRxiv, arXiv, Google Scholar and Research Gate for relevant studies published between 2020 and 2026. To be included, studies had to be published in English, available in full text, and presented at recognized conferences or in reputable journals. They also had to discuss quantum machine learning, focus on medical image analysis, and provide sufficient technical detail about the QML model, algorithm or architecture. Duplicate studies, non-English publications, and personal or casual blogs were excluded.
Of 1,140 studies identified and screened, only 16 made it into the final review. Of the 1,140 studies identified and screened, only 16 were included in the final review. The methodology included analysis of model architectures, challenges, techniques, and performance comparisons between classical and quantum-based models, with further areas for improvement also explored.
The review’s central technical finding is that quantum machine learning performs well compared to its classical counterpart in detecting complex patterns even with small datasets.
Several approaches highlighted in the review also point to potential benefits for patient privacy preservation. The review frames this as a potential advantage rather than a demonstrated clinical capability, noting that the approaches remain at an early stage.
Against those positives, the authors identify persistent challenges related to hardware constraints and well-annotated data.
The review’s overall verdict is measured: while QML holds potential, the current evidence is not enough for clinical readiness. The authors position their analysis as an assessment of performance, current limitations, and the future directions needed for the advancement of the field.
No quantum machine learning system reviewed in this study is presented as ready for diagnostic use. Until hardware matures and evidence accumulates on clinically representative tasks, quantum machine learning in medical imaging remains a research program with potential, not a clinical technology.
Subject of Research: A systematic review of quantum and hybrid quantum-classical machine learning models for medical image analysis tasks
Article Title: Systematic review of quantum machine learning in medical image analysis
Article References: Amjad, U., Yasmeen, G. M., Azeemi, F., & Raza, A. (2026). Systematic review of quantum machine learning in medical image analysis. Quantum Machine Intelligence, 8(2), Article 118. https://doi.org/10.1007/s42484-026-00451-z
Image Credits: AI Generated
DOI: 10.1007/s42484-026-00451-z
Keywords: quantum machine learning, medical image analysis, systematic review, hybrid quantum-classical models, SPAR-4-SLR framework, small datasets, patient privacy, hardware constraints, annotated data, clinical readiness, Quantum Machine Intelligence, Systematic
News Source: Teresa Odom. (November 4, 2026). Systematic review finds quantum machine learning not yet ready for medical image analysis. Scienmag.
