Quantum technologies in clinical medicine: a systematic cross-domain synthesis and translational readiness taxonomy

Abstract Quantum technologies — encompassing quantum sensing, quantum computing and machine learning (QML), quantum nanomaterials, and quantum cryptography — represent an emerging class of physical and computational tools with the potential to address fundamental limitations in clinical medicine across diagnostics, therapeutics, drug discovery, and health-information security. Despite a rapidly growing body of peer-reviewed evidence, estimated at a compound annual growth rate of approximately 14.8% over 2016–2025, the extant literature organises findings by technology type rather than clinical specialty, limiting accessibility for clinically-oriented readers and precluding systematic cross-domain technology maturity comparison. This review addresses that structural gap through a specialty-first synthesis of the quantum-medicine literature and the introduction of an original five-dimension Translational Readiness Taxonomy (TRT: Hardware Maturity, Validation Setting, Benchmarking Rigour, Reproducibility, Regulatory Pathway) as a replicable cross-domain maturity assessment instrument. A total of 168 peer-reviewed studies were identified through a reproducible seven-step PRISMA-aligned screening pipeline from a deduplicated corpus of 14,303 records (Scopus n = 7,081; Web of Science n = 4,644; PubMed n = 2,578), organised across five clinical domains: cardiology , neurology , oncology, genomics and drug discovery , and health data security ; 2022; Indumathi et al. 2024; Roosan et al. 2025; Freyer et al. 2025; Roosan et al. 2024; Lo et al. 2024; Singh et al. . Three original synthesising frameworks are additionally introduced: the Quantum-Medicine Translation Pipeline (QMTP), the Evidence-Maturity Bubble Matrix, and the Quantum-Medicine Ecosystem Map. Neurology leads translational readiness across hardware maturity, clinical validation, and benchmarking dimensions, driven by progressive optically-pumped-magnetometer magnetoencephalography (OPM-MEG) clinical adoption. Oncology, despite constituting 41.7% of included studies and generating the four highest-cited records, exhibits the lowest TRT composite score, with no prospective patient-recruited trials identified across its 70 included studies. A QML-framing-versus-evidence gap is confirmed in cardiology and oncology, where near-term clinical QML claims lack the computational proof-of-concept infrastructure that would logically need to precede them. Health data security leads regulatory maturity via the 2024 NIST finalisation of post-quantum cryptographic standards (CRYSTALS-Kyber/FIPS 203)

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Publication Details

Journal
Journal of King Saud University - Computer and Information Sciences
Published
2026-08-18
DOI
https://doi.org/10.1007/s44443-026-01063-0
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

Quantum technologies in clinical medicine: a systematic cross-domain synthesis and translational readiness taxonomy

Raghavendra M Devadas, Sowmya T
Journal of King Saud University - Computer and Information Sciences
Artificial Intelligence in Healthcare and Education
article

Quantum technologies in clinical medicine: a systematic cross-domain synthesis and translational readiness taxonomy

Raghavendra M Devadas, Sowmya T
article en

Abstract

Abstract Quantum technologies — encompassing quantum sensing, quantum computing and machine learning (QML), quantum nanomaterials, and quantum cryptography — represent an emerging class of physical and computational tools with the potential to address fundamental limitations in clinical medicine across diagnostics, therapeutics, drug discovery, and health-information security. Despite a rapidly growing body of peer-reviewed evidence, estimated at a compound annual growth rate of approximately 14.8% over 2016–2025, the extant literature organises findings by technology type rather than clinical specialty, limiting accessibility for clinically-oriented readers and precluding systematic cross-domain technology maturity comparison. This review addresses that structural gap through a specialty-first synthesis of the quantum-medicine literature and the introduction of an original five-dimension Translational Readiness Taxonomy (TRT: Hardware Maturity, Validation Setting, Benchmarking Rigour, Reproducibility, Regulatory Pathway) as a replicable cross-domain maturity assessment instrument. A total of 168 peer-reviewed studies were identified through a reproducible seven-step PRISMA-aligned screening pipeline from a deduplicated corpus of 14,303 records (Scopus n = 7,081; Web of Science n = 4,644; PubMed n = 2,578), organised across five clinical domains: cardiology , neurology , oncology, genomics and drug discovery , and health data security ; 2022; Indumathi et al. 2024; Roosan et al. 2025; Freyer et al. 2025; Roosan et al. 2024; Lo et al. 2024; Singh et al. . Three original synthesising frameworks are additionally introduced: the Quantum-Medicine Translation Pipeline (QMTP), the Evidence-Maturity Bubble Matrix, and the Quantum-Medicine Ecosystem Map. Neurology leads translational readiness across hardware maturity, clinical validation, and benchmarking dimensions, driven by progressive optically-pumped-magnetometer magnetoencephalography (OPM-MEG) clinical adoption. Oncology, despite constituting 41.7% of included studies and generating the four highest-cited records, exhibits the lowest TRT composite score, with no prospective patient-recruited trials identified across its 70 included studies. A QML-framing-versus-evidence gap is confirmed in cardiology and oncology, where near-term clinical QML claims lack the computational proof-of-concept infrastructure that would logically need to precede them. Health data security leads regulatory maturity via the 2024 NIST finalisation of post-quantum cryptographic standards (CRYSTALS-Kyber/FIPS 203)

Journal of King Saud University - Computer and Information SciencesVol. 38(7)
Manipal Academy of Higher Education (IN)
Openalex Percentile: Top 13%
Artificial Intelligence in Healthcare and Education
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