Evidence map›Paper›PMID 42835317›Full record

ReviewFrontiers in digital health2026

From 5G smart hospitals to 6G-enabled learning hospitals: a governance-oriented framework for healthcare technology, policy, and responsible deployment.

Guiyang Zhou, Lisha Wu

Abstract readReview
In one paragraph

Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Guiyang ZhouDepartment of Medical Education Management, Shenzhen Bao'an Authentic TCM Therapy Hospital, Shenzhen, Guangdong, China.
Lisha WuGeneral Administration Office, Longgang Central Hospital of Shenzhen, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Discussion of 6G in healthcare often moves directly from anticipated network capabilities to proposed clinical applications, although application-level evidence, current connectivity alternatives, and 6G-specific evidence differ substantially in maturity. This narrative review develops an evidence-calibrated, governance-oriented framework for prospective 6G-enabled learning hospitals and examines when future 6G may add meaningful value beyond existing infrastructure. Methods: A multidisciplinary narrative synthesis covered smart hospitals, learning health systems, 5G/6G communication, IoMT, edge-cloud computing, AI/XAI, robotics, digital twins, cybersecurity, interoperability, implementation, and digital-health governance. Structured searches of PubMed, Web of Science, IEEE Xplore, and Google Scholar covered 1 January 2010-31 January 2026, supplemented by relevant standards, regulatory and policy sources, and targeted recent studies. Evidence was interpreted using an author-developed Evidence Maturity Classification (EMC) spanning demonstrated implementation, clinically grounded empirical, technical/benchmark, and prospective evidence. Results: The synthesis produced a seven-layer architecture, nine use-case analyses, a capability and evidence-maturity matrix, a twelve-domain risk-governance-metric framework with decision gates, a five-phase implementation roadmap, and an executive checklist. Across use cases, evidence for the underlying healthcare application was more mature than evidence for incremental benefit specifically attributable to 6G. Existing wired networks, Wi-Fi, private cellular systems, 5G/5G-Advanced, and edge-cloud infrastructure already support many monitoring, telemedicine, imaging, home-care, logistics, maintenance, and selected teleoperation functions. Future 6G is therefore most relevant when meaningful residual capability gaps remain, particularly combinations of mobility continuity, predictable end-to-end service, dense device coordination, multimodal synchronization, integrated sensing or positioning, and communication-computing orchestration. Conclusions: 6G adoption should follow demonstration of clinical or operational value, a residual capability gap, and governance readiness. Hospitals should use the least complex infrastructure that meets requirements, generate evidence through risk-proportionate pilots, and scale advanced connectivity only when incremental value is demonstrated against optimized current alternatives. The framework provides a structured basis for these decisions but requires prospective validation across diverse hospital settings.

Indexed as

6G-enabled learning hospitalsdigital health governanceedge-cloud computingevidence maturityexplainable artificial intelligenceinternet of medical thingsresponsible deploymentsmart hospitals

Identifiers

PMID42835317
PMCPMC13635659

What OpenQuestion holds

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Registered trials

None linked

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.