<p>Artificial intelligence is reshaping hospital care delivery through federated learning pipelines, edge-cloud inference architectures, and AI-driven clinical decision support. Yet the translation of these AI capabilities into patient-centred, institutionally governable, and humanised hospital systems remains fragmented across the literature. This paper addresses that gap through a PRISMA-compliant systematic evidence synthesis of 116 included studies and reports (inter-rater reliability <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\kappa = 0.884\)</EquationSource></InlineEquation>, 95 % confidence interval (CI): 0.835−0.933), and is careful to separate its empirical findings from its conceptual contribution. Empirically, we synthesise evidence at the level of three individual AI-enabled pillars: personalisation (AI-driven genomic pipelines, AI-assisted diagnostics using convolutional neural network (CNN) and transformer architectures, continuous physiological monitoring, and longitudinal predictive models); cloudisation (multi-platform cloud architectures evaluated via a weighted multi-criteria decision analysis (MCDA), Fast Healthcare Interoperability Resources (FHIR)-native data lakes, federated AI health monitoring, and data lineage, provenance auditing, and role-based access control); and humanisation (empathy-driven interfaces, co-design evaluation frameworks, digital therapeutics regulation, and operational ethical AI governance with enforceable accountability). Conceptually, we propose that these pillars are co-constitutive and, under an Industry 5.0 governance frame, jointly define the Hospital-of-the-Future; we advance this integrated tri-pillar framework as an analytical proposition rather than an empirically validated system. The pillar-level evidence is substantial: meta-analytic standardised mean differences (SMDs) of 0.48−0.64 for personalisation outcomes, 82.4 % sensitivity and 91.1 % specificity for AI-integrated sepsis screening (<InlineEquation ID="IEq2"><EquationSource Format="TEX">\(n = 5{,}765\)</EquationSource></InlineEquation>), and a 0.7-day length-of-stay reduction (<InlineEquation ID="IEq3"><EquationSource Format="TEX">\(p = 0.031\)</EquationSource></InlineEquation>) for humanised ward design. The evidence base is, however, fragmented across the pillars: no included study evaluated the three pillars jointly, and although individual technology domains span Technology Readiness Levels (TRL) 3–8, the integrated framework itself remains at TRL 3–4. A cluster randomised trial protocol is specified to generate the missing integration evidence and to advance the framework toward TRL 6–7. The review therefore offers a promising, evidence-informed conceptual architecture and a testable research agenda for AI-enabled hospitals that are scalable, governable, and human-centred, while making explicit the gap between the currently fragmented evidence and the integrated vision it proposes.</p>

AI-governed hospitals-of-the-future under industry 5.0: intelligent personalisation, cloud-integrated AI, and human-centred governance

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2026年7月11日

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Amr Adel,
Mohammad Al-Rawi,
Anna Shillabeer,
Patrick Shearman,
Rouwa Yalda,
Tony Jan,
Asmaa Soliman Al-Moisher,
Mohammad Ali Moni

摘要

Artificial intelligence is reshaping hospital care delivery through federated learning pipelines, edge-cloud inference architectures, and AI-driven clinical decision support. Yet the translation of these AI capabilities into patient-centred, institutionally governable, and humanised hospital systems remains fragmented across the literature. This paper addresses that gap through a PRISMA-compliant systematic evidence synthesis of 116 included studies and reports (inter-rater reliability \(\kappa = 0.884\), 95 % confidence interval (CI): 0.835−0.933), and is careful to separate its empirical findings from its conceptual contribution. Empirically, we synthesise evidence at the level of three individual AI-enabled pillars: personalisation (AI-driven genomic pipelines, AI-assisted diagnostics using convolutional neural network (CNN) and transformer architectures, continuous physiological monitoring, and longitudinal predictive models); cloudisation (multi-platform cloud architectures evaluated via a weighted multi-criteria decision analysis (MCDA), Fast Healthcare Interoperability Resources (FHIR)-native data lakes, federated AI health monitoring, and data lineage, provenance auditing, and role-based access control); and humanisation (empathy-driven interfaces, co-design evaluation frameworks, digital therapeutics regulation, and operational ethical AI governance with enforceable accountability). Conceptually, we propose that these pillars are co-constitutive and, under an Industry 5.0 governance frame, jointly define the Hospital-of-the-Future; we advance this integrated tri-pillar framework as an analytical proposition rather than an empirically validated system. The pillar-level evidence is substantial: meta-analytic standardised mean differences (SMDs) of 0.48−0.64 for personalisation outcomes, 82.4 % sensitivity and 91.1 % specificity for AI-integrated sepsis screening (\(n = 5{,}765\)), and a 0.7-day length-of-stay reduction (\(p = 0.031\)) for humanised ward design. The evidence base is, however, fragmented across the pillars: no included study evaluated the three pillars jointly, and although individual technology domains span Technology Readiness Levels (TRL) 3–8, the integrated framework itself remains at TRL 3–4. A cluster randomised trial protocol is specified to generate the missing integration evidence and to advance the framework toward TRL 6–7. The review therefore offers a promising, evidence-informed conceptual architecture and a testable research agenda for AI-enabled hospitals that are scalable, governable, and human-centred, while making explicit the gap between the currently fragmented evidence and the integrated vision it proposes.