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

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 κ = 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.