Analysis of implementation materials, conducted through the process-tracing approach described above, identified recurrent operational tensions arising during the deployment of clinical AI systems. These tensions were primarily associated with infrastructure integration, regulatory alignment, and the embedding of AI outputs within routine clinical workflows. Recurrent solutions to these implementation tensions were grouped into 6 interdependent modules. These modules represent governance conditions observed in practice rather than a prescriptive institutional design.

The 6 modules collectively constitute a governance cycle through which clinical AI systems transition from experimental deployment to stabilized institutional use. The first two modules, institutional carrier formation and infrastructure governance, represent upstream enabling conditions that establish organizational ownership and sustain technical continuity prior to clinical activation. Regulatory and ethical governance defines authorization boundaries and distributes accountability across the deployment lifecycle. The remaining modules, interdisciplinary operational coordination, translational scaling, and lifecycle evaluation and oversight, support active deployment by enabling cross-departmental embedding, contextual expansion, and adaptive governance as clinical and regulatory conditions evolve. Although analytically presented as 6 modules, the framework operates as a recursive governance cycle in which implementation generates feedback that informs subsequent governance adjustments and institutional learning. The governance framework is illustrated in Figure 2.

The framework comprises 6 interdependent modules representing the organizational, technical, regulatory, and clinical functions required to stabilize AI systems as routinized components of clinical practice. Each module reflects a recurring governance tension identified during implementation and is supported by empirical process evidence drawn from the Hebei provincial clinical AI platform. Table 1 summarizes the modules, their corresponding implementation artifacts, and the transferable governance requirements derived from them.

Across the observation period, the platform was progressively integrated into routine clinical workflows spanning multiple service lines. Risk assessment tools were incorporated into outpatient triage processes to support early identification of high-risk patients, while decision-support functions were embedded within multidisciplinary case discussions to facilitate structured cross-departmental evaluation. AI-assisted documentation tools, including generative medical record support and natural-language clinical assistants, were concurrently introduced to support routine documentation and information retrieval. The platform also enabled structured capture of research indicators and integration of clinical data management with institutional quality-monitoring functions. To provide high-level proof-of-concept evidence that the platform progressed beyond bounded pilot deployment, selected operational indicators are summarized in Table 2.

During the observation period, the clinical AI platform was progressively integrated into routine clinical workflows across multiple service lines. Operational deployment was organized around 3 clinical pathways, each representing a domain in which AI-assisted functions were embedded within routine clinical decision processes and institutional coordination mechanisms.

The intelligent preconsultation pathway deployed a conversational interface based on a hospital-developed and locally deployed large language model–enabled system, rather than a commercial vendor platform, to elicit structured symptom histories from patients prior to clinical encounters. The pathway generated summarized risk assessments that were automatically transmitted to the electronic medical record (EMR) system, thereby providing preliminary clinical information to support outpatient triage and physician assessment at the point of care.

The oncology multidisciplinary decision-support pathway embedded AI-assisted treatment recommendations within tumor board workflows. It was supported by a hospital-developed oncology decision-support model based on a transformer architecture and adapted for oncology-specific text generation and clinical recommendation tasks. Model-generated outputs were reviewed alongside imaging findings, pathology reports, and patient-specific clinical data under defined specialist decision authority, with final treatment determinations remaining within established clinical accountability structures.

The TDM pathway applied an in-house machine learning pipeline that integrated principal component analysis for feature optimization and a transformer-based deep learning regression model to generate plasma drug concentration estimates for duloxetine and support individualized dosing decisions. These estimates were derived from patient-specific clinical parameters and incorporated into routine medication-management workflows to inform dosing adjustments.

In addition to these clinical pathways, AI-assisted documentation tools were deployed to support routine information management, including generative medical record functions and natural-language clinical assistants facilitating clinical documentation and information retrieval. The platform also enabled structured capture of research indicators and integration of clinical data management with institutional quality-monitoring and downstream analytical processes. Sustained operation across these domains created an empirical context in which AI-enabled systems engaged directly with institutional governance arrangements and cross-departmental coordination structures. This provided the empirical basis for examining how governance mechanisms emerged and evolved during real-world implementation.

Three forms of implementation evidence were observable during the review period, together indicating that the platform had moved beyond bounded pilot deployment. Sustained operation was evident across 3 clinical pathways, with the preconsultation pathway remaining in routine use for more than 12 months and recording more than 24,000 completed patient interactions across 5 outpatient departments. At the same time, AI outputs were incorporated into existing clinical workflows rather than remaining external demonstrations, including automatic transmission of preconsultation summaries to the EMR system, integration of oncology decision support into MDT deliberation, and incorporation of duloxetine concentration prediction into medication-management processes. Continuity of deployment was also accompanied by formal governance consolidation: regulatory authorization was documented in the algorithm filing record, role-based operational oversight was specified in pathway supervision protocols, update procedures were recorded in version-control logs, and organizational ownership was formalized through the medical AI laboratory mandate. These observations collectively provide high-level implementation evidence that the platform functioned as an institutionalized operational capability rather than remaining a time-limited pilot initiative.

A foundational governance challenge in clinical AI deployment concerns the absence of a stable organizational locus of ownership. Under such conditions, the institutional authority required to coordinate development, deployment, and lifecycle oversight remains fragmented, preventing AI capabilities from transitioning beyond pilot implementation into durable institutional use.

When AI initiatives operate as project-based activities, accountability for cross-departmental data access, clinical validation, and regulatory engagement becomes diffusely distributed across organizational units and external collaborators. In the absence of a designated institutional carrier, technically functional systems frequently fail to persist beyond initial pilot phases because no organizational entity possesses the authority required to align technical development with clinical governance and institutional decision structures.

Governance consolidation in the studied setting occurred through the establishment of a dedicated medical AI laboratory serving as an institutional carrier with a formal mandate and defined decision rights. The laboratory integrated algorithm development, clinical research, and application translation within a unified organizational structure, thereby concentrating responsibility for AI system oversight within a single accountable entity. This carrier was established prior to broader clinical integration and subsequently provided the administrative foundation upon which additional governance arrangements were constructed, including designation as a provincial engineering research center.

Institutional arrangements may vary across health systems in organizational form and administrative configuration. A consistent governance principle nevertheless emerges, whereby sustained clinical AI integration requires a durably designated organizational entity responsible for coordinating development, deployment, and lifecycle oversight. Such carriers may take the form of dedicated laboratories, formally chartered governance committees, or federated institutional arrangements, but must remain structurally distinct from temporary project teams and independent of short-term funding cycles.

A second governance challenge arises from the treatment of data and computational resources as project-bound technical assets rather than as governed institutional infrastructure. When infrastructure provision remains embedded within temporary project arrangements, the standardization, interoperability, and operational continuity required for routine clinical deployment cannot be reliably sustained. Under such conditions, AI systems remain dependent on configurations that are structurally incompatible with long-term institutional operation.

Fragmented information systems, inconsistent data standards, and reliance on temporary computing environments constrain stable model operation and impede integration into clinical workflows. These limitations typically arise not from data scarcity but from the absence of institutional governance mechanisms capable of ensuring infrastructure continuity, access control, and system-wide coordination across heterogeneous hospital environments. Without formal governance of underlying infrastructure, technically validated AI systems may nevertheless fail to operate reliably at the point of clinical deployment, creating conditions in which deployment continuity depends on informal negotiation rather than defined accountability structures.

Infrastructure governance within the studied platform was progressively incorporated into institutional oversight arrangements. High-performance computing capacity was established and integrated with existing clinical information systems, while external certifications, including EMR functional maturity assessment and interoperability standardization evaluation, confirmed that infrastructure conditions satisfied requirements for clinical deployment. Data standardization across legacy systems further required iterative coordination among clinical, technical, and administrative units, as nonstandardized terminologies embedded within departmental workflows impeded the establishment of unified data pipelines.

Infrastructure architectures differ across institutions in scale, technical configuration, and organizational management. A consistent governance principle nevertheless emerges, whereby data pipelines, computational resources, and interoperability systems must be governed as durable institutional infrastructure, maintained under defined accountability arrangements and independent of the project-bound funding cycles that characterize early-stage AI deployment.

A further governance challenge concerns the specification of regulatory authorization and accountability prior to clinical deployment. When regulatory alignment is deferred until after deployment decisions are made, accountability arrangements remain incompletely defined during the period of greatest institutional uncertainty, and the boundaries of legitimate system use are established retrospectively rather than by design.

In the absence of prior regulatory specification, system purpose, intended use boundaries, and designated institutional responsibility may remain insufficiently defined at the point of clinical activation. Effective governance therefore requires that regulatory authorization and accountability structures be established prior to deployment, particularly in regulatory environments that continue to evolve alongside emerging clinical AI capabilities.

Within the Hebei provincial platform, regulatory authorization was obtained prior to broader clinical activation of the oncology decision-support pathway through national algorithm filing procedures administered under the Cyberspace Administration of China framework. The filing process clarified system purpose, defined intended use boundaries, and formally designated institutional responsibility for ongoing deployment. Regulatory requirements therefore informed operational design decisions at the predeployment stage rather than constraining systems already embedded in clinical practice, and oversight functions were incorporated within existing institutional governance arrangements rather than constituted as a separate compliance structure.

Regulatory frameworks vary substantially across jurisdictions in scope, procedural requirements, and institutional applicability. A consistent governance principle nevertheless emerges, whereby accountability specification and the definition of authorized use boundaries should precede clinical activation. When regulatory alignment occurs only after deployment, governance uncertainty persists during implementation and institutional oversight across the system lifecycle becomes structurally constrained.

Effective clinical deployment of AI systems depends on structured coordination across clinical, technical, and administrative domains. When such coordination is not institutionally specified, technically functional systems may operate without agreed procedures for clinical use, and accountability for AI-generated outputs may remain diffuse or unassigned.

Ambiguity frequently emerges at the interface between algorithm development and clinical decision-making. Technical personnel may not possess the authority to modify systems in response to operational feedback, while clinicians may lack formally defined procedures for incorporating AI outputs into diagnostic or therapeutic workflows. Without governance arrangements that connect these domains, AI systems may remain technically operational but institutionally unstable.

Operational coordination in the studied implementation was institutionalized through explicit role allocation across a 3-division laboratory structure responsible for algorithm development, clinical research, and application translation. In the oncology multidisciplinary decision-support pathway, AI outputs were embedded within established team workflows under defined decision rights. Model-generated outputs were reviewed during structured multidisciplinary meetings alongside imaging findings, pathology reports, and patient-specific clinical data, while final treatment decisions remained within established clinical accountability structures. Early deployment further revealed that responsibility boundaries required clarification, and one pathway experienced temporary suspension pending resolution of ambiguity regarding authority over model update decisions.

Institutional coordination mechanisms differ across health systems in organizational design and governance structure. A consistent governance principle nevertheless emerges, whereby sustained clinical deployment requires clearly defined roles, decision rights, and accountability structures linking algorithm development, clinical use, and institutional oversight.

Durable institutional integration of clinical AI systems requires governance mechanisms capable of extending operational capacity beyond localized implementations. Without such mechanisms, systems demonstrating reliable performance within circumscribed clinical contexts may remain confined to isolated deployments, preventing broader organizational adoption and durable integration at scale.

Localized operational success does not automatically generate the institutional authority required for expansion across departments or clinical service lines. Effective scaling therefore depends on governance arrangements that connect deployment experience with institutional decision structures, enabling operational practices developed in one context to be translated into formally recognized organizational capabilities.

In the studied implementation, scaling occurred through progressive integration of AI-supported pathways into institutional governance structures rather than replication of isolated applications. The preconsultation risk assessment pathway was extended across multiple outpatient departments through standardized workflow integration, enabling its routine use across distinct clinical service lines. Additional AI-supported pathways were subsequently introduced in coordination with clinical and administrative units, each accompanied by governance documentation specifying operational roles, workflow procedures, and system use parameters. Over time, these expansions transformed initially localized tools into institutionally coordinated clinical services embedded within routine care processes. Institutional recognition further reinforced this process through research center designation and the establishment of a provincial clinical AI research institute.

Scaling mechanisms vary across institutional contexts in governance structure and policy environment. A consistent governance principle nevertheless emerges, whereby durable expansion of clinical AI capacity requires governance pathways linking localized deployment with institution-wide authority structures, allowing operational experience to translate into formally recognized institutional capability.

Long-term clinical deployment of AI systems requires governance mechanisms capable of monitoring performance, managing system updates, and adapting deployment conditions over time. Without such mechanisms, operational systems may gradually diverge from their intended use conditions as clinical workflows change, data distributions shift, and regulatory expectations develop over time.

In the absence of structured lifecycle oversight, responsibility for monitoring system performance may become diffuse across organizational units. Version control may be inconsistently documented, and operational feedback generated by clinical users may not systematically reach those with authority to modify deployed systems. This configuration introduces the risk of operational drift as AI systems expand beyond initial pilot environments into routine clinical practice, underscoring the necessity of governance mechanisms capable of maintaining accountability across the full system lifecycle.

Within the observed implementation, lifecycle oversight practices were incorporated into existing institutional governance arrangements rather than established as a fully separate governance protocol. Monitoring activities included documentation of system use boundaries, clarification of responsible units for pathway supervision, and iterative adjustments in response to operational feedback. System updates were conducted within established institutional decision structures and aligned with regulatory requirements. Standardized version-control documentation remained under iterative development during the observation period as institutional governance procedures matured.

Lifecycle governance arrangements vary across institutional contexts in their degree of formalization and organizational structure. Across these variations, a consistent governance principle applies, whereby sustained clinical deployment requires monitoring mechanisms linked to defined institutional authority, ensuring that system updates, clinical workflow adjustments, and evolving regulatory expectations remain subject to accountable oversight throughout the operational lifecycle.

Persistent pilot dependence in clinical AI is commonly attributed to governance deficits, including the absence of accountability structures, ownership arrangements, or regulatory alignment that would enable AI systems to move from experimental deployment into routine clinical use [22,23]. While this interpretation identifies important institutional constraints, it does not fully explain why governance arrangements so often fail to emerge even when pilot implementations are technically successful. The present analysis points to a more precise formulation. In this view, the pilot trap reflects not only the absence of governance but also insufficient implementation depth for governance structures to take shape [4,24]. Systems remain project-bound not because governance was unavailable in principle, but because deployment ended before the operational tensions capable of generating governance had fully materialized. What distinguishes sustained integration from persistent pilot dependence is therefore not the prior existence of a governance framework but the capacity of implementation processes to generate governance through iterative responses to recurrent coordination demands.

Reconceptualizing the pilot trap in institutional terms carries direct implications for how it is addressed. Existing approaches often position governance as a precondition for deployment by emphasizing accountability frameworks, compliance structures, and ownership arrangements before clinical integration proceeds [25,26]. Evidence from this case indicates a different dynamic. Governance capacity did not precede implementation; it emerged through it. Data-standardization conflicts, responsibility-boundary disputes, and scaling misalignments created the institutional conditions under which governance structures became necessary and were progressively consolidated [27]. Deployment therefore proceeded not from a complete governance architecture but from a minimal institutional foundation that accumulated governance capacity as implementation deepened. In contrast to frameworks that emphasize technical integration requirements or clinician adoption barriers as the primary constraints on clinical AI sustainability, the present analysis foregrounds the governance formation process itself. Attention is directed toward the mechanisms through which organizational ownership, accountability boundaries, and coordination structures become stabilized as durable institutional capacity. Across the implementation trajectory, 6 governance modules repeatedly emerged as functional conditions necessary for sustained deployment rather than as elements of a predefined governance design. Considered together, these modules constitute governance capacity as a system-level capability through which sociotechnical resources are consolidated into routinized clinical infrastructure. The operational indicators summarized in Table 2 provide empirical grounding for this interpretation and suggest that governance formation was accompanied by sustained operational embedding rather than remaining a purely organizational or aspirational construct.

These indicators are not reported as performance benchmarks but as governance-relevant evidence. Sustained use across more than 12 months of continuous operation, encompassing more than 24,000 patient interactions within a phased multidepartment deployment, indicates that governance arrangements were sufficient to support accountable clinical operation over time rather than confining the platform to a bounded pilot episode. Operational continuity across the preconsultation pathway is consistent with the stabilization of M1 and M2, without which system continuity would be more likely to depend on informal coordination than on defined accountability structures. Regulatory authorization obtained prior to clinical activation of the oncology decision-support pathway is consistent with the functioning of M3, as documented in the algorithm filing record and related compliance materials. The maintenance of defined clinical decision authority throughout the oncology MDT pathway is consistent with the operationalization of M4. The phased expansion of the preconsultation pathway across multiple outpatient departments is indicative of M5, as documented in deployment materials and departmental rollout records. Iterative system updates conducted within institutional governance structures during the observation period are indicative of M6 functioning as an active governance mechanism rather than a nominal policy commitment. Although governance functions vary in their observability through use metrics, each of the 6 modules is linked to at least one category of traceable institutional artifact, including operational records, regulatory filings, deployment materials, and organizational documents. This supports the descriptive reproducibility of the framework as an analytical lens, whereas its broader transferability across heterogeneous health system contexts is discussed in the following section. Collectively, these indicators suggest that the governance modules described in this framework were associated with observable operational continuity and that the transition from pilot deployment to institutionalized clinical practice was sustained across multiple service lines and governance domains.

Implementation of the platform was accompanied by several operational frictions that required institutional responses, through which governance structures gradually emerged [24,28]. Rather than resulting from deliberate institutional design, governance arrangements developed through iterative responses to practical operational challenges. The analysis identifies 3 generative mechanisms that shaped this process: authorization, responsibility allocation, and coordination.

Authorization translated regulatory expectations into operationally defined deployment boundaries and clarified the legitimate scope and conditions under which clinical AI systems could be used [25,29]. Responsibility allocation embedded AI-related tasks within existing organizational structures by assigning accountable ownership across institutional units and professional roles [30]. Coordination across clinical, technical, and administrative domains was gradually stabilized as decision rights, communication channels, and operational interfaces became more clearly defined [31].

These mechanisms emerged through concrete operational friction rather than prior institutional design. During early deployment of the TDM pathway, inconsistencies in laboratory data formatting and record structures across hospital information systems temporarily limited automated data ingestion and required manual reconciliation. Resolving this tension required sustained coordination among clinical departments, the hospital information center, and the AI development team, ultimately producing standardized data interfaces and validation procedures, which were later incorporated into the governance framework.

A second coordination tension arose during deployment of the oncology MDT decision-support pathway. Clinician uncertainty regarding the appropriate role of AI-generated recommendations within established multidisciplinary decision-making processes led to a temporary operational suspension. Following administrative review, responsibility boundaries and documentation requirements were clarified, and the governance framework formalized AI outputs as advisory inputs under defined clinician oversight with role-based monitoring arrangements.

These episodes illustrate that institutionalization does not occur through seamless technological integration but through the iterative resolution of organizational tensions [27,32]. Governance neither simply precedes implementation nor follows it as a downstream regulatory layer. Instead, governance is produced through the implementation process itself. Institutionalization emerges when governance arrangements stabilize in response to recurrent operational demands, transforming iterative problem-solving into durable institutional capacity embedded within routine organizational practice.

Effective clinical AI deployment requires governance fit rather than governance expansion. Governance-first approaches are sometimes criticized for introducing procedural rigidity that slows technological innovation. This analysis addresses that concern by distinguishing governance functions from the institutional forms through which they are implemented. This distinction also clarifies why the framework is analytically reproducible across settings even when organizational forms differ.

Certain governance functions constitute nonnegotiable conditions for safe and accountable integration. These include clearly defined authorization boundaries, accountable ownership, and mechanisms for lifecycle oversight. The institutional arrangements through which such functions are enacted, however, may vary substantially in scale, procedural intensity, and degree of centralization across health system contexts. Accordingly, reproducibility in this context lies not in replicating identical organizational structures but in assessing whether comparable governance functions can be identified through traceable institutional evidence.

Governance fit refers to the calibrated alignment between governance intensity and system characteristics such as clinical risk, update frequency, and operational complexity [25,33]. Under this perspective, governance architectures should not impose uniform procedural requirements across all deployment environments. Instead, effective governance preserves adaptive capacity by calibrating oversight intensity according to the potential consequences and reversibility of system changes.

Several mechanisms illustrate how such calibration can be operationalized in practice. Risk-tiered update thresholds allow routine model refinements to proceed under simplified authorization procedures while reserving stricter review processes for high-impact system modifications. Predefined authorization pathways for low-risk adjustments further reduce procedural bottlenecks. Staged deployment strategies similarly enable systems to evolve within monitored operational environments before wider clinical integration [34].

Both extremes generate systemic challenges. Insufficient governance creates fragmentation and reinforces persistent pilot dependence, whereas excessive governance may introduce bureaucratic inertia that constrains technological adaptation [35,36]. Health systems can therefore balance accountability with technological agility by implementing governance arrangements that differentiate routine system evolution from high-risk modification processes while maintaining clear institutional responsibility for oversight. The broader question of how these governance functions travel across institutional environments is addressed in the following section on functional transferability.

The absence of structural patient and community involvement in the present case invites a more precise analytical observation than simply noting a gap. Existing literature on patient participation in clinical AI often conflates two distinct roles. Patients may participate as design informants who help shape decisions about system purpose, or as governance stakeholders who contribute to oversight of how deployed systems operate within clinical environments [37,38]. Most existing guidance addresses the former by emphasizing co-design principles and participatory development methodologies. By contrast, patient involvement in operational governance has received substantially less analytical attention, and the institutional conditions required to make such participation structurally meaningful remain insufficiently specified.

Participatory governance capacity is not a function of inclusion per se but of the institutional anchoring through which participatory inputs acquire an accountable pathway to governance decisions. Meaningful patient involvement in AI governance requires an organizational carrier capable of receiving, processing, and acting on participatory inputs within established responsibility structures. Without such a carrier, participation risks becoming consultative rather than consequential, producing inputs that lack a defined institutional pathway to governance decisions [39]. The sequencing observed here, therefore, reflects a structural constraint rather than a deliberate exclusion. Participatory mechanisms were introduced following initial stabilization of the accountability architecture, a sequencing that reflected the specific institutional conditions of this implementation context rather than a principled position on the optimal timing of patient involvement. Contemporary co-design frameworks and implementation science literature frequently advocate for patient involvement from the earliest stages of governance development, and parallel co-design of governance and participation structures represents a well-documented and viable alternative in other health system contexts.

As institutionalization advances and governance architecture stabilizes, participatory capacity becomes both feasible and functionally necessary. Within the proposed framework, patient and community involvement is most appropriately integrated into lifecycle governance mechanisms. Defined feedback channels, transparent communication of system use boundaries, and structured review of operational incidents allow oversight to extend beyond professional actors while preserving clear lines of institutional accountability [38,40]. This integration addresses not only the legitimacy requirements identified in the literature but also the operational question of how participatory inputs are processed within functioning governance structures. Design-focused participation frameworks rarely specify these institutional pathways, leaving the governance dimension of participation largely unresolved [37,41].

Although the framework emphasizes governance capacity rather than technological sophistication, several baseline conditions support its applicability across health system contexts [42]. These conditions represent minimal enabling environments for sustained clinical AI operation rather than structural requirements of the framework itself. Institutional authority to assign responsibility for AI oversight must be present. Basic digital infrastructure capable of supporting clinical data capture, storage, and computational processing is also required, together with regulatory or administrative mechanisms that permit AI tools to be activated within clinical workflows. Interdisciplinary coordination capacity must additionally connect clinical practice, technical development, and organizational management to sustain cross-functional collaboration. Where such conditions are only partially present, functional equivalents such as shared infrastructure platforms, external technical partnerships, or regional governance arrangements may provide alternative implementation pathways [43].

Framework transferability therefore lies in functional reproduction rather than structural replication [22]. In this paper, functional transferability is used as an analytic formulation to distinguish transferable governance functions from the setting-specific organizational forms through which they are instantiated. Institutionalization does not require identical organizational architectures but depends on the presence of governance functions capable of stabilizing clinical AI as durable institutional capacity. Six functional requirements underpin this process, including durable ownership arrangements, governed infrastructure, defined authorization boundaries, cross-functional coordination mechanisms, structured scaling pathways, and lifecycle oversight. Under this interpretation, governance transferability depends on whether equivalent institutional functions can be realized through different organizational arrangements across settings, rather than on replication of a single structural form. In this sense, reproducibility should be understood descriptively rather than structurally. What can be reproduced across settings is not an identical organizational form but an analytic approach that examines whether comparable governance functions are evidenced through traceable institutional artifacts. Table 3 illustrates how these requirements may be instantiated across heterogeneous health system contexts. It presents both the institutional arrangements observed in the study setting and illustrative analogs that may fulfill equivalent governance roles in more decentralized health systems. The purpose of this mapping is therefore to operationalize functional equivalence across contexts, rather than to prescribe identical organizational structures.

Economic feasibility represents an important consideration for broader adoption of governance-oriented implementation models [43]. The framework does not require the independent construction of large-scale computational infrastructure. The operative condition is continuity of access to governed data and computational resources rather than infrastructural ownership. In practice, this requirement may be fulfilled through shared regional platforms, cloud-based infrastructures operating under institutional data governance agreements, or staged implementation strategies focusing on high-value clinical pathways. These configurations allow institutions across resource levels to establish governance capacity without replicating the infrastructural scale characteristic of tertiary referral centers. Resource pooling, coordinated implementation, and shared accountability structures may therefore support deployment across diverse institutional configurations, including regional health networks, public-private partnerships, and externally supported platforms.

This functional interpretation situates the framework within the broader landscape of international AI governance initiatives while clarifying its distinct contribution. Existing frameworks, including ISO/IEC 42001 [44], World Health Organization guidance on health AI governance [19], and regulatory pathways developed by agencies such as the US Food and Drug Administration [45], share a common structural assumption that the organizational capacity to implement governance already exists within deploying institutions. These frameworks specify what governance should achieve, including accountability documentation, risk management structures, and compliance requirements. However, they presuppose the existence of an institutional carrier capable of activating and sustaining these arrangements [25,28]. The framework developed here addresses the prior question of how such capacity comes into existence. It identifies the conditions under which organizational ownership, coordination authority, and oversight mechanisms emerge through implementation rather than being fully established in advance [22,24]. These two levels should therefore be seen as complementary rather than competing. Normative standards define the accountability architecture within which clinical AI should operate, while the present framework explains the institutional formation process through which that architecture becomes operationally viable at the point of deployment. The framework is therefore transferable not because institutions can or should replicate the Hebei case in structural detail, but because comparable governance functions may be identified, examined, and adapted across heterogeneous implementation settings.

The framework presented in this study derives from a single institutional case observed during an early stage of governance consolidation, which necessarily constrains generalizability [46]. Embedded observation enabled detailed insight into implementation dynamics but also introduced interpretive proximity. Triangulation across traceable institutional artifacts and iterative review by coauthors with distinct clinical, administrative, and governance roles were used to mitigate this limitation. Lifecycle governance mechanisms were still evolving during the observation period, and participatory governance remained only partially developed. The scope of claims the present analysis can support is accordingly bounded by these conditions. Although the framework may be analytically transferable across settings, its empirical adequacy and practical consequences remain to be tested through comparative research.

Beyond the constraints of this single case, a more fundamental limitation lies in the current state of the field. Clinical AI governance research has generated a substantial body of normative frameworks, implementation principles, and policy guidance, yet the relationship between governance arrangements and clinical outcomes remains largely unexamined [47]. Governance frameworks are frequently justified by the assumption that they improve care quality, patient safety, and operational reliability. However, empirical investigation of this assumption remains conspicuously limited. Governance studies rarely measure clinical outcomes, and outcome studies rarely examine governance structures [47]. The result is a structural circularity in which the value of governance is asserted but seldom demonstrated empirically.

Breaking this circularity requires a specific methodological intervention, namely longitudinal comparative studies that track governance formation and clinical outcomes simultaneously, treating governance artifacts, including carrier establishment, authorization records, coordination protocols, and oversight documentation, as traceable intermediate variables in a causal pathway from implementation to clinical impact [48]. Investigations of this kind would need to span multiple institutional contexts to distinguish governance effects from institutional confounders and would require follow-up periods sufficient to capture the maturation of lifecycle governance mechanisms beyond early implementation phases. Economic analyses characterizing the cost structures of alternative governance configurations constitute an equally pressing methodological demand [49], particularly for institutions operating with resource constraints that preclude replication of tertiary-center infrastructure. Most fundamentally, the field requires an agreed methodology for attributing clinical and operational outcomes to governance arrangements rather than to technical model performance alone [14,47], a distinction that existing evaluation frameworks are currently ill-equipped to draw.

Clinical AI adoption is typically framed as a problem of technology transfer, understood as the movement of capable systems from development into clinical practice. The present analysis points toward a more fundamental reconceptualization. What health systems are required to construct is not a pathway for technology to enter institutions but the institutional capacity through which technology becomes governable, accountable, and sustained. Institutional capacity of this kind is not established in advance of implementation; it is generated through it. The operational tensions that arise during deployment are not obstacles to governance formation. They are its generative conditions. The 6-module framework developed in this study identifies the functional arrangements through which this formation process stabilizes into sustained institutional capacity. The framework thus reorients institutional strategy away from governance prespecification and toward the cultivation of implementation conditions capable of generating governance over time. Institutions that recognize governance as a product of implementation rather than its prerequisite are better positioned to support the iterative process through which clinical AI transitions from experimental artifact to organizational infrastructure. Without such capacity, technically mature systems remain perpetually promising pilots awaiting conditions that never arrive.