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. 2026 Feb 19;35(10):4460–4472. doi: 10.1111/jocn.70256

Nursing Professional Organisations as Human Rights Intermediaries: Towards an Integrated Framework of Stakeholdership for Healthcare AI Governance

Jerome Visperas Cleofas 1,2,✉
PMCID: PMC13569261  PMID: 41712187

ABSTRACT

Aim

To propose a normative framework that guides nursing professional organisations to act as human rights intermediaries in the governance of artificial intelligence in healthcare.

Design

Discursive paper.

Results

The paper presents a triaxial framework that conceptualises the role of nursing professional organisations in artificial intelligence governance. The framework consists of a domain axis, which identifies key areas of engagement; a modality axis, which aligns actions with the specific functions of these organisations; and a human rights axis, which defines their role towards rights claimants and duty bearers.

Conclusion

The proposed framework provides a practical tool for nursing professional organisations to strategically plan and implement initiatives to influence the advancement and regulation of artificial intelligence. Its application can help ensure that healthcare innovation is equitable and rights‐based.

Implications for the Profession

This paper provides a blueprint for nursing leaders and policymakers to engage proactively with the ethical dimensions of artificial intelligence. It emphasises the salient roles of nursing professional organisations in advocating for the human right to health in a technologically driven healthcare landscape.

Impact

This paper addresses the gap in how the nursing profession can systematically engage with artificial intelligence governance. The main finding is a novel framework that provides a structured way for nursing professional organisations to act as human rights intermediaries. This research will have a significant impact on nursing leadership, patient advocacy groups, and policymakers involved in healthcare technology and ethics.

Patient or Public Contribution

Initial parts of this paper were presented to allied health practitioners via a webinar, providing early feedback and dialogue that informed its development.

Keywords: artificial intelligence, delivery of health care, health care governance, human rights, nursing, nursing professional organization

Key Points

  • This paper introduces an integrative framework for nursing professional organisations to systematically engage in the global discourse on artificial intelligence governance.

  • The discussion names three axes where stakeholdership of nursing professional organisations can be mapped within artificial intelligence governance ecosystems: domains, modalities, and human rights considerations.

  • It elevates conversations regarding artificial intelligence in nursing beyond practical application to a rights‐based, ethical imperative.

1. Introduction

The rapid and widespread integration of artificial intelligence (AI) systems and related technologies across various sectors has prompted a paradigmatic shift in the natural, social, and life sciences. This shift is particularly evident in healthcare settings, where AI has been integrated into clinical practice for monitoring, imaging, diagnosis, and intervention planning, as well as into organisational processes such as quality assurance and resource management. Additionally, AI supports patient empowerment by facilitating access to health information, enhancing self‐management capabilities, and improving adherence to treatment (Khosravi et al. 2024). In the nursing field, AI has innovated diverse facets of practice, such as patient assessment and classification, the development of personalised nursing care plans, the enhancement of care delivery, medical record management, and research (Ruksakulpiwat et al. 2024). Taken together, these emerging AI‐driven transformations have prompted what scholars coin as ‘algorithmic condition’ (Brzeziński et al. 2024, 1) in healthcare. As the largest professional group in the healthcare sector, nurses possess a collective voice that can shape governance structures and regulatory mechanisms guiding AI integration in medical and other health institutions.

Despite the general optimism surrounding AI in healthcare, evidence has demonstrated nurses' concerns about its associated risks, including its implications for human rights (Lora and Foran 2024). If not responsibly regulated, the deployment of AI in healthcare settings can inadvertently undermine fundamental human rights, such as the right to health, safety, privacy, non‐discrimination, and equitable access to care (Business for Social Responsibility [BSR] 2023; Siala and Wang 2022). Other issues such as biased algorithms, cybersecurity risks, the reinforcement of soical inequities, the erosion of meaningful patient‐provider interactions, professional liability concerns, and job security threats highlight the ethical complexities of AI integration (Mennella et al. 2024; Moulaei et al. 2024). By engaging with this emerging technology through a Human Rights‐Based Approach (HRBA; Patterson 2024), nurses and other healthcare providers can play a proactive role in ensuring that AI adoption upholds the human right to health, empower individual patients, and improving society at large (Türk 2023).

Guided by International Council of Nurses (ICN)'s imperative to be champions of human rights for health (ICN 2011), nurses are expected to become activated stakeholders in the healthcare AI governance ecosystem. The aim is to maximise the benefits, mitigate the risks, and ensure equitable and just distribution of quality AI‐enabled care. While nurses can enact their roles as human rights safeguards at an individual level, their potential to influence AI integration in healthcare can be further enhanced through collective action. Pertinent to this are nursing professional organisations (NPOs) that have historically represented and advocated not only nurses, but also other health professionals and their clientele. Through their performance of key roles such as advocacy and influencing policy (Chiu et al. 2020, 2023), professional and standards development (Cline et al. 2019; Weber 2011), and networking and community building (Campbell et al. 2024; dos Santos et al. 2016), NPOs can serve as influential organisational actors that can help shape both the innovation and regulation of AI adoption across health systems. In line with this, the main aim of this paper is to present a proposed integrative framework of stakeholdership specific to nursing professional organisations; it aims to elucidate how NPOs can perform their functions as a component of the multi‐stakeholder network within the healthcare AI governance ecosystem.

This discursive paper utilises abductive reasoning and rhetorical argumentation, engaging in theoretical reflections on recent literature in the distinct but intersecting fields of AI governance, nursing professional organisations, and human rights. The framework is built upon three axes, representing key ideas found within the aforementioned fields. This discursive paper unfolds in four parts. First, it presents the healthcare AI governance ecosystem and defines the domains of AI stakeholdership. Second, it discusses the functions of NPOs as stakeholders for AI in healthcare. Third, it explains human rights perspectives in AI applications in health. Finally, the paper presents an integrative normative framework for NPOs to become rights‐based stakeholders in AI governance.

2. The Healthcare AI Governance Ecosystem and Domains of Stakeholdership

Effective and ethical AI deployment in healthcare necessitates a robust governance ecosystem encompassing various actors, structures, and processes engaged in continuous, reciprocal, and dynamic interactions. This section introduces the foundational elements for the first axis of this paper's proposed framework for NPOs' AI stakeholdership: the domain of stakeholdership. First, it presents an overview of the AI governance ecosystem as conceptualised by Wirtz et al. (2022). Next, it zooms in on the stakeholder layer of the ecosystem, defining stakeholdership and describing the domains of engagement by the players in this network to ensure responsible AI integration.

2.1. An Overview of the Healthcare AI Governance Ecosystem

Recognising that AI's rapid advancements often move quicker than regulatory development (Zaidan and Ibrahim 2024), a systematic and responsive approach to governing its usage in healthcare is important. Following Wirtz et al. (2022), healthcare AI governance can be understood as the structured oversight of AI's development and application through established rules and frameworks, designed to manage its societal impact while balancing innovation with necessary control. It involves a collaborative assemblage of human, material, and institutional elements. These elements are collectively engaged in the development, deployment, and monitoring of AI tools. The overarching objective of this ecosystem is to ensure that AI‐driven healthcare initiatives foster public trust, maximise benefit, reduce harm, and are equitably distributed (Hassan et al. 2024; Ronquillo et al. 2021).

Wirtz et al. (2022) posit that AI governance can be framed as an ecosystem: a dynamic network of tangible and non‐tangible elements that cooperate to address AI's implications. This ecosystem is structured around five interconnected layers. First, the AI System Layer is the object of governance, referring to the AI application, its service, and underlying technology (e.g., chatbots, GPTs, automated assessment and diagnosis tools). Second, AI Governance Challenges Layer represent the specific issues demanding governance attention, broadly categorised into technological, informational, economic, social, ethical, and regulatory aspects. Third, the AI Multi‐Stakeholder Governance Layer positions a constellation of stakeholders (i.e., government, industry, civil society, academia, etc.) engaged in recursive processes that elucidate challenges to governance measures. Fourth, the AI Governance Mechanisms Layer, are the instruments, tools and/or pathways for implementing governance, including contractual (e.g., legal standards), relational (e.g., trust, collaboration), and technological (e.g., code‐based rules embedded in AI systems). Finally, the AI Governance Policy Layer is the resulting body of rules and remedies addressing AI challenges. These layers interact dynamically, creating a systemic approach to AI governance in healthcare.

Building on this, the American Nurses Association's (ANA 2022) position statement exemplifies how NPOs can actively participate in AI governance through the lens of the ecosystem framework (Wirtz et al. 2022). For instance, this statement addresses the AI System by providing ethical guidance for various AI tools in nursing, ensuring they augment, not replace, human judgement. The statement confronts key AI Governance Challenges like bias, privacy, transparency, and accountability for AI decisions. Through its development and dissemination, the ANA engages in the AI Multi‐Stakeholder Governance Process by advocating for nurses' essential role in AI policy formulation. Position statements such as this can be considered a Relational Governance Mechanism, which helps communicate the commitment to nurses and other stakeholders. Ultimately, this informs AI Governance Policies in health institutions, consequently influencing standards for AI integration in nursing practice.

2.2. Defining Healthcare AI Governance Stakeholdership

Nursing professional organisations function as salient agentic collectives within the multi‐stakeholder layer of the healthcare AI governance ecosystem (i.e., third of the five, and central layer of the ecosystem). NPOs are part of this network, collaborating with various groups and entities–formal and informal–involved in AI health systems. Through continuous interaction, these NPOs can shape the broader landscape of healthcare AI governance by influencing the other layers of the ecosystem (Wirtz et al. 2022). In this evolving regulatory environment, a clear and structured understanding of how NPOs express their unique stakeholdership in the AI era of healthcare is essential to harness their expertise and advocacy effectively.

In its broadest sense, stakeholdership is generally understood as the dynamic engagement of any individual, group, or organization that has the capacity to influence, or be influenced by, the activities, technologies, resources, and outcomes of other entities. This concept inherently involves joint effort, open communication, and collective decision‐making (Kujala et al. 2022). Cleofas (2026) further inscribes this concept into the discipline by defining nursing stakeholdership as the ‘actions, expressions, involvements and resource deployments of various nursing and nursing‐related agents (individual or collective) with an attempt to initiate or respond to influence(s) in a given multistakeholder network.’ In the ecosystem perspective of AI governance, stakeholdership of NPOs (i.e., professional associations, unions, and specialty organisations) is viewed at the organisational level, focusing on their collective capacity to contribute to responsible AI integration. Extending the theoretical contributions of a previous parallel work on nursing academia (Cleofas 2026), this paper frames the nature of healthcare AI governance stakeholdership as having three axes, addressing the essential questions of ‘what,’ ‘how,’ and ‘for whom + to what end’ of NPOs' engagement. An overview of the axes and their elements can be seen in Table 1.

TABLE 1.

An overview of the axes and elements of the Nursing Professional Organization (NPO) Stakeholdership in Healthcare AI Governance.

Axis of stakeholdership Definition Elements
Domain (‘what’) The substantive areas where NPOs engage in AI governance alongside other stakeholders to shape the development, use, and oversight of AI systems

  1. Framing (Identifying & Defining Challenges)

  2. Risk/Benefit Assessment (Analysing Impact)

  3. Evaluation (Assessing Governance Effectiveness)

  4. Risk Management (Mitigating Harms)
Modality (‘how’) The organisational functions and means through which NPOs perform their roles as stakeholders in AI governance

  1. Professional Development (Enhance Knowledge/Skills)

  2. Organising & Mobilisation (Unite & Act Collectively)

  3. Advocacy (Influence Policy & Public Opinion)
Human Rights Considerations (‘for whom + to what end’) The ethical orientation of NPOs' stakeholdership actions, specifying whom these actions aim to serve, and which rights they seek to protect or fulfil
For Whom
  • Rights Claimants (to empower)
  • Duty Bearers (to strengthen and hold accountable)
To What End
  • Realisation of Human Rights Principles Specific to AI‐enabled Healthcare
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3. The Domains of NPO Stakeholdership in Healthcare AI Governance

The first axis of stakeholdership is domain, which refers to the areas of engagement of NPOs with other stakeholders. Simply put, domain is the ‘what’ of stakeholdership. Following Wirtz et al. (2022), there are four stakeholdership domains: framing, risk/benefit assessment, evaluation, and risk management. In the framing domain, NPOs actively collaborate with other governance stakeholders to sense, identify, and articulate emerging AI‐related challenges specific to healthcare by providing their collective professional insights. Within the risk/benefit assessment domain, NPOs are uniquely positioned to analyse AI's ethico‐legal, social, and practical implications for nursing care, potentially through efforts like mobilising special committees or disseminating commissioned reports. In the evaluation domain, NPOs contribute perspectives based on the latest evidence and emerging professional contexts; this can assist other stakeholders in formulating and choosing governance options from a professional practice and public protection standpoint. Finally, in the risk management domain, NPOs work alongside regulators and institutions in reinforcing governance mechanisms that aim to mitigate the harms of these technologies. Across these salient domains, NPOs' collective expertise and advocacy are instrumental in fostering informed, equitable, and collaborative decision‐making with other key governance stakeholders within the evolving AI landscape in health.

4. The Modalities of Stakeholdership in Healthcare AI Governance: Functions of NPOs

The second axis of stakeholdership is modality, which refers to the general functions or means through which these organisations fulfil their roles for nurses and society at large. This axis highlights the specific actions and operations NPOs employ to influence the development, integration, and oversight of AI in healthcare. Simply put, modality is the ‘how’ of stakeholdership. Anchored on the societal roles and organisational affordances of NPOs, this axis advances three interconnected modalities of NPO stakeholdership: professional development, organising and mobilisation, and advocacy.

4.1. AI‐Related Professional Development by NPOs

Professional development stands as a salient NPO modality that serves as a mechanism for nurses to continuously enhance their clinical, educational, and managerial capabilities. NPOs, such as the Democratic Nursing Organisation of South Africa (Conco et al. 2014), emphasise professional advancement, education, and research as vital for nursing growth. Professional associations and societies employ diverse strategies such as scholarship, mentorship, and inter‐professional collaboration to support nurses in achieving higher academic degrees and career progression (Anele et al. 2022). Beyond individual career benefits, these efforts ensure the cultivation of an adaptive workforce capable of navigating evolving healthcare landscapes. NPOs also facilitate the advancement of the discipline and profession through continuous evidence production and updating of standards (Conco et al. 2014; Weber 2011). Moreover, NPOs foster professional socialisation, with membership often opening doors to networking opportunities that support career growth (Goolsby and DuBois 2017).

In the context of healthcare AI governance, professional development becomes a modality of stakeholdership of NPOs through building capacities of nurses to engage with AI systems ethically and competently. NPOs can design and deploy programs that cultivate both digital and moral literacy among nurses in an increasingly AI‐augmented work environment. For example, the American Association of Colleges of Nursing (AACN 2025) offers AI‐related webinars wherein attendees can earn continuous professional development credits and update their nursing informatics competencies. Similarly, the American Nurses Credentialing Center offers courses via Walden University (2023) to bolster AI readiness among nurse leaders. These NPO‐driven initiatives equip nurses to critically assess AI tools, understand their implications, and actively participate in shaping AI's responsible integration into practice.

4.2. NPOs' Organising and Mobilisation in Response to AI Deployment in Healthcare

Organising, mobilisation, and coalition building are fundamental functions of NPOs. Through these NPOs collectively address systemic issues and advocate for the profession's interests, as documented in prior studies (e.g., Chen et al. 2022; Riveros 2023). Organising refers to establishing structures and processes that unite nurses, while mobilisation involves galvanising members for collective action towards a cause. Coalition building extends these efforts by forging strategic alliances with other stakeholders to achieve shared goals. These functions are crucial for NPOs to create political change, both for the nurses and the patients they serve. By leveraging shared governance, NPOs and union groups can help in illuminating professional distress experienced by nurses in the workplace (Wesp et al. 2024). Coalition building further amplifies nursing's influence, as organisations form (in)formal partnerships with academic institutions, healthcare organisations, and governing/regulatory boards to advance the profession, in both local (e.g., Montemayor 2023) and international levels (e.g., Hawkins et al. 2025). Collaborations among organisations like the American Organization of Nurse Executives and the National Black Nurses Association also illustrate how unified voices can transform health systems and policy (Keepnews 2005).

As regards healthcare AI governance, organising, mobilisation, and coalition building are potent expressions of NPOs' stakeholdership, enabling them to exert collective influence over AI regulations. A powerful exemplar is the National Nurses United (NNU), which has explicitly used direct action to push for patient protections against AI. NNU organised nurses nationwide in the United States to march in support of safe staffing and patient protections against the potentially harmful deployment of AI in healthcare (NNU 2024). This large‐scale mobilisation directly challenges the uncritical adoption of AI in clinical settings and amplifies calls for policies that prioritise human governance, patient safety, and adequate nurse staffing over automation. By organising such demonstrations and demanding safeguards, this initiative demonstrates how NPOs engage in robust collective action to influence AI regulation, ensuring nurses' voices are amplified and centred in AI's responsible integration into healthcare and advancing a human‐oriented counterbalance to technology‐driven agendas.

4.3. Advocacy Engagement of NPOs in the Age of AI

Advocacy is another essential role of NPOs, which enable them to actively influence health policy, protect the interests of nurses, and promote the rights of the populations they serve. During challenging times (e.g., the COVID‐19 pandemic), professional nursing associations demonstrated their salient role in policy advocacy, providing essential support to nurses, the public, and healthcare systems alike (Chiu et al. 2021; Chiu et al. 2023). Their advocacy extends to addressing specific professional concerns, like the academic nurse educator shortage, where NPOs offer concrete recommendations to enhance the support for nurses (Quay et al. 2023). Organisations like the National Black Nurses Association have also advocated for care recipients, especially those experiencing marginalisation; this has been indicated by their strong history of championing health equity and social justice (Keepnews 2005).

Specifically for AI in healthcare, the advocacy modality of stakeholdership of NPOs can directly shape the regulatory and ethical landscape of AI deployment. This modality uniquely focuses on representing nursing's perspective in policy formulation. A prime example is the European Federation of Nurses Associations (EFN), which publicly submitted an online document of their position statement regarding the need for nurses to co‐design AI tools to explicitly lobby for policy (EFN 2021). The EFN strongly advocates for nurses' essential involvement from the earliest stages of AI development and implementation planning. By pushing for co‐design principles, EFN can directly influence European policies and frameworks for AI integration in the health sector. Moreover, the Oncology Nursing Society practiced patient advocacy by publishing evidence about patients' attitudes towards AI‐augmented healthcare, urging allied health professionals to be transparent regarding their usage of the technology (Kennedy 2025).

In addition to the aforementioned examples, Table 2 outlines illustrative potential actions that emerge at the intersection of the four governance domains and the three modalities of nursing professional organization (NPO) stakeholdership, offering a structured lens through which NPOs can articulate, coordinate, and strengthen their contributions to the multi‐stakeholder processes shaping healthcare AI governance.

TABLE 2.

An initial illustration of sample NPO‐driven actions/initiatives based on domains and modalities of stakeholdership for healthcare AI governance.

Domains (‘what’) Modalities (‘how’)
A. Professional development (enhance knowledge/skills) B. Organising & mobilisation (unite & act collectively) C. Advocacy (influence policy & public opinion)
1. Framing (identifying & defining challenges)

  • Webinars on identifying emerging AI biases in patient data

  • Toolkits for nurses to identify and report AI‐related safety concerns

  • Formation of special interest groups to identify emerging AI challenges

  • Member surveys to gather frontline nurse insights on AI challenges

  • Issuing position statements defining critical ethical challenges of AI in nursing

  • Submitting expert testimony to regulatory bodies on anticipated AI harms
2. Risk/Benefit assessment (analysing impact)

  • Training modules on evaluating AI diagnostic tools' accuracy and reliability

  • Competency frameworks for nurses to critically appraise AI's impact on patient outcomes

  • Mobilising expert panels for comprehensive ethical impact assessments of AI

  • Creating regional networks to analyse real‐world AI incidents

  • Lobbying for legislation requiring transparent reporting of AI's performance data and biases

  • Publicly endorsing or critiquing proposed AI systems based on risk–benefit analyses
3. Evaluation (assessing governance effectiveness)

  • Educational materials on assessing different AI regulation models for healthcare

  • Training for nurses to participate in AI governance committee evaluations

  • Forming a national nursing coalition to evaluate proposed AI governance regulations

  • Building alliances with legal and ethics experts to co‐develop evaluation criteria for AI governance

  • Submitting formal recommendations on proposed AI governance policies

  • Publishing comparative analyses of AI regulatory approaches, recommending optimal models for healthcare
4. Risk management (mitigating harms)

  • Best practice guides for integrating AI into nursing workflows while minimising harm

  • Certification programs for nurses specialising in AI oversight and risk mitigation

  • Forming multi‐stakeholder working groups to implement AI incident reporting systems

  • Mobilising nurses to participate in pilot programs for new AI risk mitigation tools

  • Proposing legislative mandates for continuous monitoring and auditing of AI systems in healthcare

  • Organising public awareness campaigns on patient rights in AI‐driven healthcare
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5. Human Rights Considerations for NPOs' Stakeholdership in Healthcare AI Governance

The preceding sections have mapped the healthcare AI governance ecosystem, including its layers and components, and explored the domains and modalities of NPOs' stakeholdership. An essential goal of AI governance is to maximise benefits and minimise harms in AI integration (Wirtz et al. 2022; Ronquillo et al. 2021). To illuminate what constitutes benefit or harm and determine the ethical commitments of NPOs towards other stakeholders, a third axis is introduced: the human rights axis. This axis embeds human rights‐based considerations into the framework, providing the moral foundation and addressing the critical questions of ‘for whom?’ and ‘to what end?’ of stakeholdership.

Human rights have long served as a normative compass for NPOs, guiding both their mission and actions. NPOs such as the International Council of Nurses (ICN 2011) and the Royal College of Nursing (RCN 2012) uphold international human rights standards by endorsing the Universal Declaration of Human Rights and mandating active engagement in health and social policy development. Moreover, these NPOs emphasise the need to document rights violations, support legal remedies, and promote human rights awareness for nurses and patients alike. Hence, the human rights lens is arguably an effective normative perspective for NPOs to view and responsibly engage with AI‐enabled healthcare.

5.1. Human Rights Based Approach to AI Integration in Healthcare

A Human Rights‐Based Approach (HRBA) is a framework rooted in principles of universality, indivisibility, equality, non‐discrimination, participation, inclusion, accountability, and the rule of law (Patterson 2024; Patterson et al. 2023; UN 2019). It represents a shift from needs‐based governance to one that emphasises rights, responsibilities, and structural change to address inequities. For healthcare, HRBA aligns with the right to health, asserting that institutions must ensure availability, accessibility, acceptability, quality, and equal distribution of health services (Patterson 2024; UN Committee on Economic, Social and Cultural Rights [CESCR] 2000).

In AI‐enabled healthcare, specific human rights considerations are forwarded by civic and professional organisations within (ANA 2022; EFN 2021; Healthcare Today 2024; ICN 2023; NNU 2024) and outside nursing (BSR 2023; WHO 2023). Taken together, these organisations forward key human rights commitments. First is the Right to Autonomy and Informed Consent, wherein AI systems must enhance, not diminish, decision‐making through transparency and explainability. Second is the Right to Privacy and Data Security, which ensures strict and consistent safeguards against unauthorised access and misuse of personal health data. Third is the Right to Equality and Non‐Discrimination, which calls on mitigating algorithmic bias, promoting inclusion, and preventing perpetuation of disparities (i.e., health, social, and digital). Fourth is the Right to Accountability and Redress, which entitles care providers and recipients to clear mechanisms for addressing harm or losses due to AI, ensuring justice for affected individuals and accountability for developers and implementors of AI systems. By upholding these rights, NPOs can design and implement stakeholder‐driven actions in healthcare AI governance, shaping other layers of the AI ecosystem and ultimately advancing human rights in health and other domains of well‐being.

5.2. NPOs as Human Rights Intermediaries: Mapping Rights Claimants and Duty Bearers

Understanding the commitment‐driven relationship between rights claimants and duty bearers is central to HRBA‐oriented stakeholdership in healthcare AI governance. The way NPOs interact with other players in the governance ecosystem is based on HRBA's distinctions. Rights claimants are individuals and groups entitled to fundamental rights and protections, especially those vulnerable to violations (Patterson 2024). In AI‐enabled healthcare, rights claimants include:

  1. healthcare recipients, which includes citizens (i.e., actual and potential clients) and society at large. This set of claimants include key populations, such as those from marginalised sectors, whose rights to autonomy, privacy, non‐discrimination, and quality care must be safeguarded in AI‐driven health applications (BSR 2023; ICN 2021; ANA 2022); and,

  2. nurses and other frontline health workers, who are claimants of rights to fair working conditions, autonomy, participation in AI innovations and policies, and protection from AI threats to job security and professional integrity (Smith et al. 2023; Rony et al. 2024).

From an HRBA lens, claimants require empowerment through rights awareness and active participation in AI development and policymaking (EFN 2021; Patterson 2024).

Duty bearers are entities in the stakeholder network responsible for respecting, protecting, and fulfiling human rights in AI‐enabled healthcare (WHO 2023). Key duty bearers include:

  1. the state/government, which is the bearer of primary responsibility for regulatory mechanisms ensuring ethical AI distribution, such as the AI act in the European Union (European Commission 2025; van Kolfschooten and van Oirschot 2024);

  2. AI developers & health technology sectors, who hold accountability for aligning innovations with human rights principles;

  3. healthcare organisations, which are responsible for implementing institutional policies and ethical guidelines for patients' and providers' rights (BSR 2023; Ronquillo et al. 2021; Badawy et al. 2024);

  4. global regulatory bodies, such as World Health Organization (WHO), that must provide international‐level oversight (WHO 2023)

These entities are accountable for rights realisation and can be capacitated to enhance their human rights obligations.

Through the lens of HRBA, NPOs function as intermediaries within the stakeholder network. They bridge duty bearers and rights claimants, ensuring responsible, ethical, and just deployment of healthcare. As human rights intermediaries, NPOs fulfil two crucial roles in enshrining human rights in healthcare AI governance. First, NPOs empower rights claimants through various modalities. This includes actions such as training nurses to uphold patient and nurse rights in AI (professional development), engaging patients, advocates, and communities to raise AI awareness and foster participation (organising and mobilisation), and popular dissemination of AI‐related rights in health and assisting patients and nurses needing redress (advocacy).

Second, and simultaneously, NPOs strengthen and hold duty bearers accountable through the same modalities. This can include initiatives such as equipping state actors, tech firms, and healthcare institutions with competencies for equitable AI governance (professional development), building coalitions with organisations outside nursing for resource and expertise sharing (organising and mobilisation), and leveraging influence to lobby AI‐related policies in health and hold duty bearers accountable for lapses (advocacy). Through their various modalities, NPOs bridge the gap between rights claimants and duty bearers, fostering an AI‐driven healthcare landscape that respects and upholds human rights.

6. Towards an Integrated Framework of Stakeholdership for Healthcare AI Governance: The Role of Nursing Professional Organisations as Human Rights Intermediaries

This concluding section integrates the three axes (i.e., domains, modalities, and human rights) to propose a comprehensive framework for Nursing Professional Organisations' stakeholdership in healthcare AI governance. This normative framework establishes NPOs' engagement at the intersection of what (domains: framing, risk/benefit assessment, evaluation, risk management) and how (modalities: professional development, organising and mobilisation, advocacy) they contribute. Crucially, the human rights axis anchors these efforts, defining the for whom and to what end of NPO engagement through meaningful interactions with rights claimants and duty bearers in the multi‐stakeholder governance network. Table 3 visually demonstrates how this triaxial matrix elucidates NPOs' possible initiatives, moving beyond a biaxial approach (see Table 2) to comprehensively address all facets of stakeholdership in healthcare AI governance.

TABLE 3.

An integrative triaxial matrix containing sample NPO‐driven actions/initiatives and target co‐stakeholders for healthcare AI governance integrating the domain, modality, and human rights axes of stakeholdership.

Domain 1: Framing (identifying & defining AI challenges)
Modalities Human rights considerations
Empowering rights claimants (patients, professional nurses, society at large) Strengthening and holding duty bearers accountable (government, medical and tech industries, healthcare institutions, regulatory bodies)
Professional development

Develop educational webinars for nurses on recognising patient privacy threats from new AI data practices

(Co‐stakeholders: Nurses, Patients)

Collaborate with healthcare institutions and regulatory bodies for integration of AI‐related content in nursing curricula

(Co‐Stakeholders: Healthcare Institutions, Regulatory Bodies, Academic Institutions)

Organising & Mobilisation Initiate joint campaigns with civil society organisations to highlight AI's potential for exacerbating health disparities (Co‐stakeholders: Volunteer Groups, Marginalised Communities)

Convene multi‐stakeholder workshops with government and industry to define priority AI governance challenges

(Co‐stakeholders: Government Agencies, Healthcare/Med Tech Industries)

Advocacy Push for patient representation on AI design and ethics review boards (Co‐stakeholders: Patients, Healthcare Institutions, AI Developers) Engage hospital associations to develop institutional policies that proactively and preemptively identify and mitigate AI's impact on nursing practice (Co‐stakeholders: Healthcare Institutions)
Domain 2: Risk/benefit assessment (analysing AI impacts)
Modalities Empowering rights claimants Strengthening and holding duty bearers accountable
Professional development

Provide training on identifying algorithmic bias in AI‐automated patient assessments

(Co‐stakeholders: Nurses, Frontline Healthcare Workers)

Share best practices with industry on involving diverse user groups in AI system testing for bias detection

(Co‐stakeholders: Healthcare/Med Tech Industries)

Organising & Mobilisation

Organise community town halls to discuss the pros and cons of AI in local healthcare settings

(Co‐stakeholders: Patients, Volunteer Groups, Local Communities)

Mobilise institutional ethics committees to standardise AI risk/benefit assessment protocols within hospitals

(Co‐stakeholders: Healthcare Institutions)

Advocacy Lobby policies to require patient‐friendly labels on AI‐powered medical devices detailing assessed risks and benefits (Co‐stakeholders: Patients, Government Agencies) Publish policy recommendations for healthcare institutions to conduct comprehensive ethical and social impact assessments for AI adoption (Co‐stakeholders: Healthcare Institutions)
Domain 3: Evaluation (assessing AI governance effectiveness)
Modalities Empowering rights claimants Strengthening and holding duty bearers accountable
Professional development

Courses on evaluating the effectiveness of AI governance policies in practice for nurses

(Co‐stakeholders: Nurses, Healthcare Workers)

Offer customised training for hospital quality improvement teams on evaluating AI's effect on patient safety indicators

(Co‐stakeholders: Healthcare Institutions)

Organising & mobilisation

Organise public campaigns to collect feedback on how existing AI policies impact care delivery and patient rights

(Co‐stakeholders: Patients, Public)

Facilitate industry‐wide forums for sharing data on AI system performance and accountability

(Co‐stakeholders: Healthcare/Med Tech Industries)

Advocacy

Issue public reports on the gaps between current AI regulations and actual patient experiences

(Co‐stakeholders: Public, Patients, Volunteer Groups)

Advocate for international agreements on standardised AI evaluation metrics for cross‐country deployment

(Co‐stakeholders: International Regulatory Bodies)

Domain 4: Risk management (mitigating AI risks)
Modalities Empowering rights claimants Strengthening and holding duty bearers accountable
Professional development

Create simulations for student nurses to practice ethical decision‐making when facing AI‐induced dilemmas

(Co‐stakeholders: Student Nurses, Faculty)

Share best practices with industry on transparent communication strategies for AI's limitations and potential risks (Co‐stakeholder: Healthcare/Med Tech Industries)
Organising & Mobilisation

Establish a hotline or online forum for nurses to report AI‐related ethical concerns or harms

(Co‐stakeholders: Nurses, Healthcare Workers)

Build a coalition of NPOs, academic institutions and patient advocacy groups to lobby for greater investment in explainable AI research (Co‐stakeholders: Volunteer Groups, AI Developers, Academia)
Advocacy Lobby for legislation that clearly defines liability and redress mechanisms for AI‐induced harm to patients (Co‐stakeholders: Patients, Government Agencies)

Advocate for strong regulatory oversight bodies with enforcement powers for AI safety standards

(Co‐stakeholders: Government Agencies, International Regulatory Bodies)

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The multifaceted stakeholdership efforts of NPOs hold the potential to influence all levels of the healthcare AI governance ecosystem, extending beyond the multi‐stakeholder layer to impact AI systems and policy outcomes (as seen in Wirtz et al. 2022). Figure 1 provides a visual schema of this integrative framework, illustrating how NPOs' human rights‐based stakeholdership contributes to the broader governance ecosystem. The diagram specifically expands the multi‐stakeholder layer, wherein the NPOs' intermediary role within this network of key actors is mapped. This integrated framework transcends Wirtz et al.' (2022) original ecosystem model by identifying co‐stakeholders as either rights claimants or duty bearers, aligning with the HRBA.

FIGURE 1.

FIGURE 1

Open in a new tab

Visualises the integrative framework of human rights‐based stakeholdership of NPOs in the healthcare AI governance ecosystem.

From NPOs, two primary arrows depict their intermediary function. The first points to rights claimants, including nursing professionals, patients, and the broader citizenry. Here, NPOs engage in empowerment initiatives to facilitate the realisation of human rights in AI‐enabled healthcare. The diagram further illustrates that nurses within this field also bear a duty to empower patients and citizens, highlighting the cascading and interdependent nature of HRBA.

The second arrow from NPOs extends to duty bearers: entities which must be strengthened and held accountable to ensure their obligations are met in providing high‐quality, equitably distributed, and risk‐mitigated AI‐enabled healthcare. This group encompasses government and public agencies, healthcare institutions, AI developers, medical technology industries, and international bodies. Within this HRBA‐oriented multi‐stakeholder network, these actors also share mutual accountability for upholding human rights while negotiating the balance between maximising AI's benefits and mitigating its harms. The interactions and outcomes within this network cascade to influence all other stakeholders of AI governance. Guided by this integrated triaxial model, NPOs—through their core functions of professional development, organising and mobilisation, and advocacy—emerge as potential transformative agents that champion rights‐oriented AI governance in health.

6.1. Practical Applications of the Triaxial NPO Stakeholdership Framework

While the proposed triaxial NPO stakeholdership framework is foundationally normative in nature, this paper argues that it also possesses significant pragmatic value. This framework can directly inform NPOs' decisions, operations, and strategies concerning technologies within an increasingly AI‐augmented healthcare landscape. Beyond being a conceptual map, the comprehensive triaxial matrix also serves as a practical tool for NPO leaders and members. It can help facilitate the systematic planning of responses to the profession's AI‐related needs. By using the matrix (e.g., Table 3), NPOs can brainstorm, design, and list a wide array of possible initiatives that can be undertaken. Once such a comprehensive plotting of actions is complete, NPO leaders and members can employ partial ranking to strategically prioritise initiatives, aligning them with available human, and material resources vis‐a‐vis the organisational and societal climate (see Kornberger and Leixnering 2025).

Furthermore, it is important to reiterate that the three stakeholder modalities within the framework are inherently interrelated functions of NPOs. This interconnectedness means that a single initiative can often encompass one, two, or even all three modalities simultaneously, thereby maximising efforts and impact. For example, an NPO (e.g., NNU or Sigma Theta Tau) could develop a dedicated webpage that serves as a learning resource for professional development, collects signatures to allow nurses to signify participation in campaigns, and simultaneously informs the public and policymakers about critical AI issues and solutions to advance the rights of nurses and the clients they serve (see Hawkins et al. 2025; National Nurses United 2024, 2025). Hence, the matrix can be best used by NPOs as a cognitive aid and visual prompt to facilitate collaborative decision‐making and ensure comprehensiveness of governance options that can serve as a springboard for innovation through careful and creative mix‐and‐matching.

6.2. Limitations and Challenges

The integrative framework for NPO stakeholdership in healthcare AI governance, while comprehensive, faces distinct limitations. A key challenge is fostering seamless interdisciplinary collaboration, as NPOs must bridge gaps with technical experts, policymakers, and industry, all of which have different lexicons and institutional cultures. Similar to those indicated in prior research about NPOs (Bazinski and Wilson 2023; Chiu et al. 2023; Slemon et al. 2024), resource and organisational constraints—such as budget limitations, access to expertise in technology and health policy advocacy, declining member engagement, organisational inertia, and the operational capacity to launch large‐scale initiatives—can hinder effective implementation of desired programs. Power imbalances also exist within the multi‐stakeholder governance network, which potentially marginalises the NPO voice in AI policy discourse, which is often saturated by tech, medical, and regulatory entities (Zaim and Shaw 2024). The dynamic nature of AI's ethical and regulatory landscape necessitates constant vigilance and adaptability from NPOs and partner institutions. Addressing these barriers will require NPOs to forge strategic alliances, invest in internal capacity‐building, and maintain persistent advocacy to intensify their influence on AI governance.

7. Conclusion

This study forwards a Human Rights‐Based Stakeholdership Framework for Nursing Professional Organisations as intermediaries in the healthcare AI governance ecosystem. By integrating specific governance domains with NPO modalities and human rights principles–both basic and AI‐specific, the framework illuminates how NPOs can actively strengthen duty bearers' accountability and empower rights claimants to navigate AI‐enabled healthcare safely and ethically. This triaxial model can help enhance NPOs' potential to impact AI systems through reciprocal interactions with other governance players. Human rights lens provides a common vocabulary that transcends geopolitical and disciplinary lines; hence, using HRBA, NPOs can shape both global policy and local applications. Through their concerted efforts in professional development, collective action, and policy advocacy, NPOs are uniquely positioned to champion health innovations aligned with social justice within the rapidly expanding AI‐integration in healthcare.

Author Contributions

J.V.C.: made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; involved in drafting the manuscript or revising it critically for important intellectual content; given final approval of the version to be published; participated sufficiently in the work to take public responsibility for appropriate portions of the content; agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding

The author has nothing to report.

Disclosure

Declaration of Generative AI and AI‐assisted technologies in the writing process: During the preparation of this work, the author utilized Grammarly and ChatGPT 4.0 to enhance readability. After using this tool/service, the author reviewed and edited content as needed to retain their voice and take full responsibility for the content in the publication.

Ethics Statement

The author has nothing to report.

Consent

The author has nothing to report.

Conflicts of Interest

The author declares no conflicts of interest.

Acknowledgements

Gratitude is extended to Dr. Ebinezer R. Florano, Professor from the National College of Public Administration and Governance of the University of the Philippines Diliman, and my co‐learners in the Public Administration theories course on the insightful discussions about artificial intelligence applications and issues in governance.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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