Can Artificial Intelligence Substitute Dental Public Health Expertise? A Competency‐Based Analysis Across Core DPH Domains

ABSTRACT

Background

Artificial intelligence (AI) is increasingly integrated into public health and dentistry, with a range of applications. While these developments raise questions about efficiency and scalability, uncertainty remains regarding whether AI can substitute for Dental Public Health (DPH) expertise. This paper aims to distinguish between AI‐amenable and fundamentally human DPH competencies.

Methods

An analytical framework was applied to the 10 dental public health (DPH) specialty competencies. Current AI applications reported in the health literature were mapped across competencies including program management, systems evaluation, ethical decision‐making, surveillance system design, communication, collaboration and leadership, policy advocacy, evidence appraisal, research, and integration of social determinants of health.

Results

AI demonstrated strong technical capacity across data‐intensive and analytical competencies, including program management, systems evaluation, surveillance design, evidence appraisal, research, and integration of social determinants of health. These domains were classified as augmentable, as AI enhanced data integration, pattern recognition, and operational efficiency when accompanied by appropriate human oversight. In contrast, competencies grounded in ethical decision‐making, communication, leadership and collaboration, and policy advocacy were identified as irreplaceable, as they require moral judgment, contextual interpretation, professional accountability, and relational engagement beyond current AI capabilities.

Conclusions

The findings support the use of AI as a complementary tool in DPH while affirming that the specialists remain indispensable for governance, equity, public accountability, and strategic decision‐making in population oral health policy and practice. Competency frameworks, accreditation standards, and workforce development efforts should explicitly define the appropriate role of AI while reinforcing the primacy of human judgment and ethical leadership in population oral health practice.

1. Background

Artificial intelligence (AI), particularly large language models and machine‐learning–driven decision‐support systems, is rapidly transforming health care delivery, research, and education. Within public health and dentistry, AI applications extend from diagnostic imaging and surveillance analytics to health communication, policy modeling, education support, and academic writing, reflecting its growing role as a technical aid across clinical, population, and scholarly domains while remaining dependent on human oversight and judgment [1, 2, 3, 4]. These developments have prompted growing debate regarding whether AI may eventually replace certain professional roles or fundamentally reshape how public health practitioners operate.

Dental Public Health (DPH) is uniquely positioned within this debate. As a specialty grounded in population‐level prevention, health systems analysis, ethics, leadership, and interprofessional collaboration, DPH practice extends well beyond technical or computational tasks [5, 6]. Recent literature has shown that AI tools can support a range of public health functions, including epidemiologic analysis, evidence synthesis, and health communication [1, 2, 3, 7]. At the same time, important questions remain regarding algorithmic bias, ethical accountability, data privacy, cultural competence, and the appropriate role of human judgment in population health decision‐making [7, 8].

The potential role of AI in DPH practice is nuanced, reflecting the integration of analytical capabilities, contextual interpretation, personalization, and relational leadership [7]. Importantly, professional competency frameworks provide a structured and policy‐relevant lens through which the role of AI can be critically examined. The DPH competencies articulated by the American Board of Dental Public Health (ABDPH) define 10 core domains encompassing program management, systems evaluation, ethical decision‐making, surveillance, communication, leadership and collaboration, policy advocacy, critical appraisal of evidence, research, and the integration of social determinants of health into practice [6]. These competencies collectively reflect not only what DPH professionals do, but how and why they do it.

This study aims to systematically assess and categorize DPH competencies based on their susceptibility to AI automation or augmentation, using a proposed competency‐based framework to identify competencies that remain fundamentally human.

2. Methods

2.1. Rationale for a Competency‐Based Approach

Competency‐based frameworks are widely used in medical/dental education and programs accreditation to define professional scope, accountability, and readiness for practice [9, 10]. Applying such a framework to publicly accessible generative AI tools allows for a structured, discipline‐specific assessment of technological impact on the profession. In DPH, the new competencies explicitly integrate ethical reasoning, systems thinking, leadership, and collaboration domains that are not easily captured by performance metrics alone [6]. The author qualitatively examined each of the 10 core DPH competencies articulated in “The American Board of Dental Public Health (ABDPH)”. Each competency domain reflects a core function of dental public health specialists (DPHS). The intent statements delineate the essential responsibilities and capabilities associated with each domain, clarifying its scope and the primary roles it encompasses [6].

2.2. Conceptual Framework

Building on the DPH competencies, the author, a Diplomate of the ABDPH, proposed a “Replace–Augment–Irreplace” framework to characterize the role of AI across DPH competencies:

• Replaceable functions refer to defined, technical tasks that can be automated with minimal risk.

• Augmentable functions involve shared human–AI collaboration, where AI enhances efficiency or insight but requires human oversight and discretion.

• Irreplaceable functions refer to human functions that cannot be automated, areas where accountability, values, and relationships are central.

By mapping AI capabilities onto established DPH competencies, this framework reframes the AI debate from one of occupational displacement to one of professional evolution. It clarifies where AI integration is appropriate, where caution is required, and where human expertise remains indispensable. This approach offers a scalable model for guiding education, policy, and governance as AI tools become increasingly embedded in population oral health practice.

The author discloses the use of an AI tool (ChatGPT, OpenAI) for limited language editing and clarity enhancement. All substantive intellectual contributions and final responsibility for the manuscript rest with the author.

3. Results

3.1. Mapping Artificial Intelligence to DPH Competencies

The author examined each of the 10 DPH competencies articulated in The ABDPH, through the lens of the proposed “Replace–Augment–Irreplace” framework [6]. Rather than treating AI tools as a uniform intervention, each competency is evaluated according to the nature of its underlying cognitive, ethical, analytical, and relational demands (Table 1).

TABLE 1.

Mapping artificial intelligence to the 10 ABDPH specialty competencies using the “Replace–Augment–Irreplace” framework.

ABDPH competency	Primary AI capabilities	Primary human expertise required	Framework classification
1. Manage oral health programs for population health	Demand forecasting; resource optimization; performance dashboards; program simulation	Strategic prioritization; accountability; ethical allocation; institutional negotiation	Augmentable
2. Evaluate systems of care that impact oral health	Systems modeling; data analysis; disparity detection; scenario forecasting; program workforce/program	Historical and political interpretation; regulatory understanding; equity translation	Augmentable
3. Demonstrate ethical decision‐making in DPH practice	Bias detection; disparity identification; ethical risk flagging	Moral judgment; accountability; value‐based prioritization; cultural and context understanding	Irreplaceable
4. Design surveillance systems to measure oral health status and determinants	Automated data retrieval and analysis; metrics identification; anomaly detection; real‐time monitoring	Indicator selection; governance oversight; regulatory compliance; ethical stewardship; confidentiality protection	Augmentable
5. Communicate on oral and public health issues	Drafting briefs; message tailoring and dissemination; summarization; multilingual translation	Credibility; contextual awareness; trust‐building; adaptive engagement; effective communication; cultural appropriateness; challenges recognition	Augmentable
6. Lead collaborations on oral and public health issues	Stakeholder mapping; workflow coordination; meeting facilitation	Vision‐setting; coalition‐building; conflict resolution; negotiation; mentorship	Irreplaceable
7. Advocate for public health policy, legislation, and regulation	Policy comparison; design strategies; legislative drafting assistance; impact modeling; service gap identification	Political judgment; moral persuasion; negotiation; strategic timing; raising awareness; policy promotion	Irreplaceable
8. Critically appraise evidence to address oral health issues	Literature retrieval; rapid synthesis; analytic assistance; trend detection	Methodological evaluation; contextual applicability; validity assessment	Augmentable
9. Conduct research to address oral and public health problems	Data cleaning; advanced modeling and analysis; hypothesis generation; manuscript drafting; big data management	Research question framing; ethical oversight; interpretation; scientific accountability	Augmentable
10. Integrate the social determinants of health into DPH practice	Determinants modeling; inequity mapping; upstream simulation; determinant analysis	Community engagement; structural interpretation and evaluation; equity‐centered action; design consideration	Augmentable

3.1.1. Manage Oral Health Programs for Population Health

Program management represents a core specialty function of the DPHS, encompassing “population needs assessment, strategic intervention selection, implementation, evaluation, budgeting, and sustainability planning”. This competency integrates epidemiology, health systems management, financial governance, quality assurance, and ethical oversight within a population‐based framework [6].

AI systems demonstrate strong technical capacity in several operational domains relevant to program management. For instance, AI can integrate multisource data (surveillance, census, HRSA warehouse, health reports, etc.) to identify high‐risk populations and unmet needs [11]. Further, simulation tools can compare projected outcomes of alternative preventive or intervention strategies [11, 12]. Predictive modeling can also assist in workforce deployment and service demand forecasting [11, 12, 13]. AI‐enabled dashboards allow real‐time tracking of key performance indicators and quality metrics. Automation supports data cleaning, workflow tracking, and quality assurance reporting [11, 12, 13]. Generative systems may support drafting of grants, funding proposals, creating manuals, and budget justifications. These capabilities offer efficiency gains and enhanced analytic precision, particularly in large‐scale DPH programs with complex data streams and limited available resources.

Despite these strengths, setting priorities when resources are limited requires value‐based judgment and careful consideration of competing needs. Compliance with public health laws, regulations, and ethical standards cannot be transferred to algorithmic systems. Further, financial responsibilities, including budget negotiation, governance, and accountability to funders, require political awareness and professional judgment. Managing people through recruitment, mentorship, staff development, and conflict resolution depends on interpersonal skills and leadership. Sustaining programs over time also requires adaptability, stakeholder engagement, and responsiveness to shifting policy and funding environments. Ultimately, successful public health programs are grounded in trust, transparency, and alignment with community values, qualities that extend beyond the computational capability of AI. Accordingly, this competency is classified as augmentable. While AI can enhance analytic and operational functions, it lacks moral agency, professional responsibility, and institutional accountability, and therefore serves as a decision‐support tool rather than a decision‐maker.

3.1.2. Evaluate Systems of Care That Impact Oral Health

Evaluating systems of care represents a central specialty function of the DPHS. This ABDPH competency requires “assessing the effectiveness, accessibility, equity, and sustainability of oral health care systems across diverse communities”. It includes workforce analysis, financing evaluation, policy assessment, disparity monitoring, and collaboration across professional disciplines to identify system gaps and inform reform [6].

AI systems offer substantial analytic and monitoring capacity for systems evaluation. By integrating large administrative, clinical, and workforce datasets, AI can identify inefficiencies, detect geographic and sociodemographic disparities, analyze provider distribution, and model the potential impact of policy or delivery reforms [1, 14]. These tools can enhance big data analysis and improve the timeliness of evaluation [1, 14]. However, evaluating systems of care requires more than technical analysis. Interpreting system performance requires understanding historical, political, regulatory, and socio‐cultural contexts. Algorithms trained on existing systems risk normalizing structural inequities unless critically interrogated [15, 16]. Therefore, decisions related to workforce development, financing, and equity‐focused reforms involve ethical considerations and professional judgment. In addition, translating analytic findings into feasible policy recommendations and building consensus among stakeholders requires negotiation, trust, and leadership.

Human expertise is essential for contextualizing outputs, questioning assumptions, and translating systems knowledge into equitable action. This competency is augmentable, but only with intentional DPHS oversight.

3.1.3. Demonstrate Ethical Decision‐Making in the Practice of Dental Public Health

Ethical reasoning and decision‐making form the moral foundation of DPH practice [ref]. Public health practice is guided by core values such as equity, social justice, community participation, and respect for diversity [6, 17, 18]. Professional conduct is further informed by ethical principles including autonomy, beneficence, nonmaleficence, justice, and veracity, which guide practitioners when addressing complex or competing public health priorities [17, 18].

Current AI systems may support ethical deliberation by identifying disparities in oral health outcomes, detecting bias within datasets or predictive models, and highlighting potential unintended consequences of interventions [3, 4, 7, 15]. These capabilities can help inform evidence‐based discussions regarding resource allocation, program design, and policy decisions. However, ethical decision‐making requires context understanding, moral reasoning, professional accountability, and value‐based prioritization that AI systems fundamentally lack [19]. DPHS must balance competing ethical considerations, engage communities in decision‐making processes, and ensure that policies and programs reflect public health values. AI lacks moral agency and cannot assume responsibility for decisions affecting population health. As such, this competency is irreplaceable. AI may inform ethical deliberation, but ethical authority and accountability must remain with the DPHS.

3.1.4. Design Surveillance Systems to Measure Oral Health Status and Determinants

Public health surveillance involves “the continuous and systematic collection, analysis, and interpretation of health‐related data to inform needs assessment, policy development, and program evaluation” [6]. In DPH, surveillance systems “monitor oral health status, access to care, service utilization, behavioral and environmental risk factors, workforce capacity, and broader social determinants of health”. These systems help identify emerging health concerns, track disease trends, and evaluate public health interventions [6].

AI‐enabled tools can automate labor‐intensive processes such as data cleaning, standardization, and analysis across large and heterogeneous population datasets [1, 20]. In addition, advanced pattern recognition methods facilitate identification of trends, disparities, and associations that may be difficult to detect using conventional analytic approaches. However, designing surveillance systems requires professional judgment. DPHS must select appropriate indicators, interpret trends within epidemiologic and historical context, and ensure the ethical use and protection of sensitive health data. Collaboration with epidemiologists, biostatisticians, and community stakeholders is also essential [6].

AI can strengthen data integration and analytic capacity, but the design, interpretation, multidisciplinary teams' collaboration and ethical oversight of surveillance systems remain the responsibility of the DPHS. This competency is augmentable.

3.1.5. Communicate on Oral and Public Health Issues

Effective communication is a core competency of the DPHS. According to the ABDPH, “DPHS should communicate scientific findings, program outcomes, and policy recommendations to diverse audiences including health professionals, policymakers, and the public”. This includes “preparing reports, presentations, and educational materials; engaging stakeholders in policy discussions; advocating for public health interventions; and translating scientific evidence into information that is understandable and actionable across varying levels of health literacy” [6].

Generative AI systems can assist with drafting reports, policy briefs, grant proposals, presentations, and educational materials, as well as summarizing scientific literature and translating technical information into simpler language. AI may also help tailor and disseminate communication materials for different audiences and support dissemination through digital platforms and social media [21, 22]. However, effective public health communication requires contextual awareness, credibility, and responsiveness to community needs. DPHS must interpret evidence, adapt messages for culturally and linguistically diverse populations, and engage stakeholders in meaningful dialog. Advocacy and persuasion also depend on tone, trust, professional judgment, and ethical responsibility, which cannot be delegated to algorithmic systems. This competency is augmentable. AI can enhance efficiency in drafting and disseminating information, but the interpretation, contextualization, and ethical responsibility of communication remain with the DPHS.

3.1.6. Lead Collaborations on Oral and Public Health Issues

Leadership and collaboration are essential competencies for the DPHS. DPHS often mobilize partnerships among health professionals, government agencies, academic institutions, and community organizations to address complex oral health challenges. This competency “involves building interdisciplinary coalitions, facilitating shared goals, managing stakeholder relationships, and supporting community capacity to implement public health initiatives” [6].

AI tools may support some coordination aspects of collaborative work. For example, AI‐enabled systems can assist with organizing meetings, summarizing discussions, mapping stakeholder networks, and supporting project management tasks [21, 22]. These tools may improve communication efficiency and information sharing among partners involved in public health initiatives. However, effective collaboration depends on leadership, trust‐building, and interpersonal engagement. DPHS must facilitate consensus, mediate conflicts, negotiate competing interests, conduct capacity‐building activities, and guide coalitions toward shared objectives. These functions require professional judgment, relational competence, and credibility among diverse stakeholders. This competency is irreplaceable. While AI may support coordination and information management, leadership of collaborative partnerships and coalition‐building remains fundamentally the DPHS responsibility.

3.1.7. Advocate for Public Health Policy, Legislation, and Regulations to Promote Oral Health

Advocacy is a key function of the DPHS, “involving efforts to influence policies, legislation, and resource allocation that promote oral health and reduce inequities in access to care.” DPHS engages policymakers, community organizations, and stakeholders to raise awareness of oral health disparities, communicate the value of prevention, and support policies that benefit underserved populations [6].

AI tools may assist with some analytical and communication aspects of advocacy. For example, AI can help synthesize evidence, summarize research findings, generate policy briefs, and identify trends in population health data that support policy arguments [21, 22]. AI may also assist in preparing advocacy materials and tailoring messages for different audiences.

However, effective advocacy requires political judgment, credibility, and persuasive engagement with decision‐makers and communities. DPHS must navigate political environments, negotiate competing interests, and represent the needs of vulnerable populations. These responsibilities require ethical judgment, professional accountability, and relational trust. This competency is irreplaceable. AI may support evidence synthesis and communication in advocacy efforts, but the responsibility for policy engagement, persuasion, and ethical representation of population interests remains with the dental public health specialist.

3.1.8. Critically Appraise Evidence to Address Oral and Public Health Issues

Critical appraisal of scientific evidence is a central competency of the DPHS. This competency involves “formulating relevant public health questions, identifying and retrieving scientific literature, evaluating study design and methodological rigor, and interpreting findings to inform public health policies, programs, and interventions.” These activities require foundational expertise in epidemiology and biostatistics to assess the validity, relevance, and applicability of research evidence to population health [6].

AI tools can support several technical aspects of evidence appraisal. AI‐enabled systems can assist with literature searches, rapid summarization of research findings, and identification of relevant studies across large databases [22, 23]. Natural language processing tools may also support evidence synthesis by extracting key information from scientific publications and assisting in the preparation of reports or presentations. These functions offer substantial efficiency gains in settings marked by information overload and limited manpower and resources, by accelerating literature synthesis, reducing cognitive and time demands, and supporting scalable translation of complex evidence into accessible formats for research and practice.

However, critical appraisal requires professional judgment to evaluate methodological quality, recognize biases, assess the strength of evidence, and determine the applicability of findings to specific populations or policy contexts. These interpretive tasks rely on disciplinary expertise and cannot be fully automated. This competency is therefore strongly augmentable. AI can substantially improve the efficiency of literature retrieval and synthesis, but the evaluation, interpretation, and application of evidence remain the responsibility of the DPHS.

3.1.9. Conduct Research to Address Oral and Public Health Problems

Conducting research is a fundamental competency of the DPHS. DPHS “investigate oral and public health problems by formulating research questions, designing studies, analyzing data, and disseminating findings to inform policy and practice.” This work includes the use of epidemiologic methods, quantitative and qualitative analyzes, and the interpretation of primary and secondary datasets to evaluate access, cost, quality, and outcomes of oral health services [6].

AI tools can assist several technical aspects of the research process. AI‐enabled tools can support literature identification, data cleaning, statistical modeling, and analysis of large integrated datasets [1, 20, 22, 23]. Machine learning approaches may also facilitate pattern recognition in complex or “big data” environments and assist in drafting research reports or manuscripts [24].

However, research requires professional judgment in defining meaningful research questions, selecting appropriate study designs, interpreting findings, and ensuring ethical oversight of study participants and data. DPHS are responsible for ensuring methodological rigor, transparency, and the responsible use of research findings to guide public health action. This competency is therefore strongly Augmentable. AI can enhance analytic efficiency and support data‐intensive research processes, but study design, interpretation of findings, and ethical responsibility remain the role of the DPHS.

3.1.10. Integrate the Social Determinants of Health Into Dental Public Health Practice

Understanding and integrating the social determinants of health is essential to DPH practice. These determinants include “social, economic, environmental, and cultural factors that influence oral health outcomes and access to care” [6, 25]. DPHS must assess how factors such as socioeconomic status, education, geographic location, health literacy, and cultural context shape oral health disparities and barriers to care. Integrating these determinants into program design, policy development, and research is critical for addressing inequities and promoting population health [6].

AI tools can support the analysis of social determinants by integrating large datasets and identifying patterns associated with health disparities. They are increasingly applied to model social determinants of health, map oral health care pathways, and simulate system‐level interventions across diverse populations [14, 15]. By integrating large‐scale administrative, clinical, and population‐level data, these tools can reveal structural patterns, inequities, and interdependencies within health and social care systems [1, 15]. Such applications enable exploration of how upstream determinants and health system configurations influence oral health outcomes at both individual and population levels, and allow for forecasting of potential effects associated with policy or service delivery changes [1, 14].

Nevertheless, integrating social determinants into DPH practice requires contextual understanding, community engagement, and ethical commitment to equity and social justice. Further, algorithms trained on existing systems risk normalizing structural inequities unless critically interrogated [7, 15, 16]. DPHS expertise is essential for contextualizing outputs, questioning assumptions, and translating systems knowledge into equitable action. This competency is augmentable with ethical oversight. AI can assist in analyzing social determinants and identifying patterns in population data, but the interpretation, ethical considerations, and community‐centered application of these insights remain the responsibility of the DPHS.

4. Discussion

This study indicates that AI tools demonstrate their greatest strengths in technically intensive domains of DPH competencies, particularly population‐level data utilization, evidence synthesis, and predictive risk modeling. AI tools can substantially enhance efficiency through automation of data cleaning, standardization, linkage, pattern recognition, and visualization, supporting analytic capacity for oral health promotion, patient care, and quality improvement. AI systems also show utility in supporting interprofessional care through improved information sharing, care coordination, and decision‐support functions, as well as in facilitating lifelong learning via rapid access to emerging evidence and personalized content delivery. However, the findings consistently highlight that AI capabilities remain supportive rather than substitutive. Core DPH functions such as (ethical decision‐making, leadership, policy advocacy, stakeholder engagement, cultural humility, and shared decision‐making) depend on human judgment, values, and relational processes that extend beyond algorithmic computation. Collectively, these findings underscore that while AI can enhance efficiency and analytic capacity in DPH, it cannot replace DPHS, who remain central to governance, ethical stewardship, equity, and system‐level decision‐making.

The findings of this ABDPH competency‐based analysis demonstrate that artificial intelligence does not uniformly transform DPH practice. Instead, its impact varies systematically across competencies depending on the degree to which tasks are computational, contextual, or ethical and relational in nature. While AI shows strong potential to replace or augment analytic efficiency and data‐driven functions, it remains fundamentally incapable of assuming responsibility for ethical reasoning, leadership, advocacy, or relational care [7, 8, 26].

Importantly, even within highly augmentable domains, AI outputs require careful interpretation. The analysis highlights that AI systems, particularly those trained on historical data, may reproduce or reinforce existing structural inequities if not critically examined [7, 15, 16]. As such, the role of the DPHS shifts from sole executor to informed overseer responsible for contextualizing findings, questioning assumptions, and ensuring alignment with public health values, particularly equity and social justice.

These distinctions have important implications for ABDPH accreditation and workforce preparation. As AI‐enabled tools become increasingly integrated into DPH practice, competency frameworks must clearly articulate the appropriate role of AI as a supportive resource while affirming that professional judgment, ethical responsibility, and accountability remain the responsibility of the DPHS. ABDPH‐aligned training standards should therefore incorporate competencies related to AI literacy, critical appraisal of algorithmic outputs, and oversight of data‐driven decision‐support systems, consistent with the Board's emphasis on evidence‐based practice, systems thinking, and ethical leadership [5]. Importantly, such integration must reinforce, rather than replace, the core competencies of DPHS, including governance, equity‐oriented decision‐making, interprofessional collaboration, and stewardship of population health. From a workforce perspective, the risk is not that AI will replace DPH professionals, but that professionals who lack AI literacy may become less effective within increasingly technologized systems. Future‐ready DPHS will be those who can supervise, interrogate, and ethically deploy AI as a co‐practitioner rather than defer to it as an authority. In this sense, AI represents not a threat to DPH, but a catalyst for redefining professional identity, responsibility, and leadership in population oral health.

Although this study offers a novel perspective, it is limited by its conceptual design and reliance on existing literature and qualitative analysis of the competency framework, rather than empirical evaluation of AI implementation in DPH practice. Rapid technological change, variability in institutional readiness, and the absence of real‐world outcome data further limit generalizability. Future research should focus on empirical validation of AI applications within specific DPH domains, including evaluation of their impact on population health outcomes, equity, and system performance. In addition, implementation studies examining integration into real‐world public health settings, as well as assessments of ethical, regulatory, and workforce implications, are needed to inform responsible adoption.

5. Conclusion

The findings support the use of AI as a complementary tool in DPH and affirm that DPHS remain indispensable for governance, equity, public accountability, and strategic direction functions that cannot be delegated to algorithms and automated systems. When appropriately governed, AI has the potential to enhance analytic capacity, efficiency, and evidence‐informed practice across population health functions. However, the responsible integration of AI depends on the continued leadership of DPHS to interpret outputs, balance ethical trade‐offs, safeguard equity, and ensure alignment with population needs and professional standards. From a policy and workforce perspective, these findings underscore the need for clear guidance on the appropriate scope of AI use in DPH practice, as well as deliberate investment in workforce development to ensure AI literacy, ethical oversight, and supervisory capacity among DPH professionals. As such, AI should be viewed as an enabling resource that strengthens the expertise, judgment, and societal role of the DPH workforce, while reinforcing the centrality of human leadership in shaping equitable and accountable oral health systems.

Conflicts of Interest

The author declares no conflicts of interest.

Data Availability Statement

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