FROM THE STACKS
EDITOR’S NOTE: There are literally thousands of journals published around the world that relate to the disability community. It is virtually impossible to capture even a fraction of them. HELEN receives "stacks" of journals and selectively earmarks what we feel are "must read" articles of interest for our readers. It's a HELEN perk.
An Updated Definition of Global Health
Kathryn H. Jacobsen, Caryl E. Waggett, Olusoji Adeyi, Walter Bruchhausen, Shahanaz Chowdhury, Patricia M. Davidson, Ximena Garzón-Villalba, Lawrence O. Gostin, Liz Grant, Philip J. Landrigan, Hao Li, Mario C. Raviglione, Nancy R. Reynolds, Nelson K. Sewankambo, Brittany Seymour & Keith W. Martin
Excerpt from Global Health Research and Policy volume 10, Article number: 56 (2025)
Abstract
The most cited definition of global health, published in The Lancet in 2009, defines global health as “an area for study, research, and practice that places a priority on improving health and achieving equity in health for all people worldwide”. In this article, we propose an updated definition that expresses the motivations of diverse global health actors and makes One Health and sustainability more visible: “Global health is a field of academic study, research, policy, and applied practice that advances the equitable protection and improvement of population and planetary health”. Our “5 Ps model” illustrates global health as a grid that places health for all at the center of two axes representing four domains: (1) People, (2) Planet, (3) Priorities, and (4) Policies and Practices. The people–planet axis spans from social, economic, political, and other systems that affect human health to complex worldwide challenges such as those related to globalization, migration, pandemics, and climate change. The priorities–policies/practices axis positions global health as an action-oriented field in which factors such as human rights, international law, the global burden of disease, and evidence of economic impact inform the financing, implementation, and evaluation of multisectoral partnerships and interventions. We propose using this updated definition and the 5 Ps framework to modernize discussions of the scope and purpose of global health.
Background
Global health was established as a distinct field in the late 1990s with the goal of replacing older, often colonial models of international health engagement with more equitable partnerships that respond to existential human threats while continuing to support improvements in population health in low- and middle-income countries. The new era of global health ushered in a broader array of funders, unprecedented resources for health, new public–private partnerships, and expanded roles for governments, businesses, and civil society organizations [1, 2]. Multilateralism and effective global governance, including greater transparency and accountability, were championed in this framing of global health, but some of the dominant actors downplayed critical questions about power imbalances, imperialism, aid dependency, and other tensions between the “Global North” and “Global South” [3].
Investment in global health surged in the early 2000s as the Millennium Development Goals (MDGs) established by the United Nations (UN) catalyzed international support for poverty reduction in 2000–2015 [4]. The documented successes of the MDG era prompted the UN General Assembly, in collaboration with UN Member States and hundreds of nongovernmental organizations, to adopt the more ambitious Sustainable Development Goals (SDGs) for 2016–2030 [5]. The SDGs were founded on the premise that narrowing the disparities between and within the world’s richest and poorest countries would yield lasting benefits for all collaborating parties while generating prosperity, preserving the planet, and fostering peace. As part of the SDG process, high-income countries made voluntary financial and technical commitments to accelerate progress on dozens of economic, health, environmental, and other targets in less-resourced countries.
The coronavirus pandemic that began in 2020 stalled progress on nearly all of the SDG targets and dampened enthusiasm for the concept of global goals even as it demonstrated the interdependence and shared vulnerabilities of all nations [6, 7]. The pandemic also revealed weaknesses in the International Health Regulations and in global governance structures and organizations, including the World Health Organization [8]. The strain on international relations during the pandemic foreshadowed the broader breakdown of global political norms in the post-pandemic years. By 2025, the United States and some other countries had begun withdrawing from participation in global governance and reframing equity, diversity, inclusion, and international cooperation as threats rather than strengths [9].
Global health has from its inception been motivated by a mix of economic considerations, humanitarian impulses, and biosecurity concerns [10]. All of these drivers of global health are currently being undermined by nationalistic and isolationistic movements that demand reduction of international aid and weaken the diplomatic cooperation required to mitigate shared threats like climate change, plastic pollution, armed conflict, and pandemics through treaties and other international instruments [11]. These rapid sociopolitical changes warrant an updated definition of global health that clarifies the scope and purpose of the field.
Current definition
The most commonly cited definition of global health, written by Jeffrey Koplan and the executive board of the Consortium of Universities for Global Health (CUGH) and published in The Lancet in 2009 as a viewpoint entitled “Towards a common definition of global health” [12], defines global health as “an area for study, research, and practice that places a priority on improving health and achieving equity in health for all people worldwide” and explains that “global health emphasizes transnational health issues, determinants, and solutions; involves many disciplines within and beyond the health sciences and promotes interdisciplinary collaboration; and is a synthesis of population-based prevention with individual-level clinical care”. Global health differs from public health in scope, governance, and complexity, with global health initiatives targeting health issues that have been prioritized at the international or global levels rather than at local or national scales and global health decision-making involving a more diverse set of multilateral and multisectoral actors than public health [12].
The definition by Koplan et al. has been adopted by numerous medical, public health, and other groups, in part because it presents an ambitious vision for what the field can ultimately accomplish. However, several updates would make this definition more relevant today. Two are especially important.
First, the Koplan definition emphasizes a vision for global health transformation more than the policy-based processes that are necessary for achieving this goal. We affirm that definition’s centering of the values and ideals of global health. A commitment to ensuring that all people and all communities have an equal opportunity to achieve their own best health status motivates many individuals and groups working in the multidisciplinary, interprofessional global health space [13]. Core principles like health equity, social justice, collaborative governance, and sustainability remain central to the field [14]. However, these tenets represent only part of the rationale for investment in global health. For example, governments, corporations, and philanthropists use global health to enhance their own security, strengthen their diplomatic efforts, exercise their power, expand commercial opportunities, and elevate their reputations. The Koplan definition highlights the strengths and aspirations of global health at its best but glosses over some of the strategic and political realities that also shape this multisectoral field. A more candid and comprehensive definition must express a broader set of motivations, mechanisms, and actors involved in global health prioritization, policymaking, financing, and implementation.
Second, the Koplan definition of global health, which predates the integrated cross-sectoral vision of the SDGs, focuses exclusively on human health. Since that definition was published, climate change, biodiversity loss, persistent pollution, and other forms of environmental degradation have come to be recognized as leading threats to the health and wellbeing of current and future generations [15]. Humans are interdependent with animals and ecosystems, as emphasized in the One Health approach, and all living things depend on a healthy Earth and healthy ecosystems for survival [16]. Because environmental sustainability is now recognized as essential to safeguarding human security, an updated definition of global health must make planetary health a visible priority [17, 18].
Proposed definition
Dozens of definitions of global health have been written in the years since the Koplan et al. paper was published, but that definition continues to be the most widely used one [19]. The paper presenting the definition has been cited thousands of times, and organizations in every world region feature the definition on their websites, in their institutional reports, and in their educational materials. Because this definition still holds significant weight in the field, we aim to modernize it while retaining its foundational structure. We therefore propose this update to the Koplan definition: “Global health is a field of academic study, research, policy, and applied practice that advances the equitable protection and improvement of population and planetary health”.
The key dimensions of a revised global health definition can be visualized using a grid that places health for all at the center of four domains: (1) people, (2) planet, (3) priorities, and (4) policies and practices. Figure 1 depicts this framework as a graph centered on health equity. The sample content provided for each domain is intended to be illustrative rather than exhaustive. Practitioners typically concentrate their work in one domain or quadrant, but the field as a whole comprises all parts of the graph [20].
Fig. 1
The 5 Ps model of global health
Health is protected through actions that advance human rights, attenuate risk factors, and prevent new public health threats in an equitable and just manner. The people–planet axis of the 5 Ps framework identifies the many shared problems that are amenable to preventive interventions, spanning from the full set of social, economic, political, and other factors that affect access to medical care and public health services through transnational threats like pandemics and climate change that require coordinated global responses and can affect human, animal, and ecosystem health. This axis expresses both the comprehensive and worldwide meanings of the word “global” in global health. The language of protection also evokes the importance of global health security, a responsibility that has motivated government participation in many global health partnerships and initiatives.
Health is improved through actions that ameliorate existing health concerns and generate progress toward achieving health equity [21]. The priorities–policies/practices axis summarizes the full scope of activities, from upstream governance through downstream implementation, that can increase healthy lifespans, resilience, and preparedness in communities and countries around the world. These actions span from international agreements and scientific evidence that shape global health agendas to the on-the-ground activities that contribute to solving prioritized health problems. This axis applies to both academic and applied global health work, positioning global health both as a discursive and an action-oriented field.
Implications for global health
Our proposed definition represents an incremental yet vital change for this interdisciplinary, interprofessional field. We acknowledge that this is not a transformative reimagining of global health in response to its critics, but we believe that it is necessary to clarify the current state of the field and build common ground across diverse worldviews so that stakeholders in global health can work more effectively together to shape a healthier future [22]. We have aimed to balance idealism and pragmatism in our definition, retaining health equity as the ultimate goal and framing global health as broad but not borderless. We affirm calls for continued reflection and dialogue about power dynamics and politics in global health to ensure that the field evolves in ways that are responsive to critical perspectives and changing global realities [23].
We write with a vision of strengthening, rather than abandoning, global health governance. Recognizing weaknesses in the current model of international collaboration is a critical first step toward improving the processes and structures that enable governments and their partner organizations to set effective policies, provide services, protect human rights, and build public trust. Since governments and governmental agencies may not always represent the best interests of their populations, especially in fragile states and nondemocratic settings, a model of global health that places the wellbeing of people at the center and welcomes contributions from a range of disciplines, professions, and sectors will be better equipped to meet complex health challenges now and create more effective, inclusive, transparent, accountable, and resilient systems for the future.
As leaders within CUGH and other global health organizations, we advocate for a transition from the original definition by Koplan et al. to this amended definition. We believe that this updated definition and the 5 Ps framework—people, planet, priorities, policies and practices—can enrich conversations about the field’s scope, relevance, and value and modernize how the field of global health is conceptualized and applied in teaching, research, and policy contexts.
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Dentists’ Perception and Use of AI and Robotics in the Care of Persons with Disabilities
By Najla A. Barnawi, Fay A. AlAmmar, Sultan A. Aldabeis, Salma M. Alahmar, Ahmed A. Alharthi & Mostafa A. Abolfotouh
Scientific Reports volume 15, Article number: 27551 (2025) Cite this article
Abstract
Despite the growing role of AI and robotics in healthcare, little is known about their integration into dental care for persons with disabilities (PWDs) in Saudi Arabia. This study aimed to assess dentists’ perceptions and attitudes towards and use of RT/AI in dentistry and identify the predictors of using RT/AI to care for PWDs in the Saudi context. A cross-sectional study was conducted using a previously validated online self-reported questionnaire via SurveyMonkey, targeting 309 Saudi and non-Saudi licensed dentists and dental/oral health practitioners, to collect data on the following: 1) Personal and work-related characteristics, 2) Perception toward RT/AI use, 3) Attitude toward using AI and RT in dentistry, and 4) Current use of RT and AI. RT/AI use rate was calculated for each clinical aspect and each type of impairment. Logistic regression analysis was used to identify the predictors of dentists’ use of RT and AI to provide care for PWDs. Significance was set at p < 0.05. Our study revealed that 59.2% of dentists who worked with PWDs reported utilizing RT/AI in various clinical aspects. Almost one-fourth of dentists reported using RT/AI in clinical examinations (23.9%), managing complications (26.8%), and performing invasive procedures (28.6%). Nearly one-third of respondents reported using RT/AI for taking a history (30%), non-invasive procedures (31.5%), behavioral training sessions (32.9%), health education (36.2%), medical diagnosis (36.6%), diagnostic tests (38%), and treatment planning (43.7%). Over one-half (54.9%) and one-fourth (28.6%) of the dentists reported a positive perception and attitude towards RT/AI use in dentistry. However, after adjusting for possible confounders, only previous RT/AI training remained a significant predictor of RT/AI use among dentists working with PWDs (OR = 9.18, 95% CI 2.92–28.90, p < 0.001). Our study is the first in the Saudi context to investigate the use of RT and AI by dentists caring for PWDs. Previous training was associated with greater use of RT/AI in this context. Potential collaborations between dental institutes and stakeholders in the RT and AI industry are recommended.
Introduction
Integrating artificial intelligence (AI) and robotic technology (RT) in healthcare has become essential in clinical practice, particularly for healthcare workers providing direct care to persons with disabilities1,2,3. Dental procedures such as restorative dentistry, implantology, and education have all used robotic and AI-based technology. For instance,"Yomi,"an implantology robot, is marketed as a standardized dentistry practice. However, it might not be easy for regular dentists to stay current on these robotic projects’ scientific advancements and capabilities. Most studies on robotic technology in dentistry remain in the proof-of-concept stage, and the quality of the literature is regarded as low4,5. Others, however, highlighted the significance of RT and AI in various areas of dentistry where AI has demonstrated effectiveness, including facial growth analysis, cephalometric landmark tracking, implant placement planning, automated detection of dental caries, bone loss, and periapical disease, oral cancer screening identification of radiolucent and cystic lesions, tooth color selection, tooth-root morphology analysis, and examination of the internal structure of the root canal system6,7,8.
According to the United Nations (2007), the definition of a person with disabilities (PWDs) is “a term that applies to all persons with disabilities including those who have long-term physical, mental, intellectual or sensory impairments which, in interaction with various attitudinal and environmental barriers, hinders their full and effective participation in society on an equal basis with others9. Evidence synthesized in multiple systematic reviews indicates that PWDs tend to have poorer oral health and greater susceptibility to dental caries and periodontal disease compared to their peers without disabilities10,11,12,13,14,15,16. Elevated rates of dental caries and periodontal diseases in this population can be attributed to barriers to accessing oral healthcare. These disparities are shaped by intersecting challenges, including financial limitations, provider-related issues, systemic and infrastructural barriers, and individual patient-related factors16,17,18,19.
Recent reviews highlight that AI positively influences the emotional, social, and practical skills of persons with disabilities20. Sezgin et al.21 highlighted that voice interaction and automatic speech recognition (ASR) in mobile apps are feasible and effective for tracking symptoms and health events at home and more effective for children with disabilities. Lindeman et al.22 discuss various technology-based interventions available to support family caregivers, focusing on health and well-being, social isolation, and psychological support.
These interventions include mobile and cloud solutions, robotics, connected sensors, and virtual/augmented reality, which collectively empower and support caregivers by providing real-time feedback and improving care coordination22,23. Joshi et al.24 developed an AI-driven system using deep learning models for object detection and recognition, achieving high accuracy (95.19% to 99.69%). This system integrates computer vision and sensor-based techniques to provide real-time auditory feedback, enhancing users’ spatial awareness and navigation safety. These findings highlight the transformative potential of AI in healthcare and caregiving, underscoring the importance of integrating AI tools to enhance the quality of care for persons with disabilities.
There is a research gap concerning dentists’ awareness, perception of, and use of RT and AI in oral health and PWDs in Saudi Arabia. Assessing these factors is crucial to enhancing the quality of oral health care for this vulnerable target population by leveraging AI technologies. A Saudi-based study investigated AI/Robotics use among dental professionals25. However, our study will offer a novel contribution by emphasizing its clinical focus on dentists’ perspectives on PWDs. The objectives of this study were twofold: (1) to assess dentists’ perceptions and attitudes toward AI/Robotics use, and (2) to identify predictors of AI/Robotics use among dentists treating PWDs.
Methodology
Study design and study area/settings
A cross-sectional study was conducted using SurveyMonkey as an online questionnaire tool (https://www.surveymonkey.com/r/CRTTJTW) restricted to one participant per unique internet protocol (IP) address. The study area included all non-profit and for-profit organizations/institutions caring for PWDs in different regions of Saudi Arabia. These include governmental and private hospitals, rehabilitative centers such as Kafef, Sultan Bin Abdulaziz Humanitarian City (SBAHC), Saudi Association for Hearing Impairment, medical cities, Prince Sultan Military Medical City (PSMMC), King Saud Medical City (KSMC), private dental clinics, and all National Guard Health Affairs (NGHA) premises, including medical cities, hospitals, outpatient clinics, and primary healthcare clinics (PHCs).
Study subjects, sampling technique, and sample size
This study targeted all Saudi and non-Saudi licensed dentists and dental/oral health practitioners, including general dentists, restorative dentists, periodontists, prosthodontists, endodontists, oral and maxillofacial surgeons, dental pediatricians, dental public health, orthodontists, oral pathologists, dental radiologists, dental hygienists, and dental assistants. Each participant should have an official work status in his or her institution for at least three months before the survey date. All the participants spoke and wrote in English and had internet access, as well as basic skills for using technology applications that allow them to access the study tool. Dentists whose licenses had been revoked or suspended due to professional misconduct or other disciplinary actions during the study period were excluded. All participants with licenses other than the Saudi Commission for Health Specialties (SCHS) were also excluded from the study.
Based on a population of 42,906 registered dentists in Saudi Arabia26, 25.4% use AI and RT by dentists27 and with a confidence interval of 95% and a margin of error of ± 5%, the required sample size was 292. However, the sample size increased to 309 dentists to compensate for incomplete questionnaires. Based on that, a convenient sampling technique was used to select the participants, and an online self-reported questionnaire was available in July 2024. The investigators shared the survey questionnaire with all intended participants physically via e-flyers, including the link and QR code for the key individuals in the intended settings. Selecting participants using the non-probability convenience sampling method might have affected our sample’s representativeness and limited our results’ generalizability.
Study tool
A validated self-reported questionnaire25 was utilized, with minor modifications to tailor it for dentists treating PWDs. A panel of experts performed a preliminary screening through consensus, followed by testing an initial version of the questionnaire on a pilot sample (n = 10) of dentists with diverse years of experience to address ambiguity, confirm comprehension, and assess completion time. In response to their feedback, certain questions were rephrased to enhance clarity. In developing the final questionnaire, we assessed the logical arrangement of items and the anticipated relationships between responses to ensure overall coherence. The tool demonstrated strong internal consistency, evidenced by an overall Cronbach’s alpha of 0.83. Cronbach’s alpha values for perception and attitude were 0.81 and 0.84, respectively. A supplementary file containing the English language version is attached. The questionnaire was distributed via SurveyMonkey to collect data on the following:
A-Demographic and work-related characteristics: This section covered the dentist’s gender, age, education level, nationality, professional specialty, work institution, region of work, years of experience, types of disability, and the average number of patients. It also includes a question that measures the participant’s familiarity with the concept of AI and another question about the participant’s previous training in RT and AI.
B-Perception toward RT/AI use: This section includes nine items; each was responded to on a 4-point Likert scale: strongly disagree = 1, disagree = 2, agree = 3, and strongly agree = 4. Total and percentage mean scores of perception were calculated. The levels of perception were categorized into three categories: positive (≥ 76% score), neutral (50–75% score), and negative (≤ 49% score).
C-Attitude toward using AI and RT in dentistry. This section includes 10 items; each item was responded to by strongly unlikely = 1, unlikely = 2, likely = 3, and strongly likely = 4. Total and percentage mean scores of attitude were calculated. The levels of attitude were categorized into three categories: positive (≥ 76% score), neutral (50–75% score), and negative (≤ 49% score).
D-Current use of RT and AI: This section includes 11 clinical aspects to measure the frequency of dentists’ current use of AI and RT while providing care for PWDs. RT/AI use was considered when the dentist reported using AI or robots for one or more clinical aspects. This section includes 11 clinical aspects to measure the RT or AI use by densities, mainly for those who work with PWDs. Each clinical aspect was responded to by yes = 1 or no = 0, and the RT/AI use rate was calculated for each clinical aspect and each type of impairment.
Participation in this study was entirely voluntary. Dentists who signed electronic informed consent were guaranteed anonymity. They were prompted to complete the survey to see if they had accepted the consent terms. The Ministry of National Guard-Health Affairs’ Institutional Review Board (IRB) authorized the study under reference number NRR24/045/7. This study was carried out following the Helsinki Declaration.
Data analysis
Data entry and statistical analysis were performed with the statistical package for the social sciences (SPSS) software program for Windows (version 29.0.1.1, © Copyright IBM Corporation, Armonk, NY, USA). Descriptive statistics such as percentages, means, standard deviations, and 95% confidence intervals were calculated. The Pearson Chi-square test (X2) and Chi-square test for linear trend (X2LT) were applied for categorical data, and the student-independent t-test was used for continuous data. Logistic regression analysis was used to predict the factors influencing the dentists’ use of RT and AI. Statistical significance was considered at p < 0.05 for all analyses.
Results
Personal and work-related characteristics of dentists
Table 1 shows the personal characteristics of the 309 dentists eligible for the study inclusion and exclusion criteria, representing that 57.6% were male, 54.4% were 30 years of age or older, 64.6% had bachelor’s degrees in dentistry, and 48.9% were dental board-certified. Most study participants worked in governmental institutions (70.2%), and most worked in the central region (67.3%). Of all participants, 57.9% had 1–5 years of experience in the field, and 68.9% worked with PWDs. The results show that only 32% of the participants were familiar with AI concepts, and 21% had previous training (AI = 11.7% and RT = 1.6%, while 7.7% had training in both approaches).
Table 1 Demographic, work-related, and RT/AI-related characteristics of dentists.
Rate of RT/AI current use in different clinical aspects and various types of patient impairment
Figure 1 shows current RT and AI clinical use among dentists who work with PWDs. Of all 213 dentists, 126 (59.2%) reported using RT/AI in different clinical aspects. Nearly one-fourth of dentists reported the use of RT/AI in clinical examination (23.9%), managing complications (26.8%), and invasive procedures (28.6%). Nearly one-third reported using RT/AI for taking history (30%), non-invasive procedures (31.5%), behavioral training sessions (32.9%), health education (36.2%), medical diagnosis (36.6%), diagnostic tests (38%), and treatment plans (43.7%).
Fig. 1
Rate of RT/AI current use in different clinical aspects among Dentist working with persons with disabilities.
Figure 2 shows the rate of RT/AI use by dentists in different types of patient impairments among dentists who work with PWDs. This rate ranged from 51.6% in communication impairment59% to in mental impairment.
Fig. 2
Rate of RT/AI current use in different types of impairments among Dentist working with persons with disabilities.
Levels of perception and attitude toward RT/AI among dentists
Table 2 shows all dentists’ perceptions and attitudes toward RT/AI. Based on the results, most dentists (97.3%) perceived the RT/AI contribution as a tool for increasing career productivity, medical education, and awareness at individual and community levels. More than three-quarters of the study participants (88.0%) agreed or strongly agreed that RT/AI provides quick and accurate predictions for a dental implant site through 3D views during procedures using an integrated simulation system.
Table 2 Dentists’ levels of perception and attitude toward using robot technology and artificial intelligence.
Fig. 3
Levels of Perception and attitude toward the Rt/AI use among Dentist working with persons with disabilities.
The study results showed a variation in the participants’ attitudes. Most participants (83.9%) preferred greater acceptance of the RT/AI technique by university personnel. Approximately three-quarters of the dentists (74.4%) were more likely to recommend treatment using RT/AI. Attractively, 79% of the study participants were likelier to join an RT/AI team training session, and 69.2% preferred training and working in a dental RT clinical simulation-based lab. Nearly one-half of participants (46.3%) favored receiving RT lectures or workshops, 40.8% preferred to provide dental treatment by RT/AI rather than doing it by themselves, and only if needed, and one-third (38.2%) preferred receiving information from a teaching RT to increase their self-confidence compared to the traditional classroom. The overall percent mean score of attitudes to RT/AI use was 69.7 ± 13.7. Nearly one-fourth (28.6%) of dentists reported a positive attitude towards RT/AI use in dentistry, Fig. 3.
Rate of current use of RT/AI and its association with some personal and RT/AI-related characteristics
Table 3 shows that dentists’ overall RT/AI use rate was 59.2%. This rate was significantly higher among those whose works in private settings (73.5%, OR = 2.66, 95% CI 1.31–5.41, p = 0.006), and those who work in both governmental and private (92.3%, OR = 11.53, 95% CI 1.64–90.92, p = 0.004), history of previous RT/AI training (89.8%, OR = 8.80, 95% CI 3.32–23.31, p < 0.001), positive perception towards RT/AI use (66.7%, OR = 3, 95% CI 0.48–18.70, p = 0.006), and positive attitude towards RT/AI use (68.9%, OR = 7.18, 95% CI 2.07–24.95, p = 0.003). However, after adjusting for possible confounders, previous RT/AI training was the only significant predictor of RT/AI use by dentists working with PWDs (OR = 9.18, 95% CI 2.92–28.90, p < 0.001). The levels of perception and attitudes were not independent predictors of the current use of RT/AI in managing PWDs, Table 4.
Table 3 Rate of use of Robot Technology (RT) and Artificial Intelligence (AI) by dentists who work with PWDs.
Table 4 Logistic regression of RT/AI use by Dentists who work with PWDs.
Discussion
Integrating RT/AI-based care interventions in dentistry and oral health effectively and positively influences the quality of care, particularly for PWDs. Our study showed that 59.2% of dentists who worked with PWDs reported using RT/AI in various clinical aspects. Nearly one-fourth of dentists reported using RT/AI during clinical examinations, managing complications, and performing invasive procedures. Almost one-third reported utilizing RT/AI for taking patient histories, non-invasive procedures, behavioral training, health education, medical diagnosis, diagnostic tests, and treatment plans. Half and one-fourth of dentists expressed positive perceptions and attitudes towards RT/AI use in dentistry. However, after adjusting for possible confounders, prior RT/AI training was the only significant predictor of RT/AI use among dentists working with PWDs.
RT/AI innovation transforms oral health care by introducing cutting-edge solutions in various aspects of dental practice28,29,30,31. Despite the lack of literature focusing on using RT/AI by dentists who care for PWDs, several studies highlighted that dentists use RT or AI technology-based clinical intervention for diagnostic purposes or treatment plans32,33,34,35,36,37,38. These findings are compatible with our study results, which indicated that 43.7% of dentists use RT/AI for treatment plans, and 65.2% use it as an assistive tool for diagnostic tests and to obtain an accurate patient history. Others imply that AI-driven diagnostic tools improve dental imaging, automate condition identification, and support collaborative treatment planning6,29,30,39,40,41,42,43,44.
Integrating AI-robotic technology performs precise dental procedures such as implant placements and orthodontic treatments, reduces the patient’s discomfort, and enhances the treatment accuracy45,46. This is compatible with our study results, which indicate that 28.6% of dentists use this notion in invasive procedures. This implies that RT/AI is an effective tool that can benefit the oral and dental health care provided for PWDs. Our study also showed that more than 51% of dentists use RT/AI despite the variation in patient impairments (59% in mental impairment to 51.6% in communication impairment). This implies that integrating RT/AI in dental health and oral care provides more accurate diagnostic results, positively influencing the quality of care dentists offer, mainly for PWDs22.
Health education is one of the key aspects that could optimize the sustainability of oral health, mainly for PWDs, which could be a challenge for dentists. Therefore, integrating the RT/AI use could help them to provide educational materials that meet the needs of this target population based on their abilities. Based on our study, more than 60% of dentists use RT/AI for health education and dental-behavioral training sessions. Such a result is comparable to other studies, which indicate that AI-based educational tools provide feasible and easy personalized oral hygiene instructions and educational materials that promote patients’ adherence to routine oral care management47,48.
Examining the level of knowledge and familiarity of the RT/AI is essential in the context of dental and oral health for PWDs. Our study showed that 53% of the participants were familiar with and had previous training in RT/AI. Several studies aligned with our results, which indicate that knowing and familiarity with RT/AI is essential in enhancing their usability in dental care27,49,50,51,52,53, mainly when providing care for PWDs. However, Roganovi´c et al.54 claimed that only 7.9% of 281 Serbian dentists and dental students knew about AI. Aboalshamat27 highlighted that 49.40% of 389 dentists, postgraduate, and graduate dental students were aware of the concept of AI, though only 18.5% had general training and 20.30% had AI training in dentistry. On the other hand, Abouzaied et al.25 claimed that 90.7% of 628 had heard of RT and AI in dentistry, but only 7% knew the differences between RT and AI. Hamd et al.55claimed that 134 (29.1%) Emirati dentists were aware of and familiar with AI. Compared to our study results, such variation in the evidence in terms of RT/AI knowledge and familiarity by dentists could be justified based on various factors such as the type and level of integrated advanced technology in a country, age, and gender of the participants, years of experience, and target population27,53. This fact emphasizes the urgent need to increase dentists’ knowledge of the variations between RT and AI, mainly when providing care for PDWs.
Investigating the levels of perception and attitude toward utilizing RT/AI in dental and oral healthcare among dentists provides a deeper insight into the care offered to PWDs. Our study highlighted that more than half of the dentists (54.9%) reported a positive perception of RT/AI use in dentistry, and 97.3% perceived RT/AI as a positive tool that increases dentists’ career productivity, medical education, and awareness at individual and community levels. Such facts align with other studies; for instance, Elchaghaby and Wahby53 claimed that 53% of Egyptian dental students have a positive attitude toward using AI in dental care. Aboalshamat27 also claimed that 75.8% of dentists and dental students have a positive attitude toward using AI in dentistry, which is higher than our study results. Further, more than three-quarters of the study participants perceived that RT/AI provides quick and accurate predictions for dental care and treatment plans, facilitates treatment plans, and facilitates patient information storage and data accessibility and usage. Various studies reflect similar views and highlight the need for AI to facilitate dental healthcare27,45,48,53,56. Murali et al.57 indicated that 88.47% of dentists agreed that AI could diagnose and facilitate treatment plans; 77.82% reported the feasibility of AI for early detection of oral cancer; 74.13% reported that AI helps in forensic dentistry, and 80.65% reported that AI could be used as a prognostic and quality control tool. Based on such views, it is logical to assume that RT/AI use is perceived as an effective tool for PWDs, mainly when providing complex procedures such as invasive ones, considering our result.
The principles of ethics are essential for directing ethical decision-making in both clinical and nonclinical AI applications within dentistry58. Dental professionals must elucidate the processes by which AI generates particular diagnoses and treatment recommendations, enabling patients to comprehend and assess the foundations of their treatment choices. Data privacy is crucial; dental providers must honor patients’ decisions and viewpoints regarding the use of AI and secure consent when implementing AI technologies59. Most participants in our study favoured increased acceptance of the RT/AI technique among university personnel and were more inclined to endorse treatment utilizing RT/AI. Dental professionals must assess AI systems to reduce the likelihood of errors, misdiagnosis, or unsuitable treatment. AI technologies should enhance patient outcomes, improve treatment efficacy, and optimize care delivery. Over 75% of study participants concurred or strongly concurred that RT/AI delivers rapid and precise predictions for dental implant sites via 3D visualizations during procedures utilizing an integrated simulation system. Large language models can exacerbate algorithmic bias, necessitating dental care professionals to evaluate AI training model data sets to ensure transparency and equitable patient outcomes60. Dental care professionals must deliver precise information to patients concerning the capabilities, limitations, and potential risks linked to AI technologies59. In our study, fewer than two-thirds of dentists concurred that implementing robotic systems could diminish potential treatment errors and enhance the quality of endodontic procedures.
Our study revealed that most dentists perceived that AI could permanently replace them, which is compatible with other studies49,57. It is essential to align such a concern with the basic needs of maintaining autonomy and decreasing the stress level of PWDs during dental visits61,62,63,64,65. Several physical or non-physical barriers prevent PWDs from accessing dental services, diminishing the quality of dental care provided19,61,62,63,64,66. Our study indicated that previous training is a core predictor for dentists’ use of RT/AI. Therefore, there is a need to increase RT/AI training among dentists, as indicated in our study results, which revealed the importance of professional RT/AI training in enhancing RT/AI use (68.7%). Lacking standardized dental care AI courses and time are the most common barriers to dental AI use (73% and 68.9%, respectively). Several studies highlight the importance of increasing dentists’ and other healthcare providers’ RT/AI skills through standardized training courses25,28,48,67.
Strengths and limitations
This study provides insights into the perceptions and attitudes of dentists in Saudi Arabia toward RT and AI and their current use in dentistry to manage PWDs. It is the first in the Saudi context to examine the use of RT and AI by dentists caring for PWDs. However, the study has some limitations. The study primarily relied on self-reported data, which can be subject to recall bias and may not accurately reflect the participants’ knowledge. The causal relationship between dentists’ use of RT/AI and its predictors was not guaranteed because of the study’s cross-sectional nature. Selecting participants using the non-probability convenience sampling method might have affected our sample’s representativeness and influenced the findings, particularly RT/AI usage estimates, and limited generalizability to the broader dental workforce in Saudi Arabia. Moreover, the potential influence of institution type (governmental vs. private) on RT/AI exposure and implementation would be considered, especially given the sample’s skew toward public-sector dentists. The sample includes many professionals (dentists, hygienists, assistants, specialists). Yet, the analysis does not fully account for potential differences in exposure to RT/AI by specialty or clinical role. Subgroup or interaction analyses were not feasible due to sample size constraints; thus, the study’s conclusion could have been subject to potential residual confounding. Further, the wide confidence intervals observed in some estimates may reflect limited sample size and data variability, and caution should be taken in interpreting these results. The potential for social desirability bias should be considered, as dentists may have overstated their familiarity or use of RT/AI to present themselves more favorably. The risk of non-response bias is to be highlighted; dentists unfamiliar or uncomfortable with digital tools may have been underrepresented. Moreover, the online-only distribution method (Survey with IP restrictions) may have excluded participants with limited internet access or lower digital literacy. Future research is therefore needed to validate these findings with more extensive, diverse, and randomly selected participants.
Conclusion
Our study is the first in the Saudi context to examine the use of RT and AI by dentists caring for PWDs. The study findings show that more than half of dentists working with PWDs use RT/AI in dentistry in different clinical aspects to manage patients with different types of impairments. Generally speaking, the dentists reported neutral perceptions and attitudes toward using RT and AI in the field. Over half and one-fourth of dentists reported a positive perception and attitude toward RT/AI use in dentistry. However, these levels of perception and attitudes were not independent predictors of the current use of RT/AI in the management of PWDs. Previous RT/AI training was the only significant predictor of RT/AI use by dentists working with PWDs. There is a need for AI to receive more attention in dental education by including the topic in undergraduate, postgraduate, and even continuing education curricula to align with the expected digital advancements in the dental arena.
Promoting RT and AI inside dental education and curriculum, and ensuring a legal and ethical basis, may be considered key elements to the success of the RT and AI industry. Potential collaborations between dental institutes and stakeholders, as well as adding all stakeholders to the development process about the RT and AI industry to explore those technologies deeply and demonstrate the impacts of RT and AI models in dentistry practice, might also be highlighted.
Data availability
Most of the data supporting our findings is contained within the manuscript, and all others, excluding identifying/confidential patient data, will be shared upon request from Mostafa Abolfotouh mabolfotouh@gmail.com.
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