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Artificial Intelligence in Public Health, Healthcare Services, and Management
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Artificial Intelligence in Public Health, Healthcare Services, and Management

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Healthcare".

Deadline for manuscript submissions: closed (31 July 2026) | Viewed by 5925

Editors

Dr. Obinna O. Oleribe

E-Mail Website
Guest Editor
Department of Health Sciences, School of Public Health and Health Sciences, College of Health, Human Services and Nursing, California State University, Dominguez Hills 1000 E. Victoria Street, Carson, CA 90747, USA
Interests: transitional sciences; innovation diffusion in health; health system improvement
Dr. Florida Uzoaru

E-Mail Website
Guest Editor Assistant
Department of Health and Human Sciences, Southeastern Louisiana University, Hammond, LA 70402, USA
Interests: health equity; health policy evaluation; public health innovation; behavioral economics in public health; neurodevelopmental disorders

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) stands as the third major revolution of the 21st century, after personal computers and the Internet. It is rapidly reshaping the global landscape of public health and healthcare systems. While health is one of the most active frontiers in this revolution, important questions remain: What tangible progress has been made? How effective and efficient is AI in real-world applications? What barriers do health leaders and managers face in adopting, integrating, and scaling AI technologies? And critically, what roles can AI play that we have not yet fully explored?

To harness the full potential of this revolution, we must understand the current landscape—who is leading innovation, where are breakthroughs happening, and what challenges or gaps are hindering progress? We must also explore new, context-specific solutions to accelerate responsible and equitable AI adoption in healthcare.

This Special Issue aims to illuminate the diverse and evolving roles of AI across public health surveillance, clinical decision-making, service delivery, leadership and management, policy implementation, and workforce transformation. We seek to feature interdisciplinary research and applied case studies (basic, operational, and translational) that deepen our understanding of how AI can improve outcomes, boost efficiency, and reduce disparities in healthcare systems worldwide.

We invite original research, reviews, and case reports that provide theoretical insights, practical solutions, ethical reflections, and policy recommendations. Contributions highlighting innovations in low- and middle-income countries (LMICs), underserved communities, and global health systems are especially welcome. Research areas may include (but are not limited to) the following:

  • AI in public health practice, decision-making, and leadership;
  • Disease surveillance and outbreak prediction;
  • Predictive modeling and risk stratification;
  • GenAI in diagnostics, imaging, and EHRs;
  • AI-driven patient engagement and behavior change;
  • Workflow automation and service optimization;
  • Ethics, bias, and equity in AI;
  • Policy frameworks and system-level integration;
  • Case studies, especially from LMICs and resource-constrained settings.

Join us in exploring what is next for AI in health. We look forward to hearing from you.

Dr. Obinna O. Oleribe
Guest Editor

Dr. Florida Uzoaru
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • generative AI (GenAI)
  • public health innovation
  • healthcare management
  • predictive analytics
  • health systems optimization
  • digital health transformation
  • ethics and AI in healthcare
  • global health and equity
  • AI in low- and middle-income countries (LMICs)

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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27 pages, 588 KB  
Article
Determinants of AI Adoption in Saudi Arabian Healthcare Institutions
by Saeed Ali Al-Shahrani, Zahyah H. Alharbi and Tahani Alqurashi
Healthcare 2026, 14(13), 1833; https://doi.org/10.3390/healthcare14131833 - 24 Jun 2026
Viewed by 575
Abstract
Background/Objectives: Artificial Intelligence (AI) integration in healthcare promises improved diagnostic accuracy, patient safety, and operational efficiency. However, AI acceptance among healthcare workers remains limited due to knowledge gaps, risk concerns, and governance challenges, particularly in developing countries like Saudi Arabia, where rapid healthcare [...] Read more.
Background/Objectives: Artificial Intelligence (AI) integration in healthcare promises improved diagnostic accuracy, patient safety, and operational efficiency. However, AI acceptance among healthcare workers remains limited due to knowledge gaps, risk concerns, and governance challenges, particularly in developing countries like Saudi Arabia, where rapid healthcare modernization faces unique infrastructure, organizational, and cultural challenges. This research investigates the factors influencing AI acceptance among medical practitioners, nurses, administrators, and students in Saudi Arabian hospitals to identify key determinants and barriers to adoption. Methods: This cross-sectional study employed an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework integrated with ethical considerations from the Model for Ethical Assessment and Analysis of AI in Medicine (MEAAM). A structured bilingual questionnaire was administered to 119 healthcare professionals and students across Saudi Arabia, measuring constructs including Awareness and Knowledge, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Trust, Perceived Risk, Ethical Governance, and Price Value. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for quantitative analysis, supplemented by thematic analysis of open-ended qualitative responses. Results: The PLS-SEM analysis explained 59.8% of variance in behavioral intention to adopt AI (R2 = 0.598). Awareness and Knowledge emerged as the strongest predictor (β = +0.505, p < 0.001), followed by Performance Expectancy (β = +0.229, p < 0.05) and Social Influence (β = +0.123). Perceived Risk functioned as the primary barrier (β = −0.185, p < 0.05). Qualitative findings identified infrastructure gaps, regulatory ambiguities, and training deficiencies as major implementation barriers, while emphasizing opportunities in diagnostic accuracy and remote monitoring. Conclusions: AI acceptance in Saudi healthcare is primarily driven by knowledge, with perceived usefulness and peer support as secondary facilitators, while safety and accountability concerns remain substantial obstacles. Successful AI integration requires coordinated efforts in education, transparent governance frameworks, and institutional support. This study contributes theoretically by validating extended UTAUT in a non-Western healthcare context and practically by providing evidence-based strategies for sustainable AI adoption that enhance healthcare quality while respecting professional roles and ethical principles. Full article
(This article belongs to the Special Issue Artificial Intelligence in Public Health, Healthcare Services, and Management)
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Review

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26 pages, 796 KB  
Review
Clinical AI Beyond Development: A Scoping Review of Deployment-Related Robustness, Algorithmovigilance, and Lifecycle Oversight
by Rabie Adel El Arab, Mohammad Hussein Mustafa, Wesam Taher Almagharbeh, Mohammad Yahya Ayoub, Fatimah Alsanawi, Fulwa Almathen, Rawan Almosabeh and Magfrah Al Talaq
Healthcare 2026, 14(14), 2052; https://doi.org/10.3390/healthcare14142052 - 8 Jul 2026
Viewed by 388
Abstract
Background/Objectives: Clinical artificial intelligence (AI) is increasingly moving from proof-of-concept development into clinical evaluation, regulatory review, and routine care. This scoping review aimed to map and synthesise empirical evidence on clinical AI evaluation after model development, focusing on deployment-related robustness, post-development monitoring, and [...] Read more.
Background/Objectives: Clinical artificial intelligence (AI) is increasingly moving from proof-of-concept development into clinical evaluation, regulatory review, and routine care. This scoping review aimed to map and synthesise empirical evidence on clinical AI evaluation after model development, focusing on deployment-related robustness, post-development monitoring, and lifecycle oversight in practice. Methods: We conducted a scoping review in accordance with Joanna Briggs Institute guidance and reported findings using PRISMA-ScR. MEDLINE, Embase, Scopus, and Web of Science Core Collection were searched with no lower date restriction within each database’s available indexed coverage and with a common upper search date of 28 February 2026. Searches were supplemented by backward and forward citation tracking. Grey literature, preprint servers, and regulatory databases were not systematically searched because eligibility was restricted to full-text, peer-reviewed empirical studies and empirically grounded implementation or monitoring reports. Findings were synthesised using descriptive evidence mapping and inductive thematic synthesis. Results: Eighteen studies or empirically grounded reports were included. Evidence was organised into five strata: direct live or post-deployment monitoring studies; near-live bridge studies generating prospective outputs without guiding care; methodological monitoring and maintenance studies; deployment-relevant robustness and predeployment safety studies; and governance, implementation, readiness, and human-factors studies. Three themes emerged: trustworthiness after development was conditional and context-dependent; algorithmovigilance extended beyond aggregate performance tracking to include operational, workflow, fairness, contextual, and user-feedback signals; monitoring was more actionable when linked to corrective pathways, governance structures, and institutional readiness. Sociotechnical failures included automation-bias signals, workflow burden, reasoning–conclusion misalignment, and workflow-fit problems. Conclusions: Post-development clinical AI evaluation remains a layered and emerging field rather than a mature monitoring literature. Direct live evidence is limited, concentrated in high-income settings, and weighted towards radiology. The findings should be interpreted as synthesis-informed rather than as empirically validated standards for lifecycle oversight. Full article
(This article belongs to the Special Issue Artificial Intelligence in Public Health, Healthcare Services, and Management)
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25 pages, 10954 KB  
Review
The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects
by Yuqi Liu
Healthcare 2026, 14(14), 2031; https://doi.org/10.3390/healthcare14142031 - 8 Jul 2026
Viewed by 407
Abstract
Background/Objectives: The application of artificial intelligence (AI) in the healthcare field continues to deepen, with the development paradigm gradually shifting from technology-driven innovation to design-driven innovation towards “human-centered artificial intelligence (HCAI).” This aims to bridge the potential of AI technology with actual [...] Read more.
Background/Objectives: The application of artificial intelligence (AI) in the healthcare field continues to deepen, with the development paradigm gradually shifting from technology-driven innovation to design-driven innovation towards “human-centered artificial intelligence (HCAI).” This aims to bridge the potential of AI technology with actual clinical needs and improve the quality and accessibility of healthcare services. However, it still faces challenges such as insufficient integration of theoretical frameworks, complex implementation challenges, and an imperfect ethical governance mechanism. Methods: This article presents a systematic narrative review about the philosophical and ethical foundations of HCAI related literature, analyzes the specific clinical application models recorded in the literature, integrates key theories related to implementation science, human–machine collaboration, and explainable AI(XAI), and constructs a multidimensional comprehensive analysis framework for HCAI in the medical field. Results: The study shows that design-driven innovation is the key to bridging the gap between the potential of AI technology and practical medical applications; the successful implementation of “human-centered artificial intelligence” relies on interdisciplinary collaboration, stakeholder co-creation, and ethical considerations throughout the entire lifecycle; Among them, human-centered design ensures that technology meets real needs; Implementation Science guarantees innovation can effectively integrate into complex medical environments; explainable AI technology is the cornerstone of establishing clinical trust; the strategic governance framework sets boundaries and tracks for the healthy development of the entire ecosystem. Conclusions: Beyond summarizing existing research findings, this study proposes targeted design frameworks and trade-off strategies for key technical and practical dilemmas of HCAI. It also clarifies the contextual boundaries of existing empirical results, provides a differentiated operational path for the implementation of HCAI, as well as a clear direction and important reference for academic research and future practical applications of human-centered AI medicine. Full article
(This article belongs to the Special Issue Artificial Intelligence in Public Health, Healthcare Services, and Management)
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Other

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35 pages, 1117 KB  
Systematic Review
Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review of Validation, Explainability, and Implementation Readiness
by Seungmi Kim, Myung-Jun Shin, Byung Kwan Choi, Zoran Obradovic, Daniel J. Rubin and Jong-Hwan Park
Healthcare 2026, 14(11), 1543; https://doi.org/10.3390/healthcare14111543 - 1 Jun 2026
Viewed by 751
Abstract
Background and Objectives: Frailty is a multidimensional vulnerability in older adults; the Fried phenotype and Frailty Index are clinically informative but labor-intensive, limiting scalability for community screening. Machine learning (ML) can model heterogeneous, high-dimensional data, but real-world adoption is constrained by heterogeneity in [...] Read more.
Background and Objectives: Frailty is a multidimensional vulnerability in older adults; the Fried phenotype and Frailty Index are clinically informative but labor-intensive, limiting scalability for community screening. Machine learning (ML) can model heterogeneous, high-dimensional data, but real-world adoption is constrained by heterogeneity in definitions, predictors, validation strategies, and explainability. We systematically synthesized ML-based studies of frailty prediction and classification in community-dwelling older adults, examining validation rigor, explainability, and implementation readiness. Methods: This systematic review followed PRISMA 2020 and was registered in PROSPERO (CRD420251081555). PubMed, Embase, Web of Science, and Scopus were searched on 4 July 2025, with a supplementary IEEE Xplore and ACM Digital Library search conducted on 12 May 2026. Eligible studies included community-dwelling adults aged ≥60 years, ML-based frailty prediction or classification, sample ≥ 1000, and publication in a peer-reviewed journal indexed in the Web of Science Core Collection; hospital-based studies were excluded. Risk of bias and reporting quality were assessed with PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis); implementation readiness was assessed with the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework and a Technology Readiness Level (TRL)-style rubric. Findings were synthesized narratively. Results: Fourteen studies (development cohorts 1230–86,133 participants) were included; the supplementary IEEE/ACM search identified 42 records but yielded no additional eligible studies. Classification of current frailty status (n = 7) yielded AUROCs (area under the receiver operating characteristic curve) of 0.70–0.98, with the highest values likely reflecting partial label overlap with frailty components; incident prediction (n = 6) yielded internal AUROCs of 0.70–0.81 and same-cohort temporal AUROCs of 0.58–0.85; independent external validation was uncommon. Only 2 of 14 studies had both low overall risk of bias and low applicability concern (PROBAST); the field is concentrated at TRL 4–6, with no study at TRL 7 or higher and none documenting Implementation or Maintenance domains of RE-AIM. Conclusions: ML-based frailty models show heterogeneous discrimination and limited readiness for routine community use. Priorities include standardized task-type-specific definitions, independent external validation, calibration and decision-curve reporting, transparent predictor disclosure, and prospective implementation evaluation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Public Health, Healthcare Services, and Management)
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19 pages, 907 KB  
Perspective
Transforming Public Health Practice with Artificial Intelligence: A Framework-Driven Approach
by Obinna O. Oleribe, Florida Uzoaru, Adati Tarfa, Olabiyi H. Olaniran and Simon D. Taylor-Robinson
Healthcare 2026, 14(3), 385; https://doi.org/10.3390/healthcare14030385 - 3 Feb 2026
Cited by 2 | Viewed by 2310
Abstract
Background: The emergence of artificial intelligence (AI) has triggered a global transformation, with the healthcare sector experiencing significant disruption and innovation. In current public health practice, AI is being deployed to power various aspects of public functions, including the assessment and monitoring of [...] Read more.
Background: The emergence of artificial intelligence (AI) has triggered a global transformation, with the healthcare sector experiencing significant disruption and innovation. In current public health practice, AI is being deployed to power various aspects of public functions, including the assessment and monitoring of health, surveillance and disease control, health promotion and education, policy development and planning, health protection and regulation, prevention services, workforce development, community engagement and partnerships, emergency preparedness and response, and evaluation and research. Nevertheless, its use in leadership and management, such as in change management, process development and integration, problem solving, and decision-making, is still evolving. Aim: This study proposes the adoption of the Public Health AI Framework to ensure that inclusive data are used in AI development, the right policies are deployed, and appropriate partnerships are developed, with human-relevant resources trained to maximize AI potential. Implications: AI holds immense potential to reshape public health by enabling personalized interventions, democratizing access to actionable data, supporting rapid and effective crisis response, advancing equity in health outcomes, promoting ethical and participatory public health practices, and strengthening environmental health and climate resilience. Achieving this goal will require a deliberate and proactive leadership vision, where public health leaders move beyond passive adoption to collaborate with AI specialists to co-create, co-design, co-develop, and co-deploy tools and resources tailored to the unique needs of public health practice. Call to action: Public health professionals can co-innovate in shaping AI evolution to ensure equitable, ethical, and value-based public health. Full article
(This article belongs to the Special Issue Artificial Intelligence in Public Health, Healthcare Services, and Management)
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