Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 3rd Edition

Topic Information

Dear Colleagues,

Since the COVID-19 pandemic, we have witnessed a profound and lasting increase in scientific and societal interest in the development and application of Artificial Intelligence (AI) in the health domain. The pandemic served as a pivotal a turning point, exposing the fragility of health systems while underscoring the critical importance of data-driven, scalable, and adaptive solutions for managing complex public health challenges.

In this context, AI research has become strategically vital in the evolution of health systems and is inextricably linked to the broader development of digital health. This includes the collection, monitoring, integration, and management of health-related data, as well as the optimization of hospital infrastructures and interconnected governmental information systems. Technologies such as wearable monitoring devices, big data analytics, and robotics have already demonstrated their value in supporting diagnosis, treatment, and care pathways.

At the same time, the scope and capabilities of AI have expanded significantly beyond traditional machine learning and pattern recognition. Recent advances—particularly in generative AI and large language models (LLMs)—are transforming how knowledge is produced, accessed, and applied. These technologies are reshaping scientific communication, clinical documentation, citizen engagement, and decision-making processes, enabling new forms of interaction between individuals, professionals, and health systems.

Simultaneously, the emergence of computational modeling and digital twins offers innovative perspectives for simulating biological processes, patient-specific conditions, and even population-level dynamics. These tools enable the exploration of “what-if” scenarios, supporting prevention strategies, policy design, and system-level resilience.

Artificial intelligence applications continue to permeate a wide range of domains, including: organ, functional tissue, and cellular diagnostics; care robotics assisting in interventions, rehabilitation, and communication; biomedicine, from genetics to advanced modeling; and precision and personalized medicine. These established areas remain fundamental, especially when integrated with new AI paradigms and translated into scalable solutions.

A statement attributed to Henry Ford reminds us that “real progress happens only when the advantages of a new technology become available to everybody”. In this sense, the consolidation of AI technologies in health should aim to generate benefits for all stakeholders—from institutions to professionals, and ultimately to citizens and patients—promoting accessibility, inclusiveness, and equity of care.

Within this evolving landscape, public health plays a central and unifying role. AI has the potential to profoundly impact the following:

• The prevention of disease onset at both individual and population levels;
• The provision of personalized care and assistance within community and system contexts;
• The understanding of disease trends and the influence of biological, environmental, and behavioral determinants;
• The organization and optimization of healthcare services, including diagnostic and decision-making processes;
• The design, implementation, and evaluation of public health policies.

Through AI, big data can be leveraged to predict diseases and identify risk patterns across populations, enabling early interventions and targeted prevention strategies. At the same time, wearable and connected technologies allow continuous monitoring of individuals, supporting adaptive and personalized care pathways that can be scaled to population-level insights.

Furthermore, the synergy between AI immersive technologies—such as virtual reality and augmented reality—is pioneering new forms of digital health services, including remote care, training, rehabilitation, and simulation environments. These innovations contribute not only to clinical practice but also to public health preparedness and education.

However, the rapid expansion of AI also raises critical ethical, legal, and societal challenges, including issues related to transparency, accountability, bias, data protection, and governance. In addition, there is an increasing need for interdisciplinary education and training to equip professionals with the skills required to apply these technologies effectively and responsibly.

This Topic provides a comprehensive platform to explore both consolidated applications and emerging frontiers of AI in health, with a particular emphasis on public health perspectives, while remaining open to interdisciplinary and clinical contributions that demonstrate clear implications for population health, health systems, and societal impact.

We invite contributions that address the research, development, validation, and application of AI in current and future scenarios, fostering dialogue between technological innovation and public health needs.

In this Topic, original research articles, reviews, commentaries, opinions, viewpoints, communications, and brief reports are welcome. Research areas may include (but are not limited to) the following:

• Artificial neural networks
• Deep learning
• Generative AI and large language models (LLMs)
• Natural language processing
• Social intelligence
• Digital twins and computational modeling
• Big data and real-world data analytics
• Care robotics
• Virtual reality and augmented reality
• Medical decision making
• Disease monitoring, prediction, diagnosis, and classification
• Patient monitoring and wearable technologies
• Public health surveillance and epidemiology
• Health system organization and management
• Diagnostic imaging
• Digital pathology
• Digital radiology
• Ethical, legal, and social implications of AI
• Education and training in AI for health and public health

We look forward to receiving your contributions.

Prof. Dr. Daniele Giansanti
Dr. Giovanni Costantini
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800	Submit
Bioengineering bioengineering	4.4	7.5	2014	16.9 Days	CHF 2700	Submit
Clinics and Practice clinpract	2.8	3.5	2011	24 Days	CHF 1800	Submit
Healthcare healthcare	3.4	5.5	2013	21.5 Days	CHF 2700	Submit
International Journal of Environmental Research and Public Health ijerph	-	9.8	2004	24 Days	CHF 2500	Submit
Journal of Clinical Medicine jcm	3.3	5.2	2012	16.6 Days	CHF 2600	Submit
Journal of Imaging jimaging	3.8	7.3	2015	21.3 Days	CHF 1800	Submit
Medical Sciences medsci	5.9	4.6	2013	18.3 Days	CHF 1600	Submit

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Published Papers (5 papers)

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All Bioengineering Clinics and Practice Healthcare J. Imaging JCM AI Medical Sciences IJERPH

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23 pages, 981 KB  

Open AccessReview

From Optical to AI-Driven Markerless Motion Capture in Motor Learning and Rehabilitation

by Panagiotis Georganakis, Konstantinos Spinthiropoulos, Konstantinos Panitsidis, Dimitrios Parris and Vasiliki Gerodimou

Bioengineering 2026, 13(7), 776; https://doi.org/10.3390/bioengineering13070776 - 3 Jul 2026

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Abstract

Traditional biomechanical analysis is constrained by high capital costs and the physical limitations imposed by markers, posing significant barriers to clinical adoption. This review evaluates the emergence of artificial intelligence (AI)-based markerless motion capture (MMC) as a transformative approach for democratizing movement science [...] Read more.

Traditional biomechanical analysis is constrained by high capital costs and the physical limitations imposed by markers, posing significant barriers to clinical adoption. This review evaluates the emergence of artificial intelligence (AI)-based markerless motion capture (MMC) as a transformative approach for democratizing movement science in clinical rehabilitation. The discussion outlines the progression from legacy geometric visual hulls to advanced deep learning architectures, with particular focus on YOLO-based two-dimensional detection and spatio-temporal transformer models for three-dimensional pose estimation. Evidence indicates that multi-camera MMC frameworks achieve research-grade positional accuracy (16–34 mm Mean Per-Joint Position Error—MPJPE), while monocular systems provide sufficient sensitivity (82–88%) for longitudinal monitoring of geriatric fall risk and stroke recovery. While challenges persist in achieving precise axial rotation measurement, integrating real-time signal refinement enables objective and ecologically valid assessments in community-based healthcare settings. This technological advancement redefines movement analysis, shifting it from a laboratory-bound procedure to a widely accessible and interoperable diagnostic tool. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 3rd Edition)

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26 pages, 385 KB  

Open AccessReview

Radiomics in Lung Cancer Imaging: A Narrative Review of Current Evidence

by Andrea Lastrucci, Nicola Iosca, Edoardo Cavigli, Diletta Cozzi, Angelo Barra, Yannick Wandael, Cosimo Nardi, Renzo Ricci, Vittorio Miele and Daniele Giansanti

J. Imaging 2026, 12(7), 287; https://doi.org/10.3390/jimaging12070287 - 29 Jun 2026

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Abstract

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, and early diagnosis and accurate disease stratification are still major clinical challenges. Radiomics has emerged as a quantitative imaging approach that extracts high-dimensional features from radiological imaging, with applications in diagnosis, prognosis, [...] Read more.

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, and early diagnosis and accurate disease stratification are still major clinical challenges. Radiomics has emerged as a quantitative imaging approach that extracts high-dimensional features from radiological imaging, with applications in diagnosis, prognosis, radio genomics, and assessment of treatment response. However, its clinical translation is still limited by methodological heterogeneity and a lack of standardization. Aim: This narrative review synthesizes evidence from systematic reviews and meta-analyses on radiomics in thoracic imaging for lung cancer, focusing on clinical applications, methodological limitations, and translational challenges. Methods: A structured search was conducted in PubMed and Scopus using predefined keywords related to radiomics, lung cancer, and imaging modalities. Only peer-reviewed systematic reviews and meta-analyses published in English were included. In total, 27 studies were selected and synthesized using a structured narrative approach guided by the ANDJ checklist. A differential integrative framework was adopted to connect evidence from systematic reviews and meta-analyses with primary empirical studies and policy documents through an intermediate layer of translational recommendations, ensuring a multi-level and interpretation-driven synthesis. Results: Radiomics demonstrated consistent potential across multiple clinical domains, including lesion classification, histological differentiation, molecular profiling, prognostic stratification, and prediction of treatment response. Machine learning and deep learning approaches frequently improved predictive performance. However, key limitations were identified, including heterogeneity in imaging protocols, lack of external validation, small single-centre datasets, and limited reproducibility of radiomic features. Conclusions: Radiomics in lung cancer imaging shows strong clinical potential but remains constrained by methodological and translational barriers. Future progress will depend on standardization, external validation, multimodal data integration, and improved interpretability, alongside alignment with regulatory and clinical implementation frameworks. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 3rd Edition)

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29 pages, 1183 KB  

Open AccessReview

Conditional Acceptance and the Optimism–Knowledge Gap: A Scoping Review of Attitudes and Perceptions of Artificial Intelligence in Healthcare in Italy

by Christian J. Wiedermann, Giuliano Piccoliori, Doris Hager von Strobele Prainsack and Dietmar Ausserhofer

Med. Sci. 2026, 14(2), 276; https://doi.org/10.3390/medsci14020276 - 28 May 2026

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Abstract

Background/Objectives: Artificial intelligence (AI) is integrated into diagnostic, therapeutic, administrative, and communicative healthcare domains in Italy under regulations requiring human oversight. Empirical evidence on AI attitudes, acceptance, and perceptions in Italian healthcare is rapidly accumulating but not systematically mapped. This scoping review [...] Read more.

Background/Objectives: Artificial intelligence (AI) is integrated into diagnostic, therapeutic, administrative, and communicative healthcare domains in Italy under regulations requiring human oversight. Empirical evidence on AI attitudes, acceptance, and perceptions in Italian healthcare is rapidly accumulating but not systematically mapped. This scoping review aimed to (i) map empirical evidence on AI attitudes, acceptance, and perceptions in Italy by population and domain; (ii) identify measurement instruments used in studies and their origins; and (iii) characterize determinants, themes, and methodological gaps in the Italian evidence base. Methods: This review used Joanna Briggs Institute methodology, reported via PRISMA-ScR (protocol Open Science Framework doi: 10.17605/OSF.IO/TZRVF). PubMed and Embase were searched on 27 April 2026 from January 2018 in English, Italian, or German, combining controlled vocabulary and free-text terms across AI, attitudes-acceptance, and healthcare delivery, with an Italian-context qualifier; a complementary AI-assisted semantic search (Consensus Pro) was conducted to validate retrieval completeness. Eligibility criteria used the Population–Concept–Context mnemonic. Results: Of 1510 unique records screened, 35 empirical studies were retained, comprising 7 studies of Italian patients and the general population, 22 studies of healthcare professionals, 3 psychometric validation studies of AI-acceptance instruments, 1 mixed-population study, and 2 international comparator studies with substantial Italian sub-samples. Acceptance was consistently positive but conditional on physician oversight, training, and regulatory clarity. A recurrent optimism–knowledge gap and an absence of probabilistic, population-representative evidence were identified as principal gaps. Conclusions: Italian evidence on AI attitudes is expanding but methodologically narrow. Three Italian-validated acceptance instruments are now available. Population-representative, multilingual, and longitudinal evidence is required. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 3rd Edition)

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22 pages, 4604 KB  

Open AccessReview

Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care

by Charat Thongprayoon, Francesco Pesce and Wisit Cheungpasitporn

Clin. Pract. 2026, 16(6), 101; https://doi.org/10.3390/clinpract16060101 - 27 May 2026

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Abstract

Rare diseases affect over 300 million individuals worldwide yet remain underdiagnosed and poorly characterized due to fragmented data, small cohorts, and phenotypic heterogeneity. Advances in artificial intelligence (AI) are enabling integration of genomics, imaging, electronic health records, and patient-generated data to support diagnosis, [...] Read more.

Rare diseases affect over 300 million individuals worldwide yet remain underdiagnosed and poorly characterized due to fragmented data, small cohorts, and phenotypic heterogeneity. Advances in artificial intelligence (AI) are enabling integration of genomics, imaging, electronic health records, and patient-generated data to support diagnosis, phenotyping, prognosis, and therapeutic discovery. In kidney care, these capabilities are reflected in tools for genomic variant prioritization, AI-assisted histopathology, and integrated risk stratification models for rare and complex kidney diseases. This review synthesizes current AI applications across the rare disease continuum and proposes a clinically grounded framework to distinguish exploratory models from systems that are methodologically robust and operationally deployable. We highlight advances that address data sparsity and heterogeneity, alongside persistent challenges in validation, generalizability, equity, and workflow integration. Finally, we outline future directions, including federated learning, digital twins, and AI-driven clinical decision agents, as pathways toward precision-guided, workflow-integrated rare disease care. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 3rd Edition)

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21 pages, 2117 KB  

Open AccessArticle

Machine Learning-Based Survival Prediction in Early-Stage Non-Small Cell Lung Cancer: Development and Cross-National External Validation

by Nikhil Joshi, Hari Ponnamma Rani, Maxim Shevtsov and Thyageshwar Chandran

J. Clin. Med. 2026, 15(10), 3701; https://doi.org/10.3390/jcm15103701 - 11 May 2026

Viewed by 606

Abstract

Background: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. However, prognostic models developed within a specific population may not be accurate when applied to another population due to differences in demographics and clinical practices. In the present study, we [...] Read more.

Background: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. However, prognostic models developed within a specific population may not be accurate when applied to another population due to differences in demographics and clinical practices. In the present study, we investigated the cross-national applicability of machine learning (ML)-based survival prediction models trained on population data from the United States and validated on an independent Chinese clinical cohort. Methods: Cox proportional hazards, Random Survival Forest (RSF), and XGBoost-Cox models were developed and externally validated. Model discrimination was evaluated using the concordance index (C-index) and time-dependent AUC at 1, 3, and 5 years, along with calibration and decision curve analysis. Hyperparameter tuning was performed using cross-validation to reduce overfitting and improve model generalizability. Results: Three survival prediction models were developed using the U.S. SEER database (n = 13,260) and externally validated in an independent Chinese cohort (n = 505). Baseline characteristics differed between the cohorts, with the Chinese cohort being younger and having a higher proportion of stage IA disease. Despite these differences, all models demonstrated acceptable discrimination. The RSF model was the most stable across cohorts and time horizons, with a C-index of 0.740 (95% CI: 0.735–0.746) in SEER and 0.782 (95% CI: 0.720–0.844) in the Chinese cohort. RSF showed good calibration at 1 and 3 years but slightly overestimated 5-year mortality risk in the Chinese cohort. Conclusions: Machine learning-based survival prediction models, such as the Random Survival Forest model, are promising and robust tools for predicting cross-population survival in early-stage non-small cell lung cancer (NSCLC). However, differences in patient characteristics and treatment patterns may influence long-term model performance. These findings highlight the potential of flexible machine learning models in oncology and the essential role of rigorous external validation. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 3rd Edition)

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