Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition

Topic Information

Dear Colleagues,

Due to the COVID-19 pandemic, we are witnessing a growing scientific interest in the development and application of artificial intelligence in the health domain. Research in this area is strategic for the development of health systems and is inextricably linked to the development of digital health, both as regards the collection, -monitoring and management of information, and as regards the management of hospital and connected government information systems. Think, for example, of the opportunities presented by wearable monitoring, big data, and robotic surgery. The applications of artificial intelligence have received growing interest in many sectors, such as: organ, functional tissue and cell diagnostics; care robotics, assisting in interventions, rehabilitation and supporting the communication and assistance of disabled people; the biomedicine sector, from genetics to modeling; and precision and personalized biomedicine.

A statement by Henry Ford reported that "real progress happens only when the advantages of a new technology become available to everybody".

The consolidation of technologies based on artificial intelligence in the health domain is intended to bring benefits to everyone, from the stakeholder to the patient, in the form of equity of care.

Artificial intelligence in the future will have a strong impact on:

• The prevention of the onset of diseases in the individual and in society
• The provision of personal care and assistance.
• Society trends regarding diseases and the impact of biological and behavioral factors.
• Organization of hospital activities with regard to treatment, diagnostic and decision-making processes.

Thanks to artificial intelligence, on the one hand, big data will help us to predict diseases on an individual and collective basis and to identify and correct population behaviors; on the other hand, wearable technologies will allow us to monitor and collect individual medical information and to calibrate the care process. The integration of artificial intelligence with virtual reality and augmented reality will allow us to create both virtual medicine services that citizens can access in a simple and direct way, and robotic surgery applications that are increasingly effective and safe.

This topic is very broad, and ranges from scientific development to applications in the health domain, and it also includes ethical and training issues.

This Topic invites authors to contribute on aspects of the research on, development, and application of artificial intelligence in current applications in the health domain and in future scenarios of use.

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
• Care robotics
• Natural language processing
• Social intelligence
• Virtual reality
• Augmented reality
• Medical decision making
• Disease monitoring, prediction, diagnosis, and classification
• Patient monitoring
• Hospital organization
• Diagnostic imaging
• Digital pathology
• Digital radiology.

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	5.0	6.9	2020	20.7 Days	CHF 1600	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Bioengineering bioengineering	3.7	5.3	2014	19.2 Days	CHF 2700	Submit
Healthcare healthcare	2.7	4.7	2013	21.5 Days	CHF 2700	Submit
International Journal of Environmental Research and Public Health ijerph	-	8.5	2004	27.8 Days	CHF 2500	Submit
Journal of Clinical Medicine jcm	2.9	5.2	2012	17.7 Days	CHF 2600	Submit
Clinics and Practice clinpract	2.2	2.8	2011	22.7 Days	CHF 1600	Submit
Journal of Imaging jimaging	3.3	6.7	2015	15.3 Days	CHF 1800	Submit

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Supplementary material:
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25 pages, 4609 KB  

Open AccessArticle

Mapping Mental Trajectories to Physical Risk: An AI Framework for Predicting Sarcopenia from Dynamic Depression Patterns in Public Health

by Yaxin Han, Renzhi Tian, Chengchang Pan and Honggang Qi

AI 2025, 6(12), 300; https://doi.org/10.3390/ai6120300 - 21 Nov 2025

Viewed by 725

Abstract

Background: The accelerating global population aging underscores the urgency of addressing public health challenges. Sarcopenia and depression are prevalent, interrelated conditions in older adults, yet prevailing research often treats depression as a static state, neglecting its longitudinal progression and limiting predictive capability for [...] Read more.

Background: The accelerating global population aging underscores the urgency of addressing public health challenges. Sarcopenia and depression are prevalent, interrelated conditions in older adults, yet prevailing research often treats depression as a static state, neglecting its longitudinal progression and limiting predictive capability for sarcopenia. Methods: Using data from four waves (2011–2018) of the China Health and Retirement Longitudinal Study (CHARLS), we identified distinct depressive symptom trajectories via Group-Based Trajectory Modeling. Seven machine learning algorithms were employed to develop predictive models for sarcopenia risk, incorporating these trajectory patterns and baseline characteristics. Results: Three depressive symptom trajectories were identified: ‘Persistently Low’, ‘Persistently Moderate’, and ‘Persistently High’. Tree-based ensemble methods, particularly Random Forest and XGBoost, demonstrated superior and robust performance (mean accuracy: 0.8265 and 0.8178; mean weighted F1-score: 0.8075 and 0.8084, respectively). Feature importance analysis confirmed depressive symptoms as a core, independent predictor, ranking third (5.7% importance) in the optimal Random Forest model, only after BMI and cognitive function, and surpassing traditional risk factors like age and waist circumference. Conclusions: This study validates that longitudinal depressive symptom trajectories provide superior predictive power for sarcopenia risk compared to single-time-point assessments, effectively mapping mental health trajectories to physical risk. The robust ML framework not only enables early identification of high-risk individuals but also reveals a multidimensional risk profile, highlighting the intricate mind–body connection in aging. These findings advocate for integrating dynamic mental health monitoring into routine geriatric assessments, demonstrating the potential of AI to facilitate a paradigm shift towards proactive, personalized, and scalable prevention strategies in public health and clinical practice. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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13 pages, 294 KB  

Open AccessPerspective

Understanding the Risks and Benefits of Implementing AI-Enabled Remote Patient Monitoring Systems for Disease Management

by Junaid Nabi, Richard Staynings, Javaid Iqbal Sofi and Henry H. Willis

Int. J. Environ. Res. Public Health 2025, 22(11), 1734; https://doi.org/10.3390/ijerph22111734 - 17 Nov 2025

Viewed by 946

Abstract

Effectively managing risk is essential for fostering innovation in healthcare, especially with advancements like artificial intelligence (AI) and machine learning (ML). These technologies aim to enhance accessibility, efficiency, and equity in healthcare delivery. To assess the practical utility of AI-enabled remote patient monitoring [...] Read more.

Effectively managing risk is essential for fostering innovation in healthcare, especially with advancements like artificial intelligence (AI) and machine learning (ML). These technologies aim to enhance accessibility, efficiency, and equity in healthcare delivery. To assess the practical utility of AI-enabled remote patient monitoring (RPM) devices, it is crucial to identify and evaluate associated risks while distinguishing between acceptable risk, which society tolerates, and optimal risk, which balances risk reduction costs with benefits. This paper outlines how policymakers should adopt the framework of optimal risk to ensure patient safety while maximizing the advantages of these technologies. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

16 pages, 4601 KB  

Open AccessPerspective

AI in Pediatric Spine Care: Clinical, Research, and Ethical Considerations

by Hans K. Nugraha, Adam P. Rasmussen, Kellen L. Mulford, Linjun Yang, Cody C. Wyles and A. Noelle Larson

J. Clin. Med. 2025, 14(22), 8115; https://doi.org/10.3390/jcm14228115 - 16 Nov 2025

Viewed by 522

Abstract

Artificial intelligence (AI) is increasingly shaping pediatric spine care, leveraging its rapid advancements in healthcare to improve efficiency, accuracy, and disease understanding. Moreover, machine learning and deep learning excel at detecting complex patterns. This holds promise in processing spinal deformity data, with the [...] Read more.

Artificial intelligence (AI) is increasingly shaping pediatric spine care, leveraging its rapid advancements in healthcare to improve efficiency, accuracy, and disease understanding. Moreover, machine learning and deep learning excel at detecting complex patterns. This holds promise in processing spinal deformity data, with the potential to surpass traditional statistical methods in predictive accuracy. Challenges persist, however, including unclear clinical implementation guidelines, limited model transparency, and ethical concerns surrounding data privacy and bias. Small sample sizes and the need for larger, diverse datasets further complicate integration. In order to realize AI’s transformative potential in pediatric spine care, these critical obstacles must be addressed for effective and ethical clinical adoption. This review examines the role of AI through applications such as image sorting, surgical outcome prediction, forecasting of spinal curve progression, and vertebral volumetric analysis using deep reasoning. It also explores possible intraoperative contributions from AI, including robotics and optimized screw trajectory planning, and the potential of large language models in clinical practice. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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12 pages, 451 KB  

Open AccessArticle

Patients Prefer Human Empathy, but Not Always Human Wording: A Single-Blind Within-Subject Trial of GPT-Generated vs. Clinician Discharge Texts in Emergency Ophthalmology

by Dea Samardzic, Jelena Curkovic, Donald Okmazic, Sandro Glumac, Josip Vrdoljak, Marija Skara Kolega and Ante Kreso

Clin. Pract. 2025, 15(11), 208; https://doi.org/10.3390/clinpract15110208 - 14 Nov 2025

Viewed by 379

Abstract

Background/Objectives: Written discharge explanations are crucial for patient understanding and safety in emergency eye care, yet their tone and clarity vary. Large language models (LLMs, artificial intelligence systems trained to generate human-like text) can produce patient-friendly materials, but direct, blinded comparisons with clinician-written [...] Read more.

Background/Objectives: Written discharge explanations are crucial for patient understanding and safety in emergency eye care, yet their tone and clarity vary. Large language models (LLMs, artificial intelligence systems trained to generate human-like text) can produce patient-friendly materials, but direct, blinded comparisons with clinician-written texts remain scarce. This study compared patient perceptions of a routine clinician-written discharge text and a GPT-5-generated explanation, where GPT-5 (OpenAI) is a state-of-the-art LLM, based on the same clinical facts in emergency ophthalmology. The primary objective was empathy; secondary outcomes included clarity, detail, usefulness, trust, satisfaction, and intention to follow advice. Methods: We conducted a prospective, single-blind, within-subject study in the Emergency Ophthalmology Unit of the University Hospital Centre Split, Croatia. Adults (n = 129) read two standardized texts (clinician-written vs. GPT-5-generated), presented in identical format and in randomized order under masking. Each participant rated both on Likert scales with 1–5 points. Paired comparisons used Wilcoxon signed-rank tests with effect sizes, and secondary outcomes were adjusted using the Benjamini–Hochberg false discovery rate. Results: Empathy ratings were lower for the GPT-5-generated text than for the clinician-written text (means 3.97 vs. 4.30; mean difference −0.33; 95% CI −0.44 to −0.22; p < 0.001). After correcting for multiple comparisons, no secondary outcome differed significantly between sources. Preferences were evenly split (47.8% preferred GPT-5 among those expressing a preference). Conclusions: In emergency ophthalmology, GPT-5-generated explanations approached clinician-written materials on most perceived attributes but were rated less empathic. A structured, human-in-the-loop workflow—in which LLM-generated drafts are reviewed and tailored by clinicians—appears prudent for safe, patient-centered deployment. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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17 pages, 1610 KB  

Open AccessArticle

Advancing Toward P6 Medicine: Recommendations for Integrating Artificial Intelligence in Internal Medicine

by Ismael Said-Criado, Filomena Pietrantonio, Marco Montagna, Francesco Rosiello, Oleg Missikoff, Carlo Drago, Tiffany I. Leung, Antonio Vinci, Alessandro Signorini and Ricardo Gómez-Huelgas

Clin. Pract. 2025, 15(11), 200; https://doi.org/10.3390/clinpract15110200 - 29 Oct 2025

Viewed by 680

Abstract

Background: Internists formulate diagnostic hypotheses and personalized treatment plans by integrating data from a comprehensive clinical interview, reviewing a patient’s medical history, physical examination and findings from complementary tests. The patient treatment life cycle generates a significant volume of data points that can [...] Read more.

Background: Internists formulate diagnostic hypotheses and personalized treatment plans by integrating data from a comprehensive clinical interview, reviewing a patient’s medical history, physical examination and findings from complementary tests. The patient treatment life cycle generates a significant volume of data points that can offer valuable insights to improve patient care by guiding clinical decision-making. Artificial Intelligence (AI) and, in particular, Generative AI (GAI), are promising tools in this regard, particularly after the introduction of Large Language Models. The European Federation of Internal Medicine (EFIM) recognizes the transformative impact of AI in leveraging clinical data and advancing the field of internal medicine. This position paper from the EFIM explores how AI can be applied to achieve the goals of P6 Medicine principles in internal medicine. P6 Medicine is an advanced healthcare model that extends the concept of Personalized Medicine toward a holistic, predictive, patient-centered approach that also integrates psycho-cognitive and socially responsible dimensions. An additional concept introduced is that of Digital Therapies (DTx), software applications designed to prevent and manage diseases and disorders through AI, which are used in the clinical setting if validated by rigorous research studies. Methods: The literature examining the relationship between AI and Internal Medicine was investigated through a bibliometric analysis. The themes identified in the literature review were further examined through the Delphi method. Thirty international AI and Internal Medicine experts constituted the Delphi panel. Results: Delphi results were summarized in a SWOT Analysis. The evidence is that through extensive data analysis, diagnostic capacity, drug development and patient tracking are increased. Conclusions: The panel unanimously considered AI in Internal Medicine as an opportunity, achieving a complete consensus on the matter. AI-driven solutions, including clinical applications of GAI and DTx, hold the potential to strongly change internal medicine by streamlining workflows, enhancing patient care and generating valuable data. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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23 pages, 3532 KB  

Open AccessReview

Generative Artificial Intelligence in Healthcare: A Bibliometric Analysis and Review of Potential Applications and Challenges

by Vanita Kouomogne Nana and Mark T. Marshall

AI 2025, 6(11), 278; https://doi.org/10.3390/ai6110278 - 23 Oct 2025

Viewed by 2307

Abstract

The remarkable progress of artificial intelligence (AI) in recent years has significantly extended its application possibilities within the healthcare domain. AI has become more accessible to a wider range of healthcare personnel and service users, in particular due to the proliferation of Generative [...] Read more.

The remarkable progress of artificial intelligence (AI) in recent years has significantly extended its application possibilities within the healthcare domain. AI has become more accessible to a wider range of healthcare personnel and service users, in particular due to the proliferation of Generative AI (GenAI). This study presents a bibliometric analysis of GenAI in healthcare. By analysing the Scopus database academic literature, our study explores the knowledge structure, emerging trends, and challenges of GenAI in healthcare. The results showed that GenAI is increasingly being adoption in developed countries, with major US institutions leading the way, and a large number of papers are being published on the topic in top-level academic venues. Our findings also show that there is a focus on particular areas of healthcare, with medical education and clinical decision-making showing active research, while areas such as emergency medicine remain poorly explored. Our results also show that while there is a focus on the benefits of GenAI for the healthcare industry, its limitations need to be acknowledged and addressed to facilitate its integration in clinical settings. The findings of this study can serve as a foundation for understanding the field, allowing academics, healthcare practitioners, educators, and policymakers to better understand the current focus within GenAI for healthcare, as well as highlighting potential application areas and challenges around accuracy, privacy, and ethics that must be taken into account when developing healthcare-focused GenAI applications. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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39 pages, 1188 KB  

Open AccessReview

A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data

by Hajarimino Rakotomanana and Ghazal Rouhafzay

Bioengineering 2025, 12(11), 1136; https://doi.org/10.3390/bioengineering12111136 - 22 Oct 2025

Cited by 1 | Viewed by 2478

Abstract

This scoping review systematically maps the rapidly evolving application of Artificial Intelligence (AI) in Autism Spectrum Disorder (ASD) diagnostics, specifically focusing on computational behavioral phenotyping. Recognizing that observable traits like speech and movement are critical for early, timely intervention, the study synthesizes AI’s [...] Read more.

This scoping review systematically maps the rapidly evolving application of Artificial Intelligence (AI) in Autism Spectrum Disorder (ASD) diagnostics, specifically focusing on computational behavioral phenotyping. Recognizing that observable traits like speech and movement are critical for early, timely intervention, the study synthesizes AI’s use across eight key behavioral modalities. These include voice biomarkers, conversational dynamics, linguistic analysis, movement analysis, activity recognition, facial gestures, visual attention, and multimodal approaches. The review analyzed 158 studies published between 2015 and 2025, revealing that modern Machine Learning and Deep Learning techniques demonstrate highly promising diagnostic performance in controlled environments, with reported accuracies of up to 99%. Despite this significant capability, the review identifies critical challenges that impede clinical implementation and generalizability. These persistent limitations include pervasive issues with dataset heterogeneity, gender bias in samples, and small overall sample sizes. By detailing the current landscape of observable data types, computational methodologies, and available datasets, this work establishes a comprehensive overview of AI’s current strengths and fundamental weaknesses in ASD diagnosis. The article concludes by providing actionable recommendations aimed at guiding future research toward developing diagnostic solutions that are more inclusive, generalizable, and ultimately applicable in clinical settings. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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33 pages, 1304 KB  

Open AccessSystematic Review

Sustainability of AI-Assisted Mental Health Intervention: A Review of the Literature from 2020–2025

by Danicsa Karina Espino Carrasco, María del Rosario Palomino Alcántara, Carmen Graciela Arbulú Pérez Vargas, Briseidy Massiel Santa Cruz Espino, Luis Jhonny Dávila Valdera, Cindy Vargas Cabrera, Madeleine Espino Carrasco, Anny Dávila Valdera and Luz Mirella Agurto Córdova

Int. J. Environ. Res. Public Health 2025, 22(9), 1382; https://doi.org/10.3390/ijerph22091382 - 4 Sep 2025

Viewed by 4230

Abstract

This systematic review examines the role of artificial intelligence (AI) in the development of sustainable mental health interventions through a comprehensive analysis of literature published between 2020 and 2025. In accordance with the PRISMA guidelines, 62 studies were selected from 1652 initially identified [...] Read more.

This systematic review examines the role of artificial intelligence (AI) in the development of sustainable mental health interventions through a comprehensive analysis of literature published between 2020 and 2025. In accordance with the PRISMA guidelines, 62 studies were selected from 1652 initially identified records across four major databases. The results revealed four dimensions critical for sustainability: ethical considerations (privacy, informed consent, bias, and human oversight), personalization approaches (federated learning and AI-enhanced therapeutic interventions), risk mitigation strategies (data security, algorithmic bias, and clinical efficacy), and implementation challenges (technical infrastructure, cultural adaptation, and resource allocation). The findings demonstrate that long-term sustainability depends on ethics-driven approaches, resource-efficient techniques such as federated learning, culturally adaptive systems, and appropriate human-AI integration. The study concludes that sustainable mental health AI requires addressing both technical efficacy and ethical integrity while ensuring equitable access across diverse contexts. Future research should focus on longitudinal studies examining the long-term effectiveness and cultural adaptability of AI interventions in resource-limited settings. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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27 pages, 1098 KB  

Open AccessArticle

Enhancing Healthcare for People with Disabilities Through Artificial Intelligence: Evidence from Saudi Arabia

by Adel Saber Alanazi, Abdullah Salah Alanazi and Houcine Benlaria

Healthcare 2025, 13(13), 1616; https://doi.org/10.3390/healthcare13131616 - 6 Jul 2025

Cited by 1 | Viewed by 1954

Abstract

Background/Objectives: Artificial intelligence (AI) offers opportunities to enhance healthcare accessibility for people with disabilities (PwDs). However, their application in Saudi Arabia remains limited. This study explores PwDs’ experiences with AI technologies within the Kingdom’s Vision 2030 digital health framework to inform inclusive healthcare [...] Read more.

Background/Objectives: Artificial intelligence (AI) offers opportunities to enhance healthcare accessibility for people with disabilities (PwDs). However, their application in Saudi Arabia remains limited. This study explores PwDs’ experiences with AI technologies within the Kingdom’s Vision 2030 digital health framework to inform inclusive healthcare innovation strategies. Methods: Semi-structured interviews were conducted with nine PwDs across Riyadh, Al-Jouf, and the Northern Border region between January and February 2025. Participants used various AI-enabled technologies, including smart home assistants, mobile health applications, communication aids, and automated scheduling systems. Thematic analysis following Braun and Clarke’s six-phase framework was employed to identify key themes and patterns. Results: Four major themes emerged: (1) accessibility and usability challenges, including voice recognition difficulties and interface barriers; (2) personalization and autonomy through AI-assisted daily living tasks and medication management; (3) technological barriers such as connectivity issues and maintenance gaps; and (4) psychological acceptance influenced by family support and cultural integration. Participants noted infrastructure gaps in rural areas, financial constraints, limited disability-specific design, and digital literacy barriers while expressing optimism regarding AI’s potential to enhance independence and health outcomes. Conclusions: Realizing the benefits of AI for disability healthcare in Saudi Arabia requires culturally adapted designs, improved infrastructure investment in rural regions, inclusive policymaking, and targeted digital literacy programs. These findings support inclusive healthcare innovation aligned with Saudi Vision 2030 goals and provide evidence-based recommendations for implementing AI healthcare technologies for PwDs in similar cultural contexts. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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12 pages, 1687 KB  

Open AccessArticle

AI-Assisted LVEF Assessment Using a Handheld Ultrasound Device: A Single-Center Comparative Study Against Cardiac Magnetic Resonance Imaging

by Giovanni Bisignani, Lorenzo Volpe, Andrea Madeo, Riccardo Vico, Davide Bencardino and Silvana De Bonis

J. Clin. Med. 2025, 14(13), 4708; https://doi.org/10.3390/jcm14134708 - 3 Jul 2025

Viewed by 2290

Abstract

Background/Objectives: Two-dimensional echocardiography (2D echo) is widely used for assessing left ventricular ejection fraction (LVEF). This single-center comparative study aims to evaluate the accuracy of LVEF measurements obtained using the AI-assisted handheld ultrasound device Kosmos against cardiac magnetic resonance (CMR), the current gold [...] Read more.

Background/Objectives: Two-dimensional echocardiography (2D echo) is widely used for assessing left ventricular ejection fraction (LVEF). This single-center comparative study aims to evaluate the accuracy of LVEF measurements obtained using the AI-assisted handheld ultrasound device Kosmos against cardiac magnetic resonance (CMR), the current gold standard. Methods: A total of 49 adult patients undergoing clinically indicated CMR were prospectively enrolled. AI-based LVEF measurements were compared with CMR using the Wilcoxon signed-rank test, Pearson correlation, multivariable linear regression, and Bland–Altman analysis. All analyses were performed using STATA v18.0. Results: Median LVEF was 57% (CMR) vs. 55% (AI-Echo), with no significant difference (p = 0.51). Strong correlation (r = 0.99) and minimal bias (1.1%) were observed. Conclusions: The Kosmos AI-based autoEF algorithm demonstrated excellent agreement with CMR-derived LVEF values. Its speed and automation make it promising for bedside assessment in emergency departments, intensive care units, and outpatient clinics. This study aims to fill the gap in current clinical evidence by evaluating, for the first time, the agreement between LVEF measurements obtained via Kosmos’ AI-assisted autoEF and those from cardiac MRI (CMR), the gold standard for ventricular function assessment. This comparison is critical for validating the reliability of portable AI-driven echocardiographic tools in real-world clinical practice. However, these findings derive from a selected population at a single Italian center and should be validated in larger, diverse cohorts before assuming global generalizability. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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13 pages, 1126 KB  

Open AccessArticle

ChatGPT in the Management of Chronic Rhinosinusitis with Nasal Polyps: Promising Support or Digital Illusion? Insights from a Multicenter Observational Study

by Riccardo Manzella, Angelo Immordino, Cosimo Galletti, Federica Giammona Indaco, Giovanna Stilo, Giuliano Messina, Francesco Lorusso, Rosalia Gargano, Silvia Frangipane, Giorgia Giunta, Diana Mariut, Daniele Portelli, Patrizia Zambito, Maria Grazia Ferrisi, Francesco Ciodaro, Manuela Centineo, Salvatore Maira, Francesco Dispenza, Salvatore Gallina, Ignazio La Mantia, Francesco Galletti, Bruno Galletti and Federico Sireciadd Show full author list remove Hide full author list

J. Clin. Med. 2025, 14(13), 4501; https://doi.org/10.3390/jcm14134501 - 25 Jun 2025

Viewed by 1282

Abstract

Background/Objective: Chronic rhinosinusitis with nasal polyps is a chronic inflammatory disease with a significant impact on quality of life and is frequently associated, from a pathogenetic perspective, with type 2 inflammation. The introduction of biologic therapies has marked a turning point in the [...] Read more.

Background/Objective: Chronic rhinosinusitis with nasal polyps is a chronic inflammatory disease with a significant impact on quality of life and is frequently associated, from a pathogenetic perspective, with type 2 inflammation. The introduction of biologic therapies has marked a turning point in the management of severe forms of the disease, offering a valuable treatment option. However, selecting the most suitable biologic agent for a specific patient remains a clinical challenge. Artificial intelligence, and, in particular, ChatGPT, has recently been proposed as a potential tool to support medical decision-making and guide therapeutic choices. To evaluate the concordance between the therapeutic recommendations provided by ChatGPT and those of a multidisciplinary expert board in selecting the most appropriate biologic therapy for CRSwNP patients, based on the analysis of their phenotype and endotype. Methods: A multicenter observational cohort study was conducted. Clinical data from 286 patients with CRSwNP were analyzed. For each case, the therapeutic choice among Dupilumab, Mepolizumab, and Omalizumab was compared between the board and ChatGPT. Concordance rates and Cohen’s Kappa coefficient were calculated. Results: Overall concordance was 59.2%, with a Cohen’s Kappa coefficient of 0.116. Concordance by drug was 62.8% for Dupilumab, 26.5% for Mepolizumab, and 9.1% for Omalizumab. Patients presented with severe clinical profiles, with an average Nasal Polyp Score of 6.22 and an average SNOT-22 score of 64.5. Conclusions: This study demonstrates that, despite its substantial theoretical potential, ChatGPT is currently not a reliable tool for the autonomous selection of biological therapies in patients with CRSwNP. Further studies are necessary to enhance its reliability and integration into clinical practice. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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9 pages, 402 KB  

Open AccessArticle

Using Machine Learning to Predict Resilience Among Nurses in a South African Setting

by Jennifer Chipps, Amanda Cromhout and Umit Tokac

Int. J. Environ. Res. Public Health 2025, 22(7), 996; https://doi.org/10.3390/ijerph22070996 - 24 Jun 2025

Viewed by 1003

Abstract

Nursing is a stressful profession. Stress can affect the mental health of nurses. A positive response to stress, resilience, is known to be a protective factor against mental health issues. This study aimed to use machine learning with secondary data from five survey [...] Read more.

Nursing is a stressful profession. Stress can affect the mental health of nurses. A positive response to stress, resilience, is known to be a protective factor against mental health issues. This study aimed to use machine learning with secondary data from five survey studies, conducted between 2022 and 2023, to identify factors predicting high versus low levels of resilience in South African nursing samples from the Western Cape Province, South Africa. The sample included (1134 records (male = 250, 22.0%, female = 874, 77.1%, and other = 10 (0.9%) included all data on all categories of nursing staff (student nurses (567, 50%), professional registered nurses (315, 27.8%), and non-professional nurses (246, 21.7%) who completed a survey using a response to stress scale. We used random forest analysis, demographic variables, years of experience, and a brief 4-item screen of resilience to predict resilience. The model yielded limited added value from demographic groupings in this model, but the brief screening had an overall classification accuracy of 86.41% (95% CI: 0.810; 0.908). Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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40 pages, 1816 KB  

Open AccessReview

Exploring the Potential of Digital Twins in Cancer Treatment: A Narrative Review of Reviews

by Daniele Giansanti and Sandra Morelli

J. Clin. Med. 2025, 14(10), 3574; https://doi.org/10.3390/jcm14103574 - 20 May 2025

Cited by 11 | Viewed by 6435

Abstract

Background: Digital twin (DT) technology, integrated with artificial intelligence (AI) and machine learning (ML), holds significant potential to transform oncology care. By creating dynamic virtual replicas of patients, DTs allow clinicians to simulate disease progression and treatment responses, offering a personalized approach to [...] Read more.

Background: Digital twin (DT) technology, integrated with artificial intelligence (AI) and machine learning (ML), holds significant potential to transform oncology care. By creating dynamic virtual replicas of patients, DTs allow clinicians to simulate disease progression and treatment responses, offering a personalized approach to cancer treatment. Aim: This narrative review aimed to synthesize existing review studies on the application of digital twins in oncology, focusing on their potential benefits, challenges, and ethical considerations. Methods: The narrative review of reviews (NRR) followed a structured selection process using a standardized checklist. Searches were conducted in PubMed and Scopus with a predefined query on digital twins in oncology. Reviews were prioritized based on their synthesis of prior studies, with a focus on digital twins in oncology. Studies were evaluated using quality parameters (clear rationale, research design, methodology, results, conclusions, and conflict disclosure). Only studies with scores above a prefixed threshold and disclosed conflicts of interest were included in the final synthesis; seventeen studies were selected. Results and Discussion: DTs in oncology offer advantages such as enhanced decision-making, optimized treatment regimens, and improved clinical trial design. Moreover, economic forecasts suggest that the integration of digital twins into healthcare systems may significantly reduce treatment costs and drive growth in the precision medicine market. However, challenges include data integration issues, the complexity of biological modeling, and the need for robust computational resources. A comparison to cutting-edge research studies contributes to this direction and confirms also that ethical and legal considerations, particularly concerning AI, data privacy, and accountability, remain significant barriers. Conclusions: The integration of digital twins in oncology holds great promise, but requires careful attention to ethical, legal, and operational challenges. Multidisciplinary efforts, supported by evolving regulatory frameworks like those in the EU, are essential for ensuring responsible and effective implementation to improve patient outcomes. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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13 pages, 1649 KB  

Open AccessArticle

Impact of the COVID-19 Pandemic on Life Expectancy in South Korea, 2019–2022

by Soojin Song and Daroh Lim

Healthcare 2025, 13(3), 258; https://doi.org/10.3390/healthcare13030258 - 28 Jan 2025

Cited by 2 | Viewed by 1855

Abstract

Objective: This study investigated changes in life expectancy due to the COVID-19 pandemic by analyzing the contributions of age, sex, and cause of death in 2019 and 2022. Methods: Korea’s simplified life table and cause-of-death statistics from 2019 to 2022 were used to [...] Read more.

Objective: This study investigated changes in life expectancy due to the COVID-19 pandemic by analyzing the contributions of age, sex, and cause of death in 2019 and 2022. Methods: Korea’s simplified life table and cause-of-death statistics from 2019 to 2022 were used to assess mortality changes by age, sex, and cause of death during the pandemic. Joinpoint regression analysis was applied to detect trends, and the Arriaga decomposition method was used to quantify the contributions of age, sex, and cause of death to life expectancy changes. Results: Joinpoint regression identified a slow increase in life expectancy in 2007 and a decline in 2020, coinciding with the COVID-19 pandemic. Life expectancy decreased markedly for men (−0.36 years per year, 95%CI: −0.68 to −0.03) and women (−0.45 years per year, 95%CI: −0.71 to −0.18). Age-specific contributions revealed declines across age groups, with the steepest reductions in the older population (80 years or older: −0.35 years for men; −0.52 years for women). Women (−0.68 years) contributed more to the decline in life expectancy than men (−0.41 years). COVID-19 ranked as the third leading cause of death in 2022, significantly contributing to the decline in life expectancy among the older population (aged 80 years or older: −0.306 years for men, −0.408 years for women). Women in Korea were more affected than men, reducing the sex-specific gap in life expectancy by 0.3 years. Conclusions: The COVID-19 pandemic significantly impacted the life expectancy in Korea, particularly among older adults, with women experiencing a greater decline than men. These findings emphasize the need for targeted public health strategies to address age and sex disparities in future pandemics. Before the pandemic, non-communicable diseases such as malignant neoplasms, heart disease, and cerebrovascular disease dominated Korea’s top 10 causes of death. During the pandemic, however, COVID-19 rose to third place by 2022. Notably, intentional self-harm (suicide) contributed to an increase in life expectancy, suggesting shifts in the relative impact of various causes of death. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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24 pages, 1499 KB  

Open AccessArticle

Explainable Artificial Intelligence for Predicting Attention Deficit Hyperactivity Disorder in Children and Adults

by Zineb Namasse, Mohamed Tabaa, Zineb Hidila and Samar Mouchawrab

Healthcare 2025, 13(2), 155; https://doi.org/10.3390/healthcare13020155 - 15 Jan 2025

Viewed by 3435

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a disorder that starts in childhood, sometimes persisting into adulthood. It puts a strain on their social, professional, family, and environmental lives, which can exacerbate disorders such as anxiety, depression, and bipolar disorder. Background/Objectives: This paper [...] Read more.

Attention Deficit Hyperactivity Disorder (ADHD) is a disorder that starts in childhood, sometimes persisting into adulthood. It puts a strain on their social, professional, family, and environmental lives, which can exacerbate disorders such as anxiety, depression, and bipolar disorder. Background/Objectives: This paper aims to predict ADHD in children and adults and explain the main factors impacting this disorder. Methods: We start by introducing the main symptoms and challenges ADHD poses for children and adults such as epilepsy and depression. Then, we present the results of existing research on three ADHD comorbidities: anxiety, depression, and bipolar disorder, and their possible continuity in adulthood with therapeutic implications. After that, we explain the impact of this disorder and its relationship with these comorbidities on the affected patient’s health and environment and list proposed treatments. We propose a methodology for predicting this impairment in children and adults by using Machine Learning algorithms (ML), Explainable Artificial Intelligence (XAI), and two datasets, the National Survey for Children’s Health (NSCH) (2022) for the children and the ADHD|Mental Health for the adults. Results: Logistic Regression (LR) was the most suitable algorithm for children, with an accuracy of 99%. As for adults, the XGBoost (XGB) was the most performant ML method, with an accuracy of 100%. Conclusions: Lack of sleep and excessive smiling/laughing are among the factors having an impact on ADHD for children, whereas anxiety and depression affect ADHD adults. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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