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AI-Driven Healthcare Insights
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AI-Driven Healthcare Insights

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

Deadline for manuscript submissions: 12 September 2026 | Viewed by 8475

Special Issue Editors

Dr. Victor R. Prybutok

E-Mail Website
Guest Editor
Department of Information Technology and Decision Science, University of North Texas, Denton, TX 76203, USA
Interests: AI; data analytics; quantitative methods; data modeling; quality control; service quality
Special Issues, Collections and Topics in MDPI journals
Dr. Xianghui (Richard) Peng

E-Mail Website
Guest Editor
Black School of Business, Penn State Behrend, Erie, PA 16563, USA
Interests: agile project management; healthcare operations; quality management; sustainable supply chain management; technology management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current research typically focuses on theory-driven studies. However, it is equally critical that we leverage artificial intelligence and machine learning to extract meaningful insights from healthcare data in ways that transform our understanding of complex medical phenomena. The rapid advancement of AI technologies provides unprecedented opportunities to analyze vast healthcare datasets, uncover hidden patterns, and generate actionable insights that were impossible to detect through traditional analytical approaches. This Special Issue will showcase how AI-driven discovery provides transformative insights and creates new pathways for advancing healthcare practice and delivery. We are pleased to invite you to submit your AI-driven healthcare investigations using a variety of artificial intelligence and machine learning methods, including, but not limited to, natural language processing of clinical notes, deep learning analysis of medical imaging, predictive modeling using electronic health records, and other innovative AI applications for extracting insights from healthcare data. This Special Issue is also open to comprehensive review articles that examine how AI methodologies can unlock the potential of healthcare datasets to develop new research paradigms. This Special Issue aims to demonstrate how AI-powered data exploration can lead to breakthrough discoveries that extend our understanding of current theory and practice in healthcare. We particularly encourage submissions that showcase novel AI and machine learning approaches, including but not limited to deep learning, neural networks, ensemble methods, and emerging AI technologies that offer new perspectives on healthcare challenges. Themes and article types include the application of AI and machine learning methods to healthcare datasets to discover complex relationships among variables and generate actionable insights for healthcare researchers and practitioners. Additionally, review articles that examine how AI-driven approaches create value by directing future research directions and identifying novel research questions are strongly welcomed. We look forward to receiving your contributions.

Dr. Victor R. Prybutok
Dr. Xianghui (Richard) Peng
Guest Editors

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-blind 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

  • AI-driven health insights
  • AI data mining
  • AI healthcare analytics
  • health informatics
  • AI-driven patient insights
  • AI healthcare modeling

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Published Papers (6 papers)

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Research

Jump to: Review, Other

19 pages, 1768 KB  
Article
Gender-Attributed Persona Prompts and the Diagnostic Accuracy of Proprietary and Open-Weight Large Language Models in Chagas Disease and Visceral Leishmaniasis: A Paired Experimental Study
by Aline Rafaela Soares da Silva, Dino Schwingel, Samuel Ricarte de Aquino, Rodrigo José Videres Cordeiro de Brito, Márcio de Oliveira Silva, Flávia Emília Cavalcante Valença Fernandes, Amanda Alves Marcelino da Silva, Ricardo Kenji Shiosaki, Paulo Gustavo Serafim de Carvalho, Rogério Fabiano Gonçalves, Paulo Ditarso Maciel, Jr., Fabiana Oliveira dos Santos Camatari, Paula Andreatta Maduro, Maria Jacqueline Silva Ribeiro and Paulo Adriano Schwingel
Healthcare 2026, 14(10), 1385; https://doi.org/10.3390/healthcare14101385 - 19 May 2026
Viewed by 237
Abstract
Background: Large language models (LLMs) are increasingly considered as adjuncts for differential diagnostic reasoning, yet their sensitivity to gender-attributed cues in the persona prompt—particularly for neglected tropical diseases (NTDs) and in non-English clinical settings—remains poorly characterised. Objective: The objective of this [...] Read more.
Background: Large language models (LLMs) are increasingly considered as adjuncts for differential diagnostic reasoning, yet their sensitivity to gender-attributed cues in the persona prompt—particularly for neglected tropical diseases (NTDs) and in non-English clinical settings—remains poorly characterised. Objective: The objective of this study was to compare the diagnostic accuracy of one proprietary and three open-weight LLMs for Chagas disease (CD) and visceral leishmaniasis (VL) under paired persona-prompt conditions in which the only manipulated variable was the linguistic gender of the simulated medical persona. Methods: This experimental, paired study evaluated ChatGPT-4o, LLaMA 3 70B, Meditron-70B, and Mixtral 8x7B across 12 cases per disease (n = 24) from real records at a Brazilian teaching hospital. The primary outcome was top-five diagnostic accuracy. A committee of five infectious-disease specialists assessed the biological plausibility of all differentials. Paired comparisons used Wilcoxon signed-rank tests; 95% confidence intervals were calculated using the Wilson-score method. Results: ChatGPT-4o achieved the highest accuracy (CD: 100% under both prompts; VL: 83.3–91.7%). LLaMA 3 70B and Mixtral 8x7B showed moderate performance (41.7–83.3%); the medically fine-tuned Meditron-70B exhibited paradoxically poor accuracy (16.7–25.0%) and the lowest committee-rated plausibility scores. A consistent small numerical trend favoured the female prompt across most model–disease combinations (differences of 0–16.7 percentage points), but no comparison reached statistical significance (all p > 0.05). Conclusions: Gender-attributed persona-prompt variation did not produce a systematic effect on LLM diagnostic accuracy for CD or VL. ChatGPT-4o outperformed the three evaluated open-weight alternatives, and medical-domain fine-tuning did not confer the expected advantage. Expert-validated assessment of hypothesis plausibility should complement target-disease accuracy in clinical LLM evaluation studies, particularly for NTDs. Full article
(This article belongs to the Special Issue AI-Driven Healthcare Insights)
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20 pages, 520 KB  
Article
AI-Enhanced Qualitative Analysis in Healthcare: Unlocking Insight from Interviews of Leadership at Top-Performing Academic Medical Centers
by Triss Ashton and Seth Chatfield
Healthcare 2026, 14(2), 248; https://doi.org/10.3390/healthcare14020248 - 19 Jan 2026
Cited by 1 | Viewed by 1100
Abstract
Background/Objectives: Vast amounts of textual data are generated by healthcare organizations every year. Traditional content analysis is time-intensive, error-prone, and potentially biased. This study demonstrates how freely available large language model (LLM) artificial intelligence (AI) tools can efficiently and effectively analyze qualitative [...] Read more.
Background/Objectives: Vast amounts of textual data are generated by healthcare organizations every year. Traditional content analysis is time-intensive, error-prone, and potentially biased. This study demonstrates how freely available large language model (LLM) artificial intelligence (AI) tools can efficiently and effectively analyze qualitative healthcare data and uncover insights missed by traditional manual analysis. Interview data from chief nursing officers (CNOs) at top-performing academic medical centers were analyzed to identify factors contributing to their operational and patient quality success. Methods: Semi-structured interviews were conducted with CNOs from top-performing academic medical centers that achieved top-decile quality measures while using resources most efficiently. Interview transcripts were analyzed using a mix of traditional text mining in LSA and Gemini 2.5. The capability of four freely available AI platforms—Gemini 2.5, Scholar AI 5.1, Copilot’s Chat, and Claude’s Sonnet 4.5—was also reviewed. Results: LLM AI analysis identified ten primary factors, comprising twenty-four subtopics, that characterized successful hospital performance. Notably, AI analysis identified a theoretical connection that manual analysis had missed, revealing how the identified framework aligned with Donabedian’s seminal structure, process, outcomes quality model. The AI analysis reduced the required time from weeks to nearly instantaneous. Conclusions: LLM AI tools offer a transformative approach to unlocking insight from the analysis of qualitative textual data in healthcare settings. These tools can provide rapid insight that is accessible to personnel with minimal text-mining expertise and offer a practical solution for healthcare organizations to unlock insight hidden in the vast amounts of textual data they hold. Full article
(This article belongs to the Special Issue AI-Driven Healthcare Insights)
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27 pages, 11265 KB  
Article
Using Machine Learning Methods to Predict Cognitive Age from Psychophysiological Tests
by Daria D. Tyurina, Sergey V. Stasenko, Konstantin V. Lushnikov and Maria V. Vedunova
Healthcare 2025, 13(24), 3193; https://doi.org/10.3390/healthcare13243193 - 5 Dec 2025
Viewed by 720
Abstract
Background/Objectives: This paper presents the results of predicting chronological age from psychophysiological tests using machine learning regressors. Methods: Subjects completed a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial [...] Read more.
Background/Objectives: This paper presents the results of predicting chronological age from psychophysiological tests using machine learning regressors. Methods: Subjects completed a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. The sample included 99 subjects, 68 percent of whom were men and 32 percent were women. Based on the test results, 43 features were generated. To determine the optimal feature selection method, several approaches were tested alongside the regression models using MAE, R2, and CV_R2 metrics. SHAP and Permutation Importance (via Random Forest) delivered the best performance with 10 features. Features selected through Permutation Importance were used in subsequent analyses. To predict participants’ age from psychophysiological test results, we evaluated several regression models, including Random Forest, Extra Trees, Gradient Boosting, SVR, Linear Regression, LassoCV, RidgeCV, ElasticNetCV, AdaBoost, and Bagging. Model performance was compared using the determination coefficient (R2) and mean absolute error (MAE). Cross-validated performance (CV_R2) was estimated via 5-fold cross-validation. To assess metric stability and uncertainty, bootstrapping (1000 resamples) was applied to the test set, yielding distributions of MAE and RMSE from which mean values and 95% confidence intervals were derived. Results: The study identified RidgeCV with winsorization and standardization as the best model for predicting cognitive age, achieving a mean absolute error of 5.7 years and an R2 of 0.60. Feature importance was evaluated using SHAP values and permutation importance. SHAP analysis showed that stroop_time_color and stroop_var_attempt_time were the strongest predictors, followed by several task-timing features with moderate contributions. Permutation importance confirmed this ranking, with these two features causing the largest performance drop when permuted. Partial dependence plots further indicated clear positive relationships between these key features and predicted age. Correlation analysis stratified by sex revealed that most features were significantly associated with age, with stronger effects generally observed in men. Conclusions: Feature selection revealed Stroop timing measures and task-related metrics from math and campimetry tests as the strongest predictors, reflecting core cognitive processes linked to aging. The results underscore the value of careful outlier handling, feature selection, and interpretable regularized models for analyzing psychophysiological data. Future work should include longitudinal studies and integration with biological markers to further improve clinical relevance. Full article
(This article belongs to the Special Issue AI-Driven Healthcare Insights)
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Review

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29 pages, 1297 KB  
Review
Artificial Intelligence for Early Detection and Prediction of Chronic Obstructive Pulmonary Disease Exacerbations
by LeAnn Boyce and Victor Prybutok
Healthcare 2026, 14(6), 806; https://doi.org/10.3390/healthcare14060806 - 21 Mar 2026
Viewed by 860
Abstract
Background: Exacerbations of chronic obstructive pulmonary disease (COPD) are a leading cause of morbidity, mortality, and healthcare burden worldwide. Early detection and timely intervention remain important challenges in COPD management, given the unpredictable nature of acute deterioration and limitations of traditional spirometry-based risk [...] Read more.
Background: Exacerbations of chronic obstructive pulmonary disease (COPD) are a leading cause of morbidity, mortality, and healthcare burden worldwide. Early detection and timely intervention remain important challenges in COPD management, given the unpredictable nature of acute deterioration and limitations of traditional spirometry-based risk assessment. Methods: This narrative review synthesizes artificial intelligence (AI)-driven approaches for predicting and detecting chronic obstructive pulmonary disease (COPD) exacerbations across electronic health records, wearable sensors, imaging, environmental data, and patient-reported outcomes, emphasizing novel discoveries and emerging relationships rather than predictive performance. Results: Three major discoveries have been made. First, measurable physiological and behavioral deterioration may precede symptom recognition by approximately 7–14 days, thereby establishing a potential intervention window for anticipatory care. Second, machine learning (ML) models integrating pollutant exposure, medication adherence, and clinical characteristics have identified phenotypes with differential environmental sensitivity, including unexpected exposure–adherence interactions. Third, deep neural network analysis of full spirometry curves has revealed structural phenotypes beyond traditional Forced Expiratory Volume (FEV1)-based measures and novel imaging biomarkers. The predictive performance ranges from the Area Under the Curve (AUC) 0.72–0.95, with a pooled meta-analytic AUC of approximately 0.77. Conclusions: AI has uncovered hidden patterns in the progression of COPD, supporting a shift from reactive to anticipatory management. Translation to routine care requires prospective validation, improved interpretability, workflow integration, and generalizability and equity. Full article
(This article belongs to the Special Issue AI-Driven Healthcare Insights)
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18 pages, 930 KB  
Review
Artificial Intelligence and Digital Technologies Against Health Misinformation: A Scoping Review of Public Health Responses
by Angelo Cianciulli, Emanuela Santoro, Roberta Manente, Antonietta Pacifico, Savino Quagliarella, Nicole Bruno, Valentina Schettino and Giovanni Boccia
Healthcare 2025, 13(20), 2623; https://doi.org/10.3390/healthcare13202623 - 18 Oct 2025
Cited by 9 | Viewed by 3436
Abstract
Background/Objectives: The COVID-19 pandemic highlighted how infodemics—an excessive amount of both accurate and misleading information—undermine health responses. Artificial intelligence (AI) and digital tools have been increasingly applied to monitor, detect, and counter health misinformation online. This scoping review aims to systematically map digital [...] Read more.
Background/Objectives: The COVID-19 pandemic highlighted how infodemics—an excessive amount of both accurate and misleading information—undermine health responses. Artificial intelligence (AI) and digital tools have been increasingly applied to monitor, detect, and counter health misinformation online. This scoping review aims to systematically map digital and AI-based interventions, describing their applications, outcomes, ethical and equity implications, and policy frameworks. Methods: This review followed the Joanna Briggs Institute methodology and was reported according to PRISMA-ScR. The protocol was preregistered on the Open Science Framework . Searches were conducted in PubMed/MEDLINE, Scopus, Web of Science, and CINAHL (January 2017–March 2025). Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved by a third reviewer. Data extraction included study characteristics, populations, technologies, outcomes, thematic areas, and domains. Quantitative synthesis used descriptive statistics with 95% confidence intervals. Results: A total of 63 studies were included, most published between 2020 and 2024. The majority originated from the Americas (41.3%), followed by Europe (15.9%), the Western Pacific (9.5%), and other regions; 22.2% had a global scope. The most frequent thematic areas were monitoring/surveillance (54.0%) and health communication (42.9%), followed by education/training, AI/ML model development, and digital engagement tools. The domains most often addressed were applications (63.5%), responsiveness, policies/strategies, ethical concerns, and equity/accessibility. Conclusions: AI and digital tools provide significant contributions in detecting misinformation, strengthening surveillance, and promoting health literacy. However, evidence remains heterogeneous, with geographic imbalances, reliance on proxy outcomes, and limited focus on vulnerable groups. Scaling these interventions requires transparent governance, multilingual datasets, ethical safeguards, and integration into public health infrastructures. Full article
(This article belongs to the Special Issue AI-Driven Healthcare Insights)
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Other

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25 pages, 681 KB  
Systematic Review
A Systematic Review of Topic Modeling Techniques for Electronic Health Records
by Iqra Mehmood, Zoya Zahra, Sarah Iqbal, Ayman Qahmash and Ijaz Hussain
Healthcare 2026, 14(2), 282; https://doi.org/10.3390/healthcare14020282 - 22 Jan 2026
Cited by 4 | Viewed by 1140
Abstract
Background: Electronic Health Records (EHRs) are a rich source of clinical information used for patient monitoring, disease progression analysis, and treatment outcome assessment. However, their large-scale, heterogeneity, and temporal characteristics make them difficult to analyze. Topic modeling has emerged as an effective [...] Read more.
Background: Electronic Health Records (EHRs) are a rich source of clinical information used for patient monitoring, disease progression analysis, and treatment outcome assessment. However, their large-scale, heterogeneity, and temporal characteristics make them difficult to analyze. Topic modeling has emerged as an effective method to extract latent structures, detect disease characteristics, and trace patient trajectories in EHRs. Recent neural and transformer-based approaches such as BERTopic has significantly improved coherence, scalability, and domain adaptability compared to earlier probabilistic models. Methods: This Systematic Literature Review (SLR) examines topic modeling and its variants applied to EHR data over the past decade. We follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to identify, screen, and select relevant studies. The reviewed techniques span traditional probabilistic models, neural embedding-based methods, and temporal extensions designed for pathway and sequence modeling in clinical data. Results: The synthesis covers trends in publication patterns, dataset usage, application domains, and methodological contributions. The reviewed literature demonstrates strengths across different modeling families, while also highlighting challenges related to scalability, interpretability, temporal complexity, and privacy when analyzing large-scale EHRs. Conclusions: Topic modeling continues to play a central role in understanding temporal patterns and latent structures in EHRs. This review also outlines future possibilities for integrating topic modeling with Agentic AI and large language models to enhance clinical decision-making. Overall, this SLR provides researchers and practitioners with a consolidated foundation on temporal topic modeling in EHRs and its potential to advance data-driven healthcare. Full article
(This article belongs to the Special Issue AI-Driven Healthcare Insights)
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