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AI-Driven Healthcare: Transforming Patient Care and Outcomes
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AI-Driven Healthcare: Transforming Patient Care and Outcomes

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

Deadline for manuscript submissions: 15 September 2026 | Viewed by 5393

Editor

Prof. Dr. Samina Abidi

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Guest Editor
Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, NS B3H 1V7, Canada
Interests: healthcare knowledge management; clinical decision support system; knowledge computerization; digital health system design and evaluation; AI in medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emergence of Artificial Intelligence (AI) in healthcare is offering transformative solutions to improve patient care and enhance the efficiency and effectiveness of the healthcare system. With a worldwide shortage of healthcare professionals, ageing populations (especially in the developed world), and high prevalence of non-communicable diseases, AI in Healthcare has great potential in restructuring the way we approach patient care, disease management decisions, and resource utilization. AI-based approaches such as machine learning and deep learning are being used for clinical decision support, chronic disease risk prediction, disease prognosis, personalized medicine, and image analysis. Chatbots and AI scribe using Natural Language Processing, a branch of AI, have shown usefulness in automation of routine clinical tasks, such as triage, patient education, automation of clinical documentation, pre-authorization, and clinical trial matching. While AI in healthcare offers great potential in transforming and improving patient care and outcomes, the related challenges, such as data privacy and disparity, digital equity, and replacement of human judgment by AI, must be addressed to ensure safe and ethical use of AI.

We are pleased to invite researchers as well as clinicians to submit original articles and reviews to highlight the advancement, possibilities, complexities, and challenges that AI offers in improving patient care and outcomes.

This special issue aim to explore transformative role of AI in healthcare. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • AI assisted clinical decision support
  • AI enhanced remote patient monitoring
  • AI and ML for early disease detection and prognosis
  • Digital Therapeutics
  • Digital Twins for personalized medicine
  • Real-time AI-based diagnostic tools for clinical used and home healthcare
  • AI and telemedicine
  • NLP approaches, including use of LLMs, for automation of routine clinical tasks and clinical decision support. 
  • Chatbots and LLMs for patient education, disease self-management and patient interaction systems.
  • Patient outcome evaluation of AI based systems
  • Metanalysis, systematic reviews and scoping reviews of existing research on use of AI in healthcare
  • Secure and privacy-compliant architectures for AI in healthcare
  • Domains of digital equity for use of AI in healthcare
  • Legal, ethical and regulatory frameworks for AI in healthcare

I/We look forward to receiving your contributions.

Prof. Dr. Samina Abidi
Guest Editor

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

  • AI in healthcare
  • machine learning in healthcare
  • predictive analytics in healthcare
  • clinical decision support system
  • remote patient monitoring
  • NLP in healthcare
  • LLM and chatbots in healthcare
  • digital twin in healthcare
  • evaluation of AI based systems in healthcare

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

Published Papers (6 papers)

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Research

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13 pages, 2465 KB  
Article
Intelligent Patient Appointment Schedules
by Salma Elhag, Lama Althagafi and Shroog Almouabdi
Healthcare 2026, 14(9), 1195; https://doi.org/10.3390/healthcare14091195 - 29 Apr 2026
Viewed by 1024
Abstract
Background: Hospital appointment systems suffer from extended patient waits, manual interventions, and suboptimal resource allocation, reducing satisfaction and efficiency. Methods: This study develops IPAS using Business Process Analysis (BPA), Bizagi modeling for As-Is/To-Be workflows, SWOT analysis, TQM, and Six Sigma DMAIC. [...] Read more.
Background: Hospital appointment systems suffer from extended patient waits, manual interventions, and suboptimal resource allocation, reducing satisfaction and efficiency. Methods: This study develops IPAS using Business Process Analysis (BPA), Bizagi modeling for As-Is/To-Be workflows, SWOT analysis, TQM, and Six Sigma DMAIC. It integrates ML/NLP with BioBERT-BiLSTM triage (AUC 0.92, F1 0.87) for symptom analysis, specialist matching, and automated booking, validated via Bizagi simulations. Results: Simulations show booking time was reduced 96.3% (155 to 5.73 min) and human intervention was cut 70%, with enhanced patient satisfaction and process capability. Conclusions: IPAS demonstrates simulation-based gains in scheduling efficiency, pending real-world validation. Full article
(This article belongs to the Special Issue AI-Driven Healthcare: Transforming Patient Care and Outcomes)
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23 pages, 2846 KB  
Article
Predicting Emergency Department Patient Arrivals at Hospitals Using Machine Learning Techniques
by Abdulmajeed M. Alenezi, Mahmoud Sameh, Meshal Aljohani and Hosam Alharbi
Healthcare 2026, 14(9), 1191; https://doi.org/10.3390/healthcare14091191 - 29 Apr 2026
Viewed by 794
Abstract
Background/Objective: Emergency Departments (EDs) face persistent challenges with overcrowding, unpredictable patient arrivals, and difficulty forecasting short-term demand. Precise hourly arrival predictions are crucial for effective staffing, optimal resource management, and minimizing entry delays. Methods: This paper develops and evaluates a forecasting framework comparing [...] Read more.
Background/Objective: Emergency Departments (EDs) face persistent challenges with overcrowding, unpredictable patient arrivals, and difficulty forecasting short-term demand. Precise hourly arrival predictions are crucial for effective staffing, optimal resource management, and minimizing entry delays. Methods: This paper develops and evaluates a forecasting framework comparing six approaches (a Seasonal Naive baseline, Exponential Smoothing (ETS), Ridge Regression, LightGBM, a hybrid Temporal Convolutional Network (TCN), and a hybrid Long Short-Term Memory (LSTM) network) using de-identified hourly patient arrival records from an ED in Madinah, Saudi Arabia, covering January–November 2024. A set of 183 engineered features is constructed from cyclical time encodings, weekend and public-holiday indicators, structured autoregressive lags, and volatility measures, with all lag-based features verified to use strictly retrospective information. Models are optimized using Bayesian hyperparameter search and trained under an asymmetric loss function that penalizes underprediction to reflect operational risk. Results: Results on a 14-day hold-out test set show that Ridge Regression achieves the lowest MAE (3.75, R2 = 0.52), with TCN and LSTM essentially tied (MAE 3.80 and 3.85). Diebold–Mariano tests confirm that Ridge, TCN, and LSTM are statistically indistinguishable from one another and that Ridge is marginally significantly better than LightGBM (p=0.028); all four ML models significantly outperform ETS and the Seasonal Naive baseline (p<0.001). On the asymmetric metric, TCN achieves the best AsymRMSE (5.59), reflecting its tendency to err on the safe side of staffing decisions. Robustness is confirmed through sensitivity analysis across penalty factors, feature ablation demonstrating the contribution of each feature group without overfitting, expanding-window cross-validation across three independent monthly test periods, and conformal prediction intervals achieving well-calibrated coverage. Conclusions: These results demonstrate that combining engineered temporal features with either a lightweight linear model or a hybrid sequence model yields accurate hourly ED arrival forecasts; whether the achieved accuracy is operationally sufficient for staffing decisions remains a site-specific question that requires clinical validation beyond the scope of this single-center study. Full article
(This article belongs to the Special Issue AI-Driven Healthcare: Transforming Patient Care and Outcomes)
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18 pages, 1050 KB  
Article
Real-Time Integration of an AI-Based ECG Interpretation System in the Emergency Department: A Pragmatic Alternating-Day Study of Diagnostic Performance and Clinical Process Metrics
by Min Seok Choi, Su Il Kim, Yun Deok Jang, Seong Ju Kim, In Hye Kang and Woong Bin Jeong
Healthcare 2026, 14(7), 968; https://doi.org/10.3390/healthcare14070968 - 7 Apr 2026
Viewed by 942
Abstract
Background/Objectives: Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely recognition of ST-elevation myocardial infarction (STEMI) and initiation of reperfusion therapy in the emergency department (ED). We evaluated the diagnostic performance of a real-time artificial intelligence (AI) ECG interpretation system and its [...] Read more.
Background/Objectives: Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely recognition of ST-elevation myocardial infarction (STEMI) and initiation of reperfusion therapy in the emergency department (ED). We evaluated the diagnostic performance of a real-time artificial intelligence (AI) ECG interpretation system and its pragmatic impact when integrated into routine ED workflows. Methods: This prospective, single-center pragmatic observational study was conducted in a regional emergency medical center ED in Busan, Republic of Korea (1 January–31 December 2024). Consecutive adults (≥18 years) undergoing 12-lead ECG for cardiovascular-related symptoms were enrolled (N = 1524). A predefined alternating-day protocol allocated visits to physician-only interpretation days (physician-days, N = 763) or AI output disclosure days (AI-days, N = 761). Diagnostic performance for STEMI was assessed using paired ECG-level comparisons between physician-alone interpretation and AI output against a blinded expert-panel reference standard; clinical impact outcomes included reperfusion-related time metrics, hospital length of stay (LOS), and in-hospital mortality. Results: Against the expert reference standard, AI showed higher STEMI sensitivity than physician-alone interpretation (96.7% vs. 68.3%; McNemar p = 0.027), while specificity was lower (75.9% vs. 84.5%; p = 0.018). In pragmatic day-level comparisons, door-to-balloon time was shorter on AI-days (40.0 ± 19.81 vs. 47.34 ± 21.90 min; p = 0.001), and time to PCI was significantly reduced among patients with atypical presentations (42.3 ± 18.21 vs. 57.1 ± 20.11 min; p = 0.013). Among admitted patients, hospital LOS was shorter on AI-days (13 ± 9.21 vs. 17 ± 10.31 days; p = 0.010), whereas in-hospital mortality did not differ significantly between groups (17.0% vs. 16.77%; p = 0.191). Conclusions: Real-time AI-ECG integration in the ED was associated with improved STEMI detection sensitivity and shorter reperfusion-related time metrics, particularly in atypical presentations, and with reduced hospital LOS among admitted patients. Short-term mortality was comparable between groups. Further multicenter studies are warranted to confirm generalizability and to balance benefits against potential false-positive-related operational impacts. Full article
(This article belongs to the Special Issue AI-Driven Healthcare: Transforming Patient Care and Outcomes)
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30 pages, 3839 KB  
Systematic Review
Conversational AI in Cognitive and Social Training for People with Dementia: A Systematic Review
by Mark K. K. Chan, Peter H. F. Ng and Karen P. Y. Liu
Healthcare 2026, 14(14), 2106; https://doi.org/10.3390/healthcare14142106 - 14 Jul 2026
Viewed by 366
Abstract
Background: Conversational artificial intelligence (AI), including text-based chatbots, voice-based agents, multimodal systems, and socially assistive robots (SARs), offers a scalable adjunct to therapist-led dementia care. The post-2022 emergence of large language models (LLMs) has accelerated development, yet few reviews apply a unified conversational [...] Read more.
Background: Conversational artificial intelligence (AI), including text-based chatbots, voice-based agents, multimodal systems, and socially assistive robots (SARs), offers a scalable adjunct to therapist-led dementia care. The post-2022 emergence of large language models (LLMs) has accelerated development, yet few reviews apply a unified conversational AI taxonomy across dementia care. This review synthesized the effectiveness, limitations, and implementation challenges of conversational AI across the dementia care continuum. Methods: Six databases (PubMed, Embase, Web of Science, Scopus, IEEE Xplore, ACM Digital Library) were searched for English-language studies (January 2010–March 2026) evaluating conversational AI targeting cognitive, social, or caregiver outcomes. Two reviewers independently screened and extracted data following PRISMA 2020 guidelines; risk of bias used standard tools and findings were synthesized narratively. Protocol: PROSPERO CRD420261333625. Results: Forty studies (8 randomized controlled trials [RCTs], 32 non-randomized) were included. SARs were the largest category (n = 24; 60.0%), followed by text-based chatbots (n = 12; 30.0%), multimodal systems (n = 3; 7.5%), and voice-based chatbots (n = 1; 2.5%). The strongest cognitive evidence came from a social robot RCT (gain of 3.9 points on a 30-point screening measure (p < 0.001). For caregivers, an international RCT (n = 274) showed significant reductions in depression (d = 0.37) and burden (d = 0.34). LLM-based systems produced an 18-fold increase in conversation duration. Speech recognition failure was the most consistently reported technical barrier. Conclusions: Conversational AI shows directional benefit across cognitive, social, and caregiver outcomes. Critical research gaps remain regarding voice-only randomized evidence and adequately powered LLM trials against usual care. Full article
(This article belongs to the Special Issue AI-Driven Healthcare: Transforming Patient Care and Outcomes)
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15 pages, 927 KB  
Perspective
Supporting Parental Decision-Making After Life-Limiting Fetal Diagnoses: The Role of Perinatal Hospice and the NOVA-L Decision Support System
by Margherita Dahò
Healthcare 2026, 14(11), 1516; https://doi.org/10.3390/healthcare14111516 - 29 May 2026
Cited by 1 | Viewed by 352
Abstract
Background: Prenatal diagnosis of life-limiting fetal conditions often leads to counseling focused primarily on therapeutic abortion. Perinatal hospice has emerged as an alternative model of care for families who choose to continue the pregnancy. This paper has two primary aims. First, it [...] Read more.
Background: Prenatal diagnosis of life-limiting fetal conditions often leads to counseling focused primarily on therapeutic abortion. Perinatal hospice has emerged as an alternative model of care for families who choose to continue the pregnancy. This paper has two primary aims. First, it discusses structured perinatal hospice programs and their role in supporting parental decision-making after such diagnoses, with attention to ethical and emotional complexities. Second, the paper introduces NOVA-L (Navigating Options & Vital Assistance for Life-limiting conditions), a conceptual Decision Support System (DSS) designed to complement perinatal hospice care. Methods: The paper provides a conceptual and descriptive analysis of the Comfort Care clinical model. It also outlines the proposed architecture of NOVA-L. DSSs combine clinical guidelines, research data, and outcome registries on digital platforms, providing evidence-based information and AI-supported analytical tools. Their potential adaptation to perinatal hospice care is explored. Results: The Comfort Care model involves interdisciplinary counseling, structured communication, and psychosocial support to facilitate clarification of parental values and care pathways. NOVA-L is presented as a complementary tool that may enhance transparency in risk evaluation and option comparison through accessible interfaces under professional supervision. Conclusions: Structured perinatal hospice programs may enhance clarity and compassion in decision-making. The conceptual integration of AI-supported DSS tools, such as NOVA-L, could strengthen ethically grounded, emotionally sensitive parental support. Full article
(This article belongs to the Special Issue AI-Driven Healthcare: Transforming Patient Care and Outcomes)
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13 pages, 555 KB  
Essay
Governing Generative AI in Healthcare: A Normative Conceptual Framework for Epistemic Authority, Trust, and the Architecture of Responsibility
by Fatma Eren Akgün and Metin Akgün
Healthcare 2026, 14(8), 1098; https://doi.org/10.3390/healthcare14081098 - 20 Apr 2026
Cited by 2 | Viewed by 1197
Abstract
Background/Objectives: Large language models (LLMs) such as ChatGPT are rapidly being integrated into healthcare for tasks ranging from clinical documentation to diagnostic support. Current ethical discussions focus predominantly on bias, privacy, and accuracy, leaving three critical governance questions unresolved: What kind of knowledge [...] Read more.
Background/Objectives: Large language models (LLMs) such as ChatGPT are rapidly being integrated into healthcare for tasks ranging from clinical documentation to diagnostic support. Current ethical discussions focus predominantly on bias, privacy, and accuracy, leaving three critical governance questions unresolved: What kind of knowledge does an LLM output represent in clinical reasoning? When is a clinician’s or patient’s trust in that output justified? Who bears responsibility when an AI-informed decision leads to patient harm? This study proposes the Epistemic Authority–Trust–Responsibility (ETR) Architecture, a normative conceptual framework that addresses these three questions as an integrated governance challenge. Methods: The framework was developed through normative conceptual analysis—a method that constructs governance proposals by synthesising philosophical principles, ethical theories, and empirical evidence. The literature was identified through structured searches of PubMed, PhilPapers, and EUR-Lex (January 2020–March 2026), drawing on the philosophy of medical knowledge, the ethics of trust and testimony, and the moral philosophy of responsibility. Results: The ETR Architecture produces four outputs: (i) a four-tier classification system that distinguishes LLM outputs—from administrative drafts to clinical evidence claims—and matches each tier to appropriate verification requirements; (ii) the concept of the ‘epistemic placebo’, formally defined as a governance measure that creates a documented appearance of compliance while lacking at least one operative element of genuine oversight; (iii) a model specifying four conditions under which trust in healthcare AI is justified; (iv) four testable hypotheses with associated research designs connecting governance design to trust calibration and patient safety. Conclusions: The 2025–2027 regulatory transition period offers a critical window for shaping how healthcare institutions govern AI. We argue that deploying LLMs without explicitly classifying their outputs and building appropriate oversight risks allows governance norms to be set by technology vendors rather than by evidence-informed, patient-centred policy. Full article
(This article belongs to the Special Issue AI-Driven Healthcare: Transforming Patient Care and Outcomes)
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