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

Deadline for manuscript submissions: 25 February 2027 | Viewed by 25119

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Prof. Dr. Marie-Pierre Gagnon

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Faculté des Sciences Infirmières, Université Laval, Québec, QC G1V 0A6, Canada
Interests: information and communication technologies for health; behaviour of individuals and health professionals; introduce innovation in practice and organization; synthesis, dissemination and application of knowledge; evaluation of health technologies and interventions; quantitative, qualitative and mixed methods

Special Issue Information

Dear Colleagues,

Artificial intelligence applications in healthcare have proliferated in recent years. However, AI also holds great promise for supporting health services research and the organization of care and services. Whether through the integration of automated knowledge synthesis tools, scribes for the automatic transcription of clinical notes, or support of clinical work, AI can facilitate research and decision making in the healthcare services sector. This Special Issue of Healthcare is an opportunity to explore the current state of knowledge on the benefits and limitations of AI applications in healthcare research and services. Contributive topics would include AI tools for automated data collection and analysis in knowledge synthesis, the impact of AI scribes on patients, providers, and organizations, AI tools to support integrated patient trajectories, and the impact of AI on clinical decision making.

Prof. Dr. Marie-Pierre Gagnon
Guest Editor

Manuscript Submission Information

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Keywords

artificial intelligence
electronic health record
telemedicine
disease diagnosis
decision making
mobile health

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Published Papers (6 papers)

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Research

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29 pages, 565 KB

Open AccessArticle

Healthcare Professionals’ Perceptions of AI-Assisted Clinical Decision-Making in Jordan: A Qualitative Study of Trust, Accountability, System Readiness, and Professional Practice

by Mohammad Abu Assab, Fares Al Bahar, Wael Abu Dayyih, Buthaina Mohammad Alazazmeh, Sewar W. Assaf, Anas Abed, Hayam A. Alrasheed and Zainab Zakaraya

Healthcare 2026, 14(12), 1724; https://doi.org/10.3390/healthcare14121724 - 15 Jun 2026

Viewed by 421

Abstract

Background/Objectives: Artificial intelligence (AI) is increasingly used in clinical decision-support systems, yet its adoption in low- and middle-income countries, including Jordan, remains limited and underexplored. Understanding how healthcare professionals perceive AI-assisted clinical decision-making is essential for safe and contextually appropriate implementation. This study explored healthcare professionals’ perceptions of AI-assisted clinical decision-making in Jordan, with particular attention to trust, accuracy, accountability, professional judgement, digital literacy, and health-system readiness. Medication-related safety and prescribing concerns were examined as secondary cross-cutting issues where they emerged from participants’ accounts. Methods: A qualitative study was conducted using semi-structured, in-depth interviews with 22 purposively sampled healthcare professionals from public, private, and university-affiliated healthcare institutions in Amman, Irbid, and Zarqa. Participants included physicians, nurses, pharmacists, and allied health professionals with varied specialties and levels of seniority. Data were analysed using Braun and Clarke’s reflexive thematic analysis. Member checking, peer debriefing, reflexive memos, and audit trails were used to enhance trustworthiness, and reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ). Results: Eight overarching themes were identified: conditional trust in AI-assisted clinical decision-making; concerns regarding accuracy and confident algorithmic errors; accountability and professional responsibility; AI as an adjunct rather than a substitute for clinical judgement; the influence of experience, specialty, and digital literacy on AI acceptance; Jordanian health-system readiness; privacy, confidentiality, and algorithmic bias; and training requirements for safe AI use. Medication-related safety emerged as a cross-cutting concern, particularly in relation to dosing, polypharmacy, drug–drug and drug–herb interactions, and the risk of over-reliance on AI-generated recommendations. Conclusions: Healthcare professionals in Jordan expressed cautious but constructive views toward AI-assisted clinical decision-making. AI was perceived as potentially useful when used to support, rather than replace, professional judgement. Participants’ accounts suggest that safe implementation depends on local validation, clear accountability frameworks, ethical data governance, interprofessional training, and careful consideration of medication-safety expertise where AI tools influence prescribing or therapeutic decisions. These findings highlight the importance of context-sensitive AI governance strategies that support trustworthy, accountable, and professionally supervised AI adoption in healthcare. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Services Research and Organizations)

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15 pages, 688 KB

Open AccessArticle

Artificial Intelligence: Readiness, Attitudes, and AI-Related Anxiety Among Oncology Nurses

by Elif Dönmez, Gamze Temiz, Burak Mete, Elif Marangoz and Tülay Ortabağ

Healthcare 2026, 14(7), 848; https://doi.org/10.3390/healthcare14070848 - 27 Mar 2026

Cited by 1 | Viewed by 1227

Abstract

Objectives: As artificial intelligence (AI) technologies become increasingly integrated into healthcare systems, understanding healthcare professionals’ psychological responses—particularly AI-related anxiety—has become increasingly important for the safe and effective implementation of these technologies in clinical practice. This study aimed to examine the relationships between oncology nurses’ readiness for artificial intelligence, their attitudes toward artificial intelligence, and their levels of AI-related anxiety. Design: A descriptive, cross-sectional study. Setting: An oncology hospital within a state hospital in Istanbul, Turkey. Participants: A total of 207 oncology nurses working full-time in clinical settings. Methods: Data were collected using an online survey consisting of a demographic information form, the Medical Artificial Intelligence Readiness Scale (MAIRS-MS), the Artificial Intelligence Anxiety Scale (AIAS), and the General Attitudes toward Artificial Intelligence Scale (GAAIS). Spearman correlation analysis, general linear modeling, and conditional mediation analysis were performed using JAMOVI (v2.6.17). A p-value of <0.05 was considered statistically significant. Results: AI-related anxiety was significantly and negatively correlated with both readiness and attitudes toward AI. General linear modeling showed that attitudes toward AI significantly predicted anxiety (β = −0.327, p < 0.001), whereas readiness did not have a direct significant effect. Conditional mediation analysis demonstrated that attitudes fully mediated the relationship between readiness and AI anxiety. The indirect effect of readiness on anxiety through attitudes was stronger among nurses who had received prior AI-related education. While the indirect effect remained significant among untrained nurses, its magnitude was considerably smaller. The total effect of readiness on anxiety was significant only in the untrained group, suggesting that structured education redirects the impact of readiness primarily through attitudes. Conclusions: Attitudes toward artificial intelligence represent the key psychological mechanism linking readiness to AI-related anxiety among oncology nurses. Prior AI education appears to strengthen this relationship by enhancing the association between readiness and attitudes and by being associated with lower anxiety levels. Educational and implementation strategies that emphasize ethical awareness and the development of positive, informed attitudes—rather than focusing solely on technical competence—are likely to be more effective in reducing anxiety and promoting the safe and ethical integration of AI into oncology nursing practice. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Services Research and Organizations)

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28 pages, 863 KB

Open AccessArticle

Integrating Artificial Intelligence (AI) in Primary Health Care (PHC) Systems: A Framework-Guided Comparative Qualitative Study

by Farzaneh Yousefi, Reza Dehnavieh, Maude Laberge, AliAkbar Haghdoost, Maxime Sasseville, Somayeh Noori Hekmat, Mohammad Mehdi Ghaemi and Mohsen Nadali

Healthcare 2026, 14(2), 145; https://doi.org/10.3390/healthcare14020145 - 7 Jan 2026

Cited by 3 | Viewed by 1801

Abstract

Background/Objectives: The integration of artificial intelligence (AI) into primary health care (PHC) holds significant potential to enhance efficiency, equity, and clinical decision-making. However, its implementation remains uneven across contexts. This study aimed to identify the systemic, contextual, and governance-related determinants influencing AI readiness in PHC, comparing two distinct health systems, Quebec (Canada) and Iran. Methods: A qualitative, comparative design was employed. Data were collected through semi-structured interviews and focus group discussions with key informants in both settings. A framework-guided content analysis was conducted based on the four Primary Care Evaluation Tool (PCET): stewardship, financing, resource generation, and service delivery. The analysis explored shared context-specific challenges and requirements for AI implementation in PHC. Results: Analysis revealed that AI readiness is shaped more by systemic coherence rather than technological availability alone. Across both contexts, governance- and financing-related challenges were reported by the majority of participants, alongside limited data interoperability. In Quebec, challenges were more commonly articulated around operational and ethical concerns, including workflow integration, transparency, and professional trust. In contrast, participants in Iran emphasized foundational deficiencies in governance stability, financing mechanisms, and digital infrastructure as primary barriers. Across both settings, adaptive governance, sustainable investment, data standardization, and workforce capacity-building consistently emerged as key requirements for AI integration in PHC. Conclusions: AI readiness in PHC is a multidimensional process, in which implementation priorities must align with system maturity. This comparative analysis underscores that while high-resource systems must prioritize ethical integration and workflow alignment, middle-resource settings require foundational investments in governance and infrastructure. This reinforces that AI readiness is a context-dependent and phased process rather than a one-size-fits-all endeavor. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Services Research and Organizations)

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15 pages, 895 KB

Open AccessArticle

Diagnostic Accuracy of AI-Assisted Focused Cardiac Ultrasound (FOCUS) in Primary Care

by Mihai-Sorin Iacob, Nilima Rajpal Kundnani, Abhinav Sharma, Andrei Iacob, Anca-Raluca Dinu and Simona Ruxanda Dragan

Healthcare 2025, 13(21), 2726; https://doi.org/10.3390/healthcare13212726 - 29 Oct 2025

Cited by 3 | Viewed by 4484

Abstract

Background: Focused cardiac ultrasound (FOCUS) can augment the cardiac exam in primary care but is operator-dependent. We evaluated the diagnostic performance of artificial intelligence-assisted FOCUS (AI-FOCUS) performed by family physicians against cardiologist-performed echocardiography. Methods: This research is a prospective cross-sectional study in primary care; family physicians performed conventional FOCUS and AI-FOCUS, with cardiologist-performed echocardiography within 24 h as the reference standard. The primary outcomes were accuracy, sensitivity/specificity, and agreement (κ). Results: AI-FOCUS achieved 94.33% accuracy (95% CI 93.15–95.35), 89.91% sensitivity, and 96.49% specificity, with excellent agreement compared to cardiologists (κ = 0.88). Among the confirmed abnormalities (32.9% of participants), valvular disease was most frequent (42%), followed by reduced LVEF < 50% (28%) and pericardial effusion (12%). In multivariable analysis, AI-assisted LVEF < 50% (OR = 6.05, p < 0.0001) and valvular abnormalities (OR = 4.05, p < 0.0001) were strong predictors of cardiac pathology. Conclusions: AI-FOCUS performed by trained family physicians showed high diagnostic accuracy and excellent agreement with blinded cardiologist-performed echocardiography for detecting LVEF < 50%, screening-level valvular abnormalities, and pericardial effusion, supporting its use for early detection and triage in primary care. Its ease of use and reproducibility suggest value in settings with limited access to cardiology. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Services Research and Organizations)

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Review

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26 pages, 1892 KB

Open AccessReview

Artificial Intelligence–Driven Tools in Mental Health Service Delivery: A Scoping Review

by Yeshin Woo and Kibum Jung

Healthcare 2026, 14(7), 943; https://doi.org/10.3390/healthcare14070943 - 3 Apr 2026

Cited by 1 | Viewed by 1310

Abstract

Background: Artificial intelligence (AI) holds transformative potential for mental health services. However, existing reviews have predominantly focused on algorithmic accuracy, with limited attention to how these technologies are implemented and integrated into real-world service delivery. This scoping review addresses this gap by examining the contexts in which AI technologies—including large language models (LLMs) and machine learning—are implemented, as well as the factors influencing their sustainable adoption within real-world mental health service systems. Methods: Following the established methodological framework, a systematic search (2015–2026) was conducted in PubMed and Scopus. Two independent reviewers screened an initial pool of 829 records using Zotero and Rayyan to minimize selection bias. Following title, abstract, and full-text screening based on predefined eligibility criteria, 26 studies focusing on real-world AI applications (e.g., clinical settings, community services, and case management) were included in the final synthesis. Results: The findings indicate a rapid acceleration in research, with 50% of included studies (n = 13) published since 2024. AI-driven decision support systems were the most prevalent (50%, n = 13), followed by predictive machine learning models (27%) and generative AI applications (15%). Most tools were designed for clinician use (77%) and implemented in hospital-based settings (46%). Although 46% of studies reported real-world implementation, more than half remained at the pilot stage. Notably, research emphasis has shifted from technical efficacy toward feasibility, and implementation contexts (n = 17). Conclusion: AI in mental health is transitioning from laboratory validation to real-world integration. However, the current landscape remains heavily centered on clinician workflows and screening functions, with limited expansion into community-based recovery and long-term prevention. To move beyond the pilot stage, future initiatives should prioritize seamless workflow integration and the application of structured ethical and implementation frameworks that support clinician–patient relationships. This review provides an evidentiary basis for advancing sustainable, AI-enhanced mental health service delivery. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Services Research and Organizations)

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Other

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21 pages, 899 KB

Open AccessSystematic Review

The Impact of AI Scribes on Streamlining Clinical Documentation: A Systematic Review

by Maxime Sasseville, Farzaneh Yousefi, Steven Ouellet, Florian Naye, Théo Stefan, Valérie Carnovale, Frédéric Bergeron, Linda Ling, Bobby Gheorghiu, Simon Hagens, Samuel Gareau-Lajoie and Annie LeBlanc

Healthcare 2025, 13(12), 1447; https://doi.org/10.3390/healthcare13121447 - 16 Jun 2025

Cited by 62 | Viewed by 14864

Abstract

Background: Burnout among clinicians, including physicians, is a growing concern in healthcare. An overwhelming burden of clinical documentation is a significant contributor. While medical scribes have been employed to mitigate this burden, they have limitations such as cost, training needs, and high turnover rates. Artificial intelligence (AI) scribe systems can transcribe, summarize, and even interpret clinical conversations, offering a potential solution for improving clinician well-being. We aimed to evaluate the effectiveness of AI scribes in streamlining clinical documentation, with a focus on clinician experience, healthcare system efficiency, and patient engagement. Methods: We conducted a systematic review following Cochrane methods and PRISMA guidelines. Two reviewers conducted the selection process independently. Eligible intervention studies included quantitative and mixed-methods studies evaluating AI scribe systems. We summarized the data narratively. Results: Eight studies were included. AI scribes demonstrated positive effects on healthcare provider engagement, with users reporting increased involvement in their workflows. The documentation burden showed signs of improvement, as AI scribes helped alleviate the workload for some participants. Many clinicians have found AI systems to be user-friendly and intuitive, although some have expressed concerns about scribe training and documentation quality. A limited impact on reducing burnout was found, although documentation time improved in some studies. Conclusions: Most of the studies reported in this review involved small sample sizes and specific healthcare settings, limiting the generalizability of the findings to other contexts. Accuracy and consistency can vary significantly depending on the specific technology, model training data, and implementation approach. AI scribes show promise in improving documentation efficiency and clinician workflow, although the evidence remains limited and heterogeneous. Broader and real-world evaluations are needed to confirm their effectiveness and inform responsible implementations. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Services Research and Organizations)

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