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AI-Driven Conversational Technologies and Digital Assistants in Nursing and Healthcare:...
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AI-Driven Conversational Technologies and Digital Assistants in Nursing and Healthcare: Adoption, Impact, and Future Directions

A special issue of Nursing Reports (ISSN 2039-4403). This special issue belongs to the section "Artificial Intelligence and Digital Innovations in Nursing Care".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2082

Special Issue Editors

Prof. Dr. Xujuan Zhou

E-Mail Website
Guest Editor
School of Business, University of Southern Queensland, Springfield 4300, Australia
Interests: data and text mining; machine and deep learning; health informatics; business analytics; information retrieval/filtering; recommender systems; sentiment analysis; natural language processing; information systems and management
Special Issues, Collections and Topics in MDPI journals
Dr. Paschalina Lialiou

E-Mail Website
Guest Editor
Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece
Interests: health informatics; health data mining; AI-driven diagnostic tools; nursing informatics; health information systems and management

Special Issue Information

Dear Colleagues,

The growing prevalence of health challenges across all age groups has accelerated the adoption of artificial intelligence (AI) in healthcare, marking a shift toward digitally enabled and patient-centred care. Among emerging innovations, AI-driven conversational technologies, including digital assistants and conversational agents, are increasingly influencing nursing practice and healthcare delivery by improving patient engagement, accessibility, and care coordination.

These AI-powered tools are being applied across diverse healthcare services, such as mental health support, patient education, clinical decision support, appointment scheduling, and administrative workflows. By complementing the expertise of nurses and other healthcare professionals, conversational AI has the potential to enhance clinical decision-making, personalise patient care, and improve efficiency in complex healthcare environments.

Despite these opportunities, the integration of AI-driven conversational technologies raises important challenges related to ethics, privacy, equity, trust, and professional accountability. Their effective deployment requires alignment with existing healthcare regulations, nursing standards, and ethical frameworks to ensure safe, equitable, and responsible use in clinical practice.

This Special Issue aims to explore the design, implementation, evaluation, and impact of AI-driven conversational technologies in nursing and healthcare. We invite original research articles, reviews, and methodological studies that provide evidence-based insights into how these technologies can support nursing care, clinical decision-making, and patient outcomes. The Special Issue serves as an interdisciplinary platform for researchers and practitioners across nursing, healthcare, computer science, and digital health.

Prof. Dr. Xujuan Zhou
Dr. Paschalina Lialiou
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. Nursing Reports is an international peer-reviewed open access monthly 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 1800 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

  • artificial intelligence (AI)
  • AI-driven technologies
  • digital assistants
  • conversational agents
  • nursing care
  • clinical decision-making
  • patient engagement
  • digital health

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Published Papers (3 papers)

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Editorial

Jump to: Research, Review

3 pages, 133 KB  
Editorial
A Nursing and Computer Science Perspective on Confronting Chronic Illness and Environmental Responsibility in AI Research
by S. Raquel Ramos and Rex Ying
Nurs. Rep. 2026, 16(3), 94; https://doi.org/10.3390/nursrep16030094 - 9 Mar 2026
Viewed by 335
Abstract
The exponential growth of artificial intelligence has transformed global information ecosystems, introducing complex technological challenges that extend far beyond its computational capabilities [...] Full article
(This article belongs to the Special Issue AI-Driven Conversational Technologies and Digital Assistants in Nursing and Healthcare: Adoption, Impact, and Future Directions)

Research

Jump to: Editorial, Review

16 pages, 271 KB  
Article
Exploring Nurses’ Perspectives on the Use of Artificial Intelligence Chatbots for Mental Health Support: A Cross-Sectional Study in Greece
by Paschalina Lialiou, Aglaia Katsiroumpa, Parisis Gallos, Olympia Konstantakopoulou, Ioannis Moisoglou, Olga Galani, Maria Tsiachri and Petros Galanis
Nurs. Rep. 2026, 16(4), 133; https://doi.org/10.3390/nursrep16040133 - 13 Apr 2026
Viewed by 238
Abstract
Background/Objectives: Artificial intelligence (AI) has transformed healthcare delivery by revolutionizing the offering opportunities in prognosis, diagnosis, personalized treatment, and improving patient outcomes. However, little is known about the nurses’ attitudes toward the integration of AI-driven conversational technology and AI chatbots into clinical [...] Read more.
Background/Objectives: Artificial intelligence (AI) has transformed healthcare delivery by revolutionizing the offering opportunities in prognosis, diagnosis, personalized treatment, and improving patient outcomes. However, little is known about the nurses’ attitudes toward the integration of AI-driven conversational technology and AI chatbots into clinical practice. The aim of our study was to investigate nurses’ attitudes regarding the use of AI chatbots as a tool for mental health support. Additionally, the study aimed to evaluate their levels of acceptance and fear toward AI, while examining the influence of demographic variables on these attitudes. Methods: A cross-sectional study was conducted. We employed the Artificial Intelligence in Mental Health Scale (AIMHS) to measure attitudes toward the use of AI-powered chatbots for mental health support. Additionally, we utilized the Attitudes Towards Artificial Intelligence Scale (ATAI) to assess nurses’ levels of acceptance and fear regarding artificial intelligence. Results: Technical advantages score in the AIMHS reflected low positive attitudes toward the technical aspect of AI chatbots for mental health support, while personal advantages score showed moderate positive attitudes toward the personal aspect of chatbots. ATAI scores indicated a moderate level of acceptance and fear toward AI. Results from multivariable analysis showed that increased age (b = 0.011, p-value = 0.018) and increased daily engagement with social media and websites (b = 0.058, p-value = 0.002) were significantly associated with more favorable technical attitudes towards AI-based mental health chatbots. Also, male nurses exhibited significantly more favorable attitudes toward AI-based mental health chatbots in terms of perceived personal benefits (b = 0.548, p-value < 0.001). Higher levels of digital technology competence were significantly associated with greater acceptance of artificial intelligence (b = 0.164, p = 0.032). Additionally, male nurses reported significantly higher acceptance of AI compared to their female counterparts (b = 1.587, p < 0.001). We found that lower financial status was significantly associated with heightened fear of AI (b = −0.329, p < 0.001). Conclusions: Nurses generally held moderately positive attitudes toward both AI-based mental health chatbots and AI more broadly. Several demographic factors were found to significantly influence these attitudes. Full article
(This article belongs to the Special Issue AI-Driven Conversational Technologies and Digital Assistants in Nursing and Healthcare: Adoption, Impact, and Future Directions)

Review

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20 pages, 1100 KB  
Review
Educational Applications of AI-Based Chatbots in Nursing: A Scoping Review
by Francisco Fernandes, Rúben Encarnação, José Alves, Carla Pais-Vieira, Suzinara Beatriz Soares de Lima and Paulo Alves
Nurs. Rep. 2026, 16(3), 87; https://doi.org/10.3390/nursrep16030087 - 3 Mar 2026
Viewed by 1190
Abstract
Background/Objectives: The rapid expansion of generative artificial intelligence (AI) and large language model-based chatbots has accelerated their adoption in higher education, including nursing. This scoping review mapped the use of AI-based chatbots in nursing education, including curricular domains, pedagogical approaches, educational outcomes, and [...] Read more.
Background/Objectives: The rapid expansion of generative artificial intelligence (AI) and large language model-based chatbots has accelerated their adoption in higher education, including nursing. This scoping review mapped the use of AI-based chatbots in nursing education, including curricular domains, pedagogical approaches, educational outcomes, and implementation challenges. Methods: A scoping review was conducted following the Joanna Briggs Institute methodology and reported in accordance with the PRISMA-ScR guideline. Searches were performed across major bibliographic databases and grey literature sources. Quantitative, qualitative, and mixed-methods studies addressing the use of AI chatbots in nursing education or professional training were included. Data were extracted using a standardized instrument and synthesized through descriptive statistics and qualitative content analysis. Results: Sixty-six studies (2019–2025) were included, with significant growth observed after 2023. Most studies employed quasi-experimental designs (37.9%) and were implemented in academic settings (83.3%). Application formats varied across online, hybrid, simulation-based, and classroom models. Reported benefits included improved learning performance, clinical reasoning, and student engagement. Key challenges involved the reliability of AI outputs, academic integrity, data protection, and limited institutional governance. Conclusions: AI-based chatbots represent promising tools to enhance nursing education, particularly when integrated into structured pedagogical strategies with active faculty supervision. Their use can support the development of clinical reasoning, student engagement, and personalized learning. However, methodological heterogeneity, ethical concerns, and governance gaps highlight the need for careful implementation and further rigorous research to ensure safe, effective, and pedagogically sound integration. Full article
(This article belongs to the Special Issue AI-Driven Conversational Technologies and Digital Assistants in Nursing and Healthcare: Adoption, Impact, and Future Directions)
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