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Future Internet
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Artificial Intelligence (AI) in Healthcare: Technologies, Applications, Challenges
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Artificial Intelligence (AI) in Healthcare: Technologies, Applications, Challenges

A Special Issue of Future Internet (ISSN 1999-5903) belonging to the section "Internet of Things".

Deadline for manuscript submissions: 30 October 2026 | Viewed by 14413

Editors

Dr. Daniela Gîfu

E-Mail Website
Guest Editor
Institute of Computer Science, Romanian Academy, Iasi Branch, 700011 Iasi, Romania
Interests: natural language processing; computational linguistics; web of linked data; content analysis; social media and health information; applied and computational statistics; integrated health informatics system; assisted decision systems; research ethics
Special Issues, Collections and Topics in MDPI journals
Dr. Kevin Bretonnel Cohen

E-Mail Website
Guest Editor
Computational Bioscience Program, Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
Interests: spinal cord injury and regeneration; analysis of the speech of suicidal individuals; temporality in health records; information extraction from epilepsy clinic notes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an era where artificial intelligence (AI) is rapidly transforming various sectors, healthcare stands out as a field with immense potential for innovation and improvement. This Special Issue invites original contributions—algorithmic, methodological, empirical, or theoretical—that advance the broader context of health informatics. We aim to bring together practitioners, researchers, and scholars to share insights, examples, use cases, theories, and analyses related to biomedical data.

Submissions are encouraged to explore various dimensions of AI in healthcare, including but not limited to the theory, design, development, evaluation, or deployment of AI technologies. The primary goal of this Special Issue is to establish a respected, international forum for scientific research, emphasizing key areas such as interactive health technologies, advanced analytics, and the security and privacy of healthcare data.

Dr. Daniela Gîfu
Dr. Kevin Bretonnel Cohen
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet 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

  • interactive health technologies
  • natural language processing and text mining in healthcare
  • biomedical data analytics
  • big data in healthcare
  • visual analytics and decision-support systems
  • security and privacy in healthcare data
  • evaluation and validation of AI methods

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

Published Papers (5 papers)

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Research

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16 pages, 1301 KB  
Article
Implementation and Evaluation of an Open-Source Chatbot for Patient Information Leaflets
by Lisa Heiler, Katharina Kirchsteiger, Sten Hanke and Markus Bödenler
Future Internet 2026, 18(3), 139; https://doi.org/10.3390/fi18030139 - 9 Mar 2026
Viewed by 1448
Abstract
Accessing and understanding medication information can be challenging for many people, especially when patient information leaflets (PILs) are long, complex, and printed in small font. This study presents MediChat, an open-source, locally executable chatbot designed to provide reliable, easy-to-read answers to medication-related questions [...] Read more.
Accessing and understanding medication information can be challenging for many people, especially when patient information leaflets (PILs) are long, complex, and printed in small font. This study presents MediChat, an open-source, locally executable chatbot designed to provide reliable, easy-to-read answers to medication-related questions based exclusively on official PILs. MediChat follows a retrieval-augmented generation (RAG) architecture: PILs from the Austrian Medicinal Product Index are received via API, converted to text, split into overlapping chunks, embedded, and stored in a Chroma vector database. From there the top-k relevant chunks are retrieved, and Llama 3.1 generates German responses based on this evidence. The system was evaluated using a hybrid framework. Quantitatively, 200 yes/no questions across ten drugs were answered with 80% accuracy, overall precision 0.977, recall 0.686, F1-score 0.806, and a mean response time of 727 ms. Qualitatively, two personas were used in eight simulated dialogues. Response times were around 1.1–1.3 s, and task completion exceeded 85% with high ratings for relevance and quantity. These results indicate that an open-source RAG chatbot can deliver leaflet-grounded, user-friendly medication information and provide a reproducible template for future healthcare chatbot evaluations. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare: Technologies, Applications, Challenges)
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27 pages, 1470 KB  
Article
User Perceptions of Virtual Consultations and Artificial Intelligence Assistance: A Mixed Methods Study
by Pranavsingh Dhunnoo, Karen McGuigan, Vicky O’Rourke, Bertalan Meskó and Michael McCann
Future Internet 2026, 18(2), 84; https://doi.org/10.3390/fi18020084 - 4 Feb 2026
Cited by 2 | Viewed by 1729
Abstract
Background: In recent years, virtual consultations have emerged as a crucial approach for continuity of chronic care provision, indicating a promising avenue for the future of smart healthcare systems. However, reversions to in-person care highlight persistent limitations, despite notable advantages of remote modalities. [...] Read more.
Background: In recent years, virtual consultations have emerged as a crucial approach for continuity of chronic care provision, indicating a promising avenue for the future of smart healthcare systems. However, reversions to in-person care highlight persistent limitations, despite notable advantages of remote modalities. In parallel, recent developments in artificial intelligence (AI) indicate the potential to enhance remote chronic care, but user perceptions of such assistance and the corresponding human factors remain underexplored. Objective: This mixed methods study aims to better understand the virtual consultation experiences and attitudes toward AI-assisted tools in remote care among patients with noncommunicable chronic conditions and their healthcare professionals (HCPs). It conducts an in-depth examination of the associated human–computer interaction and usability elements of virtual consultations and of potential AI assistance. Methods: Public and Patient Involvement was integrated to run pilots and refine documentations. Semi-structured interviews with patients (n = 10), focus groups with HCPs (n = 15), and an online survey (n = 83) were conducted. Qualitative data was analysed through a reflexive thematic approach. The survey comprised the Telehealth Usability Questionnaire (TUQ) and bespoke items on user AI views, and the data was used to triangulate the qualitative findings. Nonparametric Kruskal–Wallis tests and ε2 effect sizes compared TUQ and AI views scores between current and former virtual consultation user groups. Results: Seven themes emerged from the qualitative data, which were supported by the quantitative findings. The statistical analyses resulted in a mean TUQ total score of 90.6 (SD = 15.0), which indicates high usability and user satisfaction; however, they failed to detect a difference between groups (p > 0.05; ε2 = 0.002–0.032). There was a clear preference for hybrid models, while a lack of empathy was identified during remote interactions. While a notable proportion of users indicated a literacy gap towards AI use in healthcare settings, they expressed cautious openness towards AI assistance, contingent upon transparency, human oversight, and data integrity; indicating a potential gap between competence to judge the technology and willingness to use it. Significant differences in views on AI assistance across groups failed to be detected (p > 0.05; ε2 = 0.005–0.065). Conclusions: Virtual consultations for chronic conditions are widely usable and acceptable, particularly through hybrid approaches. Addressing empathic engagement, holistic patient status, and transparent AI integration can enhance clinical quality and user experiences during remote interactions. However, the low statistical power and failure to detect a difference between groups (likely due to the small sample size) indicate the need for caution when interpreting the quantitative findings. There is also the implicit need to address potential AI literacy gap among users, indicating the need for robust safeguard measures. This study has also identified evidence-based assistive AI features that can potentially enhance virtual consultations. These insights can inform the co-design of evidence-based virtual care platforms, policies and supportive AI tools to sustain remote chronic care delivery. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare: Technologies, Applications, Challenges)
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33 pages, 2277 KB  
Article
Artificial Intelligence for Pneumonia Detection: A Federated Deep Learning Approach in Smart Healthcare
by Ana-Mihaela Vasilevschi, Călin-Alexandru Coman, Marilena Ianculescu and Oana Andreia Coman
Future Internet 2025, 17(12), 562; https://doi.org/10.3390/fi17120562 - 4 Dec 2025
Viewed by 1732
Abstract
Artificial Intelligence (AI) plays an important role in driving innovation in smart healthcare by providing accurate, scalable, and privacy-preserving diagnostic options. Pneumonia is still a major global health issue, and early detection is key to improving patient outcomes. This study proposes a federated [...] Read more.
Artificial Intelligence (AI) plays an important role in driving innovation in smart healthcare by providing accurate, scalable, and privacy-preserving diagnostic options. Pneumonia is still a major global health issue, and early detection is key to improving patient outcomes. This study proposes a federated deep learning (FL) approach for automatic pneumonia detection using chest X-ray images, considering both diagnostic efficacy and data privacy. Two models were developed and tested: a custom-developed convolutional neural network and a VGG16 transfer learning architecture. The framework evaluates diagnostic efficacy in both centralized and federated scenarios, taking into account heterogeneous client distributions and class imbalance. F1-score and accuracy values for the federated models indicate competitive levels, with F1-scores greater than 0.90 for pneumonia, being robust even when the data is not independent and identically distributed. Results confirm that FL could be tested as a privacy-preserving way to manage medical imaging and intelligence across distributed healthcare. This study provides a potential proof of concept of how to incorporate federated AI into smart healthcare and gives direction toward clinically tested and real-world applications. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare: Technologies, Applications, Challenges)
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Review

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29 pages, 2191 KB  
Review
IoT Applications and Challenges in Global Healthcare Systems: A Comprehensive Review
by Fadele Ayotunde Alaba, Alvaro Rocha, Hakeem Adewale Sulaimon and Owamoyo Najeem
Future Internet 2025, 17(12), 549; https://doi.org/10.3390/fi17120549 - 29 Nov 2025
Cited by 4 | Viewed by 4425
Abstract
The Internet of Things (IoT) has influenced the healthcare industry by enabling real-time monitoring, data-driven decision-making, and automation of medical activities. IoT in healthcare comprises a network of interconnected medical devices, sensors, and software systems that gather, analyse, and transmit patient data, enhancing [...] Read more.
The Internet of Things (IoT) has influenced the healthcare industry by enabling real-time monitoring, data-driven decision-making, and automation of medical activities. IoT in healthcare comprises a network of interconnected medical devices, sensors, and software systems that gather, analyse, and transmit patient data, enhancing the efficiency, accuracy, and accessibility of healthcare services. Despite its benefits, the deployment and impact of IoT in healthcare vary between countries due to differences in healthcare infrastructure, regulatory frameworks, and technical advancements. This review highlights how IoT technologies underpin the efficiency of EHR and HIE systems by enabling continuous data flow, interoperability, and real-time patient care. It also addresses the problems involved with IoT adoption, including data privacy concerns, interoperability issues, high implementation costs, and cybersecurity dangers. Additionally, the paper examines future trends in IoT healthcare, including 5G integration, AI-enhanced healthcare analytics, blockchain-based security solutions, and the creation of energy-efficient IoT medical equipment. Through an analysis of worldwide trends and obstacles, this research offers suggestions for policies, methods, and best practices to close the digital healthcare gap and make sure that healthcare solutions powered by the IoT are available, safe, and effective everywhere. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare: Technologies, Applications, Challenges)
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Other

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16 pages, 1392 KB  
Systematic Review
Artificial Intelligence-Enabled Facial Expression Analysis for Mental Health Assessment in Older Adults: A Systematic Review and Research Agenda
by Fernando M. Runzer-Colmenares, Nelson Luis Cahuapaza-Gutierrez, Cielo Cinthya Calderon-Hernandez and Christian Loret de Mola
Future Internet 2025, 17(12), 541; https://doi.org/10.3390/fi17120541 - 26 Nov 2025
Cited by 2 | Viewed by 3162
Abstract
Facial expression analysis using artificial intelligence (AI) represents an emerging approach for assessing mental health, particularly in neurocognitive disorders. This study encompassed observational investigations that assessed facial expressions in individuals aged 60 years and above. A comprehensive literature search was carried out across [...] Read more.
Facial expression analysis using artificial intelligence (AI) represents an emerging approach for assessing mental health, particularly in neurocognitive disorders. This study encompassed observational investigations that assessed facial expressions in individuals aged 60 years and above. A comprehensive literature search was carried out across PubMed, Scopus, EMBASE, and Web of Science. Risk of bias and study quality were assessed using the QUADAS-2 and CLAIM tools. Descriptive analysis and meta-analysis of proportions were performed using STATA version 19. The pooled effect size (ES) was calculated using a random-effects model (DerSimonian–Laird method), and results were presented with corresponding 95% confidence intervals (CI). Six studies were analyzed, comprising a total of 433 participants aged over 60 years, representing diverse AI applications in the detection of neurocognitive disorders. The disorders evaluated included mild cognitive impairment (MCI) (37.4%), dementia (29.3%), and Alzheimer’s disease (AD) (33.3%). Most studies (83.3%) used video-based facial recordings analyzed through deep learning algorithms and emotion recognition models. The pooled meta-analysis demonstrated that AI-based facial recognition algorithms achieved a high overall detection accuracy in older adults (ES = 0.84; 95% CI: 0.77–0.91), with the best performance observed in Alzheimer’s disease (ES = 0.93; 95% CI: 0.89–0.97). AI-based facial analysis demonstrates high, robust, and non-invasive accuracy for the early and differential detection of neurocognitive disorders, including MCI, dementia-related conditions, and AD, in older adults. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Healthcare: Technologies, Applications, Challenges)
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