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Digital Innovations in Healthcare
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Digital Innovations in Healthcare

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 1843

Special Issue Editors

Prof. Dr. Jia-Lien Hsu

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Fu Jen Catholic University, Taipei, Taiwan
Interests: data mining; knowledge engineering; machine learning; medical informatics; multimedia database; music informatics
Dr. Rui Araújo

E-Mail Website
Guest Editor
Institute of Systems and Robotics (ISR-UC), Department of Electrical and Computer Engineering (DEEC-UC), University of Coimbra, Pólo II, PT-3030-290 Coimbra, Portugal
Interests: computational intelligence; intelligent control; computational learning; machine learning; fuzzy systems; neural networks; optimization; modeling; simulation; estimation; prediction; control; big data; robotics; mobile robotics and intelligent vehicles; robot manipulators control; sensing; soft sensors; automation; industrial systems; embedded systems; real-time systems; general architectures and systems for controlling robot manipulators; mobile robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will explore the cutting-edge digital innovations that are transforming the healthcare sector. We invite research that focuses on the development and application of AI and machine learning in healthcare, particularly in enhancing diagnostic accuracy, optimizing patient care, and improving healthcare management systems. Key areas of interest include, but are not limited to, the following: 

  • AI-driven healthcare systems for patient care and operational efficiency;
  • machine learning models for disease diagnosis and outcome prediction;
  • natural language processing (NLP) in medical records analysis;
  • multimodal data fusion for improved healthcare decision-making;
  • real-time monitoring and digital diagnostics in healthcare.

Prof. Dr. Jia-Lien Hsu
Dr. Rui Araújo
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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 2400 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

  • healthcare AI
  • machine learning
  • disease diagnosis
  • medical NLP
  • digital health solutions

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Published Papers (5 papers)

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Research

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42 pages, 2145 KiB  
Article
Uncertainty-Aware Predictive Process Monitoring in Healthcare: Explainable Insights into Probability Calibration for Conformal Prediction
by Maxim Majlatow, Fahim Ahmed Shakil, Andreas Emrich and Nijat Mehdiyev
Appl. Sci. 2025, 15(14), 7925; https://doi.org/10.3390/app15147925 - 16 Jul 2025
Viewed by 170
Abstract
In high-stakes decision-making environments, predictive models must deliver not only high accuracy but also reliable uncertainty estimations and transparent explanations. This study explores the integration of probability calibration techniques with Conformal Prediction (CP) within a predictive process monitoring (PPM) framework tailored to healthcare [...] Read more.
In high-stakes decision-making environments, predictive models must deliver not only high accuracy but also reliable uncertainty estimations and transparent explanations. This study explores the integration of probability calibration techniques with Conformal Prediction (CP) within a predictive process monitoring (PPM) framework tailored to healthcare analytics. CP is renowned for its distribution-free prediction regions and formal coverage guarantees under minimal assumptions; however, its practical utility critically depends on well-calibrated probability estimates. We compare a range of post-hoc calibration methods—including parametric approaches like Platt scaling and Beta calibration, as well as non-parametric techniques such as Isotonic Regression and Spline calibration—to assess their impact on aligning raw model outputs with observed outcomes. By incorporating these calibrated probabilities into the CP framework, our multilayer analysis evaluates improvements in prediction region validity, including tighter coverage gaps and reduced minority error contributions. Furthermore, we employ SHAP-based explainability to explain how calibration influences feature attribution for both high-confidence and ambiguous predictions. Experimental results on process-driven healthcare data indicate that the integration of calibration with CP not only enhances the statistical robustness of uncertainty estimates but also improves the interpretability of predictions, thereby supporting safer and robust clinical decision-making. Full article
(This article belongs to the Special Issue Digital Innovations in Healthcare)
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17 pages, 1312 KiB  
Article
Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
by Yongxiang Zhang and Raymond Y. K. Lau
Appl. Sci. 2025, 15(13), 7114; https://doi.org/10.3390/app15137114 - 24 Jun 2025
Viewed by 271
Abstract
Chatbots, an automatic dialogue system empowered by deep learning-oriented AI technology, have gained increasing attention in healthcare e-services for their ability to provide medical information around the clock. A formidable challenge is that chatbot dialogue systems have difficulty handling queries with unknown intents [...] Read more.
Chatbots, an automatic dialogue system empowered by deep learning-oriented AI technology, have gained increasing attention in healthcare e-services for their ability to provide medical information around the clock. A formidable challenge is that chatbot dialogue systems have difficulty handling queries with unknown intents due to the technical bottleneck and restricted user-intent answering scope. Furthermore, the wide variation in a user’s consultation needs and levels of medical knowledge further complicates the chatbot’s ability to understand natural human language. Failure to deal with unknown intents may lead to a significant risk of incorrect information acquisition. In this study, we develop an unknown intent detection model to facilitate chatbots’ decisions in responding to uncertain queries. Our work focuses on algorithmic innovation for high-risk healthcare scenarios, where asymmetric knowledge between patients and experts exacerbates intent recognition challenges. Given the multi-role context, we propose a novel query representation learning approach involving multiple views from chatbot users, medical experts, and system developers. Unknown intent detection is then accomplished through the transformed representation of each query, leveraging adaptive determination of intent decision boundaries. We conducted laboratory-level experiments and empirically validated the proposed method based on the real-world user query data from the Tianchi lab and medical information from the Xunyiwenyao website. Across all tested unknown intent ratios (25%, 50%, and 75%), our multi-view boundary learning method was proven to outperform all benchmark models on the metrics of accuracy score, macro F1-score, and macro F1-scores over known intent classes and over the unknown intent class. Full article
(This article belongs to the Special Issue Digital Innovations in Healthcare)
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19 pages, 2545 KiB  
Article
MSA K-BERT: A Method for Medical Text Intent Classification
by Yujia Yuan and Guan Xi
Appl. Sci. 2025, 15(12), 6834; https://doi.org/10.3390/app15126834 - 17 Jun 2025
Viewed by 378
Abstract
Improving medical text intent classification accuracy can assist the medical field in achieving more precise diagnoses. However, existing methods suffer from problems such as low accuracy and a lack of knowledge supplementation. To address these challenges, this paper proposes MSA K-BERT, a knowledge-enhanced [...] Read more.
Improving medical text intent classification accuracy can assist the medical field in achieving more precise diagnoses. However, existing methods suffer from problems such as low accuracy and a lack of knowledge supplementation. To address these challenges, this paper proposes MSA K-BERT, a knowledge-enhanced bidirectional encoder representation model that integrates a multi-scale attention (MSA) mechanism to enhance prediction performance while solving critical issues like heterogeneity of embedding spaces and knowledge noise. We systematically validate the reliability of this model on medical text intent classification datasets and compare it with various deep learning models. The research results indicate that MSA K-BERT makes the following key contributions: First, it introduces a knowledge-supported language representation model compatible with BERT, enhancing language representations through the refined injection of knowledge graphs. Second, it adopts a multi-scale attention mechanism to reinforce different feature layers, significantly improving the model’s accuracy and interpretability. Especially in the IMCS-21 dataset, MSA K-BERT achieves precision, recall, and F1 scores of 0.826, 0.794, and 0.810, respectively, all exceeding the current mainstream methods. Full article
(This article belongs to the Special Issue Digital Innovations in Healthcare)
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15 pages, 1958 KiB  
Article
XClinic Sensors: Validating Accuracy in Measuring Range of Motion Across Trauma Conditions
by Ilaria Ruotolo, Giovanni Sellitto, Giovanni Galeoto, Donatella Valente, Emanuele Amadio, Anna Berardi, Francescaroberta Panuccio, Raffaele La Russa, Umberto Guidoni, Gianpietro Volonnino and Paola Frati
Appl. Sci. 2025, 15(9), 4731; https://doi.org/10.3390/app15094731 - 24 Apr 2025
Viewed by 354
Abstract
Background: Accidents and injuries are major causes of chronic disability, leading to a loss of healthy years. Accurate assessment is essential for planning personalized rehabilitation programs. In recent years, wearable sensors have been introduced into research for motion analysis. This study aimed to [...] Read more.
Background: Accidents and injuries are major causes of chronic disability, leading to a loss of healthy years. Accurate assessment is essential for planning personalized rehabilitation programs. In recent years, wearable sensors have been introduced into research for motion analysis. This study aimed to validate the Xclinic wearable sensors for ROM assessment in patients with trauma. Methods: Participants were recruited from the Sapienza University of Rome (September 2023–November 2024) after road accident trauma. The active ROM of the hip, knee, and ankle was assessed bilaterally based on the injury. The SF-36 and other specific tools were also administered. Construct validity was tested using Pearson’s correlation coefficient. Results: A total of 44 participants (mean age 42.7 ± 17.3 years, 69% male) were included. Item-by-item analysis revealed significant correlations, with notable findings related to other outcome measures. Conclusions: The correlation between joint restrictions, functional impairment, and psychosocial factors highlights the need to integrate physical and psychological care into rehabilitation. Further research is needed to refine assessment tools to improve patients’ quality of life. Full article
(This article belongs to the Special Issue Digital Innovations in Healthcare)
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Review

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16 pages, 434 KiB  
Review
New Remote Care Models in Patients with Spinal Cord Injury: A Systematic Review of the Literature
by Gianluca Ciardi, Lucia Pradelli, Andrea Contini, Paola Cortinovis, Anna Di Muzio, Marina Faimali, Caterina Gennari, Vanda Molinari, Fabio Ottilia, Eleonora Saba, Vittorio Casati, Fabio Razza and Gianfranco Lamberti
Appl. Sci. 2025, 15(14), 7888; https://doi.org/10.3390/app15147888 - 15 Jul 2025
Viewed by 143
Abstract
Background: Spinal cord injury is a multisystem disease which compromises independence and quality of life; remote care models represent an opportunity for long-term management of complications. The aim of this study was to explore remote care models for chronic spinal cord injury patients. [...] Read more.
Background: Spinal cord injury is a multisystem disease which compromises independence and quality of life; remote care models represent an opportunity for long-term management of complications. The aim of this study was to explore remote care models for chronic spinal cord injury patients. Methods: A systematic review of the literature was carried out. Five databases (PubMed, CINAHL, Web of Science, Cochrane Library, Google Scholar) were systematically explored with a time limit of five years. Included studies were assessed using Jadad Score and PEDro Scale. Results: Four RCTs were included in this systematic review. In all studies, multidisciplinary home care supported by technology were compared with in-person models. Remote care models were effective in managing pressure injury, infection, and muscle atrophy and improve quality of life. Conclusions: Remote care models can be a key tool for improving self-efficacy, decreasing hospitalizations and preventing long-term mortality. Full article
(This article belongs to the Special Issue Digital Innovations in Healthcare)
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