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Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine
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Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Clinical Guidelines".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 2872

Special Issue Editor

Dr. Carlos Escobar

E-Mail Website
Guest Editor
Cardiology Department, University Hospital La Paz-Carlos III, 28046 Madrid, Spain
Interests: heart failure; hypertension; atrial fibrillation; artificial intelligence; digital therapies

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has initiated a paradigm change in healthcare, powered by the increasing availability of healthcare data and the rapid evolution of analytic techniques. It is expanding its footprint in clinical systems including databases, image analysis, evidence-based real-time clinical decision support, and robotics.

Meanwhile, digital therapies are clinically validated computer programs, used in the prevention, treatment, and management of various diseases and disorders, promoting the empowerment of patients and/or their caregivers and facilitating decision making for health professionals. They incorporate advanced technologies (AI, biometric sensors, IoT, virtual reality, augmented reality, etc.), rigorously respecting interoperability and data privacy, and can be integrated into different devices (mobile phones, tablets, smart watches, virtual reality glasses, desktop computers, or others).

The topics of the interest in this Special Issue include, but are not limited to, AI applied to diagnostics and therapeutics, digital therapies (types, utility, regulation, etc.), and telemedicine in clinical medicine.

Dr. Carlos Escobar
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 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. Journal of Clinical Medicine 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 2600 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
  • biometric sensors
  • deep learning
  • digital therapies
  • Internet of Things
  • machine learning
  • virtual reality
  • augmented reality

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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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12 pages, 549 KiB  
Article
Evaluation of ChatGPT Responses About Sexual Activity After Total Hip Arthroplasty: A Comparative Study with Observers of Different Experience Levels
by Batuhan Gencer, Ufuk Arzu, Serdar Satılmış Orhan, Turgut Dinçal and Mehmet Ekinci
J. Clin. Med. 2025, 14(9), 2942; https://doi.org/10.3390/jcm14092942 - 24 Apr 2025
Viewed by 253
Abstract
Background/Objectives: Despite the rising tendency to depend on ChatGPT for medical counselling, it is imperative to evaluate ChatGPT’s capacity to address sensitive subjects that patients often hesitate to discuss with their physicians. The objective of this study was to evaluate the recommendations [...] Read more.
Background/Objectives: Despite the rising tendency to depend on ChatGPT for medical counselling, it is imperative to evaluate ChatGPT’s capacity to address sensitive subjects that patients often hesitate to discuss with their physicians. The objective of this study was to evaluate the recommendations provided by ChatGPT for sexual activity subsequent to total hip arthroplasty (THA) by orthopaedic surgeons with varying degrees of experience, as well as using standardized scoring systems. Methods: Four patient scenarios were developed, reflecting different ages and indications for THA. Twenty-four questions were asked to ChatGPT 4.0, and responses were evaluated by three different orthopaedic surgeons. All responses were also scored using defined standardized scales. Results: No response was found to be ‘faulty’ or ‘partial’ by any of the observers. While the lowest mean score was attributed by the orthopaedic surgeon with less than five years of experience, the highest mean score was attributed by the orthopaedic surgeon with more than 15 years of experience but not actively working in the field of arthroplasty. An analysis of the data across scenarios revealed that in general, the scores decreased in the more specialized scenarios (p > 0.05). Conclusions: ChatGPT shows potential as a supplementary resource for addressing sensitive postoperative questions such as sexual activity after THA. However, its limitations in providing nuanced, patient-specific recommendations highlight the need for further refinement. While ChatGPT can support general patient education, expert clinical guidance remains essential for addressing complex or individualized concerns. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine)
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13 pages, 1078 KiB  
Article
Understanding Cybersickness and Presence in Seated VR: A Foundation for Exploring Therapeutic Applications of Immersive Virtual Environments
by Witold Pawełczyk, Dorota Olejarz, Zofia Gaweł, Magdalena Merta, Aleksandra Nowakowska, Magdalena Nowak, Anna Rutkowska, Ladislav Batalik and Sebastian Rutkowski
J. Clin. Med. 2025, 14(8), 2718; https://doi.org/10.3390/jcm14082718 - 15 Apr 2025
Viewed by 264
Abstract
Background/Objectives: To assess the spatial presence and impact of an immersive virtual reality (VR) walk on symptoms of cybersickness, emotions, and participant engagement, with the aim of providing insights applicable to future therapeutic VR interventions for individuals with limited mobility. Methods: The experiment [...] Read more.
Background/Objectives: To assess the spatial presence and impact of an immersive virtual reality (VR) walk on symptoms of cybersickness, emotions, and participant engagement, with the aim of providing insights applicable to future therapeutic VR interventions for individuals with limited mobility. Methods: The experiment involved 30 healthy individuals who used VR headsets while seated on chairs to experience a 360° virtual tour of the Venice Canals in Los Angeles. The effect of immersion was evaluated using the Virtual Reality Sickness Questionnaire (VRSQ) to measure cybersickness symptoms, the International Positive and Negative Affect Schedule-Short Form (I-PANAS-SF) to assess emotions, the Spatial Presence Experience Scale (SPES) to evaluate spatial presence, and the Flow State Scale (FSS) to quantify the flow state. Results: The results indicated that the virtual walk elicited both positive and negative reactions. The increase in eye strain (+0.66), general discomfort (+0.6), and headache (+0.43) was achieved in the VRSQ scale. Despite experiencing nausea and oculomotor symptoms, participants reported a high level of flow (range of scale items from 3.47 to 3.70), suggesting a beneficial impact of immersion on their well-being. Furthermore, the analysis of the I-PANAS-SF results revealed a predominance of positive emotions, indicating a favorable perception of the experience. However, the SPES scores exhibited variability in the perception of spatial presence (mean spatial presence score 3.74, SD 2.06), likely influenced by the characteristics of the visual material used. Conclusions: Overall, the immersive VR walk, despite the potential risk of cybersickness symptoms, as a seated passive exploration still promoted feelings of satisfaction and fulfillment, allowing the participants to actively engage with the virtual environment. These findings suggest that seated VR experiences hold promise as a tool for promoting well-being, but further research is needed to address cybersickness and optimize VR content for therapeutic use in populations with limited mobility. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine)
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10 pages, 1682 KiB  
Article
The Application of Deep Learning Tools on Medical Reports to Optimize the Input of an Atrial-Fibrillation-Recurrence Predictive Model
by Alain García-Olea, Ane G Domingo-Aldama, Marcos Merino, Koldo Gojenola, Josu Goikoetxea, Aitziber Atutxa and José Miguel Ormaetxe
J. Clin. Med. 2025, 14(7), 2297; https://doi.org/10.3390/jcm14072297 - 27 Mar 2025
Viewed by 377
Abstract
Background: Artificial Intelligence (AI) techniques, particularly Deep Learning (DL) and Natural Language Processing (NLP), have seen exponential growth in the biomedical field. This study focuses on enhancing predictive models for atrial fibrillation (AF) recurrence by extracting valuable data from electronic health records [...] Read more.
Background: Artificial Intelligence (AI) techniques, particularly Deep Learning (DL) and Natural Language Processing (NLP), have seen exponential growth in the biomedical field. This study focuses on enhancing predictive models for atrial fibrillation (AF) recurrence by extracting valuable data from electronic health records (EHRs) and unstructured medical reports. Although existing models show promise, their reliability is hampered by inaccuracies in coded data, with significant false positives and false negatives impacting their performance. To address this, the authors propose an automated system using DL and NLP techniques to process medical reports, extracting key predictive variables, and identifying new AF cases. The main purpose is to improve dataset reliability so future predictive models can respond more accurately Methods and Results: The study analyzed over one million discharge reports, applying regular expressions and DL tools to extract variables and identify AF onset. The performance of DL models, particularly a feedforward neural network combined with tf-idf, demonstrated high accuracy (0.986) in predicting AF onset. The application of DL tools on unstructured text reduced the error rate in AF identification by 50%, achieving an error rate of less than 2%. Conclusions: This work underscores the potential of AI in optimizing dataset accuracy to develop predictive models and consequently improving the healthcare predictions, offering valuable insights for research groups utilizing secondary data for predictive analytics in this particular setting. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine)
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Review

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18 pages, 1087 KiB  
Review
Ethics and Algorithms to Navigate AI’s Emerging Role in Organ Transplantation
by Amankeldi A. Salybekov, Ainur Yerkos, Martin Sedlmayr and Markus Wolfien
J. Clin. Med. 2025, 14(8), 2775; https://doi.org/10.3390/jcm14082775 - 17 Apr 2025
Viewed by 355
Abstract
Background/Objectives: Solid organ transplantation remains a critical life-saving treatment for end-stage organ failure, yet it faces persistent challenges, such as organ scarcity, graft rejection, and postoperative complications. Artificial intelligence (AI) has the potential to address these challenges by revolutionizing transplantation practices. Methods [...] Read more.
Background/Objectives: Solid organ transplantation remains a critical life-saving treatment for end-stage organ failure, yet it faces persistent challenges, such as organ scarcity, graft rejection, and postoperative complications. Artificial intelligence (AI) has the potential to address these challenges by revolutionizing transplantation practices. Methods: This review article explores the diverse applications of AI in solid organ transplantation, focusing on its impact on diagnostics, treatment, and the evolving market landscape. We discuss how machine learning, deep learning, and generative AI are harnessing vast datasets to predict transplant outcomes, personalized immunosuppressive regimens, and optimize patient selection. Additionally, we examine the ethical implications of AI in transplantation and highlight promising AI-driven innovations nearing FDA evaluation. Results: AI improves organ allocation processes, refines predictions for transplant outcomes, and enables tailored immunosuppressive regimens. These advancements contribute to better patient selection and enhance overall transplant success rates. Conclusions: By bridging the gap in organ availability and improving long-term transplant success, AI holds promise to significantly advance the field of solid organ transplantation. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine)
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18 pages, 690 KiB  
Review
Advancements in Artificial Intelligence for Kidney Transplantology: A Comprehensive Review of Current Applications and Predictive Models
by Jakub Mizera, Maciej Pondel, Marta Kepinska, Patryk Jerzak and Mirosław Banasik
J. Clin. Med. 2025, 14(3), 975; https://doi.org/10.3390/jcm14030975 - 3 Feb 2025
Cited by 1 | Viewed by 972
Abstract
Background: Artificial intelligence is rapidly advancing within the domains of medicine and transplantology. In this comprehensive review, we provide an in-depth exploration of current AI methodologies, with a particular emphasis on machine learning and deep learning techniques, and their diverse subtypes. These technologies [...] Read more.
Background: Artificial intelligence is rapidly advancing within the domains of medicine and transplantology. In this comprehensive review, we provide an in-depth exploration of current AI methodologies, with a particular emphasis on machine learning and deep learning techniques, and their diverse subtypes. These technologies are revolutionizing how data are processed, analyzed, and applied in clinical decision making. Methods: A meticulous literature review was conducted with a focus on the application of artificial intelligence in kidney transplantation. Four research questions were formulated to establish the aim of the review. Results: We thoroughly examined the general applications of AI in the medical field, such as feature selection, dimensionality reduction, and clustering, which serve as foundational tools for complex data analysis. This includes the development of predictive models for transplant rejection, the optimization of personalized immunosuppressive therapies, the algorithmic matching of donors and recipients based on multidimensional criteria, and the sophisticated analysis of histopathological images to improve the diagnostic accuracy. Moreover, we present a detailed comparison of existing AI-based algorithms designed to predict kidney graft survival in transplant recipients. In this context, we focus on the variables incorporated into these predictive models, providing a critical analysis of their relative importance and contribution to model performance. Conclusions: This review highlights the significant advancements made possible through AI and underscores its potential to enhance both clinical outcomes and the precision of medical interventions in the field of transplantology. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine)
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Other

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11 pages, 1683 KiB  
Protocol
Multicenter Study Protocol: Research on Evaluation and Detection of Surgical Wound Complications with AI-Based Recognition (REDSCAR-Trial)
by Andrea Craus-Miguel, Alejandro Fernández-Moreno, Ana Isabel Pablo-Leis, Marta Romero-Hernández, Marc Munar, Gabriel Moyà-Alcover, Manuel González-Hidalgo and Juan José Segura-Sampedro
J. Clin. Med. 2025, 14(7), 2210; https://doi.org/10.3390/jcm14072210 - 24 Mar 2025
Viewed by 277
Abstract
Background: The increasing use of telemedicine in surgical care has shown promise in improving patient outcomes and optimizing healthcare resources. Surgical site infections (SSIs) are a major cause of healthcare-associated infections (HAIs), leading to significant economic and health burdens. A pilot study already [...] Read more.
Background: The increasing use of telemedicine in surgical care has shown promise in improving patient outcomes and optimizing healthcare resources. Surgical site infections (SSIs) are a major cause of healthcare-associated infections (HAIs), leading to significant economic and health burdens. A pilot study already demonstrated that RedScar© achieved 100% sensitivity and 83.13% specificity in detecting SSIs. Patients reported high satisfaction regarding comfort, cost-effectiveness, and reduced absenteeism. Methods: This multicenter prospective study will include 168 patients undergoing abdominal surgery. RedScar© utilizes smartphone-based automated infection risk assessments without clinician input. App-based detection will be compared with in-person evaluations. Sensitivity and specificity will be analyzed using receiver operating characteristic (ROC) analysis, while secondary objectives include assessing patient satisfaction and standardizing telematic follow-up. Results: This study aims to evaluate the efficacy of the RedScar© app, sensitivity, specificity in detecting SSIs. Satisfaction regarding comfort, cost-effectiveness, and absenteeism due to telematic detection and the monitoring of SSIs will be recorded too. Conclusions: This study seeks to validate RedScar© as a reliable and scalable tool for postoperative monitoring. By improving early SSI detection, it has the potential to enhance surgical recovery, reduce healthcare costs, and optimize resource utilization. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine)
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