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Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine: 2nd Edition

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Clinical Medicine".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 1954

Special Issue Editor

Dr. Carlos Escobar

E-Mail Website
Guest Editor
Cardiology Service, La Paz University Hospital, Madrid, Spain
Interests: heart failure; hypertension; atrial fibrillation; artificial intelligence; digital therapies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is my pleasure to invite you to contribute to the Special Issue entitled "Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine: 2nd Edition". This is one new volume; we published 15 papers in the first volume. For more details, please visit: https://www.mdpi.com/journal/jcm/special_issues/6EBNK25B0N

Artificial intelligence (AI) has initiated a paradigm shift in healthcare, driven by the increasing availability of healthcare data and the rapid advancement of analytical 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 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 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. 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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Related Special Issue

Published Papers (2 papers)

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Research

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23 pages, 38482 KB  
Article
Data-Driven Analysis of Systemic Indicators Linking Stroke-Associated Pneumonia, Delayed Cerebral Ischemia, and Outcome After Aneurysmal Subarachnoid Hemorrhage
by Vanessa Magdalena Swiatek, Conrad-Jakob Schiffner, Tom Tobias Kummer, Lea Ehrhardt, Klaus-Peter Stein, Ali Rashidi, Sylvia Saalfeld, Robert Werdehausen, I. Erol Sandalcioglu and Belal Neyazi
J. Clin. Med. 2026, 15(4), 1359; https://doi.org/10.3390/jcm15041359 - 9 Feb 2026
Viewed by 562
Abstract
Background/Objectives: Delayed cerebral ischemia (DCI) is a major cause of poor outcome after aneurysmal subarachnoid hemorrhage (aSAH). Beyond large-vessel vasospasm, DCI reflects a systemic, multifactorial process involving inflammation, hematologic dysregulation, and organ dysfunction. Stroke-associated pneumonia (SAP), a frequent aSAH complication linked to [...] Read more.
Background/Objectives: Delayed cerebral ischemia (DCI) is a major cause of poor outcome after aneurysmal subarachnoid hemorrhage (aSAH). Beyond large-vessel vasospasm, DCI reflects a systemic, multifactorial process involving inflammation, hematologic dysregulation, and organ dysfunction. Stroke-associated pneumonia (SAP), a frequent aSAH complication linked to stroke-induced immunodepression, may aggravate secondary ischemic injury. Unlike prior studies focusing on classical predictors alone, we included pneumonia and longitudinal respiratory parameters alongside inflammatory, hematologic, and renal markers. Using machine learning, this study aimed to identify predictors of DCI and functional outcome from routinely collected intensive care data. Methods: In this retrospective single-center study, 182 aSAH patients treated in a neurosurgical intensive care unit were included. Clinical data, SAP status, and longitudinal inflammatory, hematologic, renal, and respiratory parameters were extracted. DCI and functional outcome were assessed. Continuous variables were summarized as minimum, maximum, and mean values. Supervised machine learning models combining 12 feature selection methods and 12 classifiers were trained using five-fold cross-validation and evaluated by accuracy, F1-score, and AUC. Results: DCI occurred in 22% of patients, and SAP in 27%. The machine learning models achieved a mean accuracy of 59.7% (F1-score 58.8%, AUC 59.7%) for DCI prediction. No single dominant feature emerged; predictive patterns included leukocyte counts, CRP, erythrocyte indices, platelet variability, renal function, and oxygenation metrics. Functional outcome prediction performed moderately better (mean AUC 65.7%) and shared overlapping predictors. Conclusions: DCI reflects systemic instability in aSAH, with longitudinal inflammatory and respiratory variability outperforming static thresholds. Dynamic risk stratification may enable earlier detection of deterioration, supporting future time-series modeling and external validation. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine: 2nd Edition)
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Review

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24 pages, 4957 KB  
Review
Mitigating Blue-Light Risk in Display-Based Digital Therapeutics: A Practical Framework to Support Clinical Efficacy
by Wonki Hong
J. Clin. Med. 2026, 15(4), 1371; https://doi.org/10.3390/jcm15041371 - 9 Feb 2026
Viewed by 1036
Abstract
Display-driven optical stimuli underpin a major class of clinically validated digital therapeutics (DTx) now expanding from neuropsychiatric disorders to chronic diseases. The display’s optical characteristics—spectral power distribution, luminance, contrast, and temporal modulation—therefore define the delivered dose of these software-based interventions. In this context, [...] Read more.
Display-driven optical stimuli underpin a major class of clinically validated digital therapeutics (DTx) now expanding from neuropsychiatric disorders to chronic diseases. The display’s optical characteristics—spectral power distribution, luminance, contrast, and temporal modulation—therefore define the delivered dose of these software-based interventions. In this context, blue-rich emission in the 450–480 nm band, particularly with evening exposure, can suppress melatonin via melanopsin-mediated intrinsically photo-sensitive retinal ganglion cell (ipRGC) pathways and perturb circadian timing, potentially attenuating therapeutic efficacy. This review summarizes clinical evidence for display-enabled DTx across major indications and synthesizes mechanistic and experimental data linking blue light to sleep and circadian disruption, with downstream mood, cognitive, cardiovascular, and metabolic effects, as well as increased risk of cancer and skin damage. This review distinguishes wavelength-dependent hazards by separating retinal photochemical risk in the roughly 415–450 nm range from circadian-disruptive melanopic effects in the 450–480 nm range, informing spectrum optimization for therapeutic use. It then synthesizes mitigation strategies spanning display emitter spectrum engineering, optical filtering or conversion films, and software controls such as color temperature tuning, high-frequency dimming, metameric spectrum design, and personalized circadian lighting. The review concludes with design, prescription, and standards considerations to align display output with therapeutic intent. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Digital Therapeutics in Clinical Medicine: 2nd Edition)
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