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Advancing Clinical Medicine Through Artificial Intelligence (AI) and Digital Technology: 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: 31 December 2026 | Viewed by 9084

Editors

Dr. Antonio Jorge Forte

E-Mail Website
Guest Editor
Division of Plastic Surgery, Department of Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
Interests: plastic surgery; hand surgery; peripheral nerve surgery; migraine surgery; lymphatic surgery; esthetic surgery; hand rejuvenation; facial reanimation; microsurgery; breast surgery
Special Issues, Collections and Topics in MDPI journals
Dr. Syed Ali Haider

E-Mail Website
Co-Guest Editor
Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL 32224, USA
Interests: artificial intelligence; plastic surgery; surgery; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Mark Lifson

E-Mail Website
Co-Guest Editor
Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
Interests: AI & machine learning in healthcare; software as a medical device (SaMD); clinical decision support & predictive analytics; digital health risk management & governance; point-of-care diagnostics & biosensing technologies

Special Issue Information

Dear Colleagues,

It is my pleasure to invite you to contribute to the Special Issue entitled “Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology: 2nd Edition”. This is one new volume; we published nine papers in the first volume. For more details, please visit: https://www.mdpi.com/journal/jcm/special_issues/GIDL446SE9.

This groundbreaking Special Issue delves deep into the profound impact of artificial intelligence (AI) on clinical medicine, showcasing its revolutionary potential to transform healthcare practices. From early disease detection to precision treatment strategies, AI-powered solutions are changing the landscape of patient care. Cutting-edge research articles explore the integration of AI algorithms in medical imaging, assisting radiologists with faster and more accurate diagnoses. Moreover, AI-driven predictive models are being leveraged to identify at-risk patients and optimize treatment plans, resulting in improved patient outcomes and reduced healthcare costs. The Special Issue also delves into AI-enabled clinical decision support systems that aid healthcare professionals in making evidence-based choices and ensuring safer and more efficient patient care. By highlighting the rapid advancements in AI technology, this Special Issue paves the way for a more patient-centric and data-driven future in clinical medicine. We invite researchers to submit original research and review articles.

Dr. Antonio Jorge Forte
Guest Editor

Dr. Syed Ali Haider
Dr. Mark Lifson
Co-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. 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.

Publisher’s Notice

The Special Issue, together with its publications, has been shifted from Section "Intensive Care" to Section "Machine Learning and Artificial Intelligence in Clinical Medicine" on 11 August 2026. The publications remain available in the regular issues in which they were originally published. The Editorial Office confirms that these articles adhered to MDPI's standard editorial process (https://www.mdpi.com/editorial_process).

Keywords

  • artificial intelligence
  • clinical medicine
  • medical imaging
  • precision medicine
  • clinical decision support systems
  • patient care
  • healthcare practices
  • early disease detection
  • patient outcomes

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Related Special Issue

Published Papers (5 papers)

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Research

16 pages, 2427 KB  
Article
Determining a Clinically Applicable Cutoff in AI Algorithms for Predicting Clinical Deterioration: A Workload-Constrained, Alarm-Based Approach
by Jaewon Jang, Yong Jun Choi, Taeyong Sim, Ki-Byung Lee, Ji-Hyun Kim, Eun Young Cho, Yuhyun Choi, Sungsoo Hong, Bo Mi Jung, Soo-Jeong Kim, Won Gi Hong and Jae Hwa Cho
J. Clin. Med. 2026, 15(14), 5753; https://doi.org/10.3390/jcm15145753 - 22 Jul 2026
Viewed by 519
Abstract
Background: This retrospective study introduces an AI-driven VitalCare-Major Adverse Event Score (VC-MAES) developed to predict major in-hospital adverse events and determine optimal cutoff thresholds. VC-MAES was originally developed to predict a composite outcome including unplanned intensive care unit (ICU) transfer, in-hospital cardiac [...] Read more.
Background: This retrospective study introduces an AI-driven VitalCare-Major Adverse Event Score (VC-MAES) developed to predict major in-hospital adverse events and determine optimal cutoff thresholds. VC-MAES was originally developed to predict a composite outcome including unplanned intensive care unit (ICU) transfer, in-hospital cardiac arrest, and mortality; the present study evaluates its performance for the composite of unplanned ICU transfer and in-hospital cardiac arrest. Methods: Patients aged ≥19 years in Yongin Severance Hospital between 1 March 2020, and 31 December 2022, were included. The primary outcome was clinica l deterioration, defined as unplanned ICU transfer or in-hospital cardiac arrest. Secondary outcomes included model performance metrics (area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, F1 score) and optimal alarm frequency (number of alarms per 100 patient-days) across cutoff determination methods (Youden’s index, F1 score, Euclidean distance, and alarm-based approach); they were used to determine optimal cutoff values. 20 July 2026. Results: VC-MAES achieved an area under the ROC curve of 0.895(full evaluable sample) at 6-h intervals, outperforming traditional early warning systems. It exhibited improved sensitivity (0.5853) compared to NEWS (0.3775) and MEWS (0.3049) while maintaining a specificity exceeding 0.95, balancing alarm frequency and predictive accuracy (all figures based on each model’s own full evaluable sample: VC-MAES, n = 7425 events; NEWS, n = 3494; MEWS, n = 3198). On the common prediction time-point sample (n = 1678 events), the alarm-based cutoff yielded a sensitivity of 0.5244 for VC-MAES versus 0.2271 for NEWS and 0.1740 for MEWS, with alarm frequencies of 9.67, 7.07, and 5.58 per 100 patient-days, respectively. When compared on a common set of prediction time-points, VC-MAES (AUROC 0.832 on the common sample, 95% CI: 0.822–0.842) significantly outperformed NEWS (AUROC 0.743, 95% CI: 0.732–0.756; DeLong Z = −14.19, p < 0.0001) and MEWS (AUROC 0.706, 95% CI: 0.693–0.720; DeLong Z = −18.22, p < 0.0001). ROC-based methods, such as Youden’s index and Euclidean distance, were deemed unsuitable for practical clinical use. Conclusions: Conventional ROC-based cutoff selection methods may not be suitable for real-world clinical implementation due to excessive alarm burden. An alarm-based cutoff approach provides a more clinically applicable strategy by balancing predictive performance with manageable alarm frequency. These findings highlight the importance of incorporating clinical usability into cutoff determination for AI-based early warning systems. Full article
(This article belongs to the Special Issue Advancing Clinical Medicine Through Artificial Intelligence (AI) and Digital Technology: 2nd Edition)
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22 pages, 2426 KB  
Article
MidFusionEfficientV2: Improving Ophthalmic Diagnosis with Mid-Level RGB–LBP Fusion and SE Attention
by Julide Kurt Keles, Soner Kiziloluk, Eser Sert, Furkan Talo and Muhammed Yildirim
J. Clin. Med. 2026, 15(6), 2352; https://doi.org/10.3390/jcm15062352 - 19 Mar 2026
Viewed by 790
Abstract
Background/Objectives: Early diagnosis of eye diseases is critically important for enhancing individuals’ quality of life and reducing the risk of vision loss. In this study, a deep learning-based hybrid model called MidFusionEfficientV2 has been proposed to classify eye diseases, including uveitis, conjunctivitis, [...] Read more.
Background/Objectives: Early diagnosis of eye diseases is critically important for enhancing individuals’ quality of life and reducing the risk of vision loss. In this study, a deep learning-based hybrid model called MidFusionEfficientV2 has been proposed to classify eye diseases, including uveitis, conjunctivitis, cataract, eyelid drooping, and normal conditions. Methods: The model presents a dual-branch architecture that combines an RGB image branch with an EfficientNetV2-S architecture and a specialized texture branch based on Local Binary Pattern (LBP) transformation at an intermediate level. Thanks to the Squeeze-and-Excitation (SE) blocks integrated into the LBP branch, channel-based attention mechanisms have been activated, enhancing the prominence of textural features. The features obtained from the RGB and LBP branches were combined at an intermediate level and transferred to the classification stage. Results: Experimental studies on the five-class eye disease dataset from the Mendeley Data platform have shown that the proposed model outperformed six strong models (ResNetV2, ConvNeXt, DenseNet-121, EfficientNet-B1, MobileNetV3 Large, and EfficientNetV2-S) with an accuracy of 98%. Especially in the difficult-to-diagnose uveitis class, recall and F1 scores of 97% and 94%, respectively, were achieved. Conclusions: The results show that a moderate combination of color and texture features significantly improves classification performance, and that MidFusionEfficientV2 offers a reliable and effective solution for the automatic diagnosis of eye diseases. Full article
(This article belongs to the Special Issue Advancing Clinical Medicine Through Artificial Intelligence (AI) and Digital Technology: 2nd Edition)
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26 pages, 5137 KB  
Article
A Cross-Ethnicity Validated Machine Learning Model for the Progression of Chronic Kidney Disease in Individuals over 50 Years Old
by Langkun Wang, Wei Zhang, Xin Zhong, Peng Dou, Yuwei Wu, Xiaonan Zheng and Peng Zhang
J. Clin. Med. 2026, 15(2), 825; https://doi.org/10.3390/jcm15020825 - 20 Jan 2026
Cited by 2 | Viewed by 1660
Abstract
Background/Objectives: Chronic Kidney Disease (CKD) is a global public health burden with a rising prevalence driven by population aging. Existing prediction models, such as the Kidney Failure Risk Equation (KFRE), often lack generalizability across ethnicities and comprehensive systemic indicators. This study aimed [...] Read more.
Background/Objectives: Chronic Kidney Disease (CKD) is a global public health burden with a rising prevalence driven by population aging. Existing prediction models, such as the Kidney Failure Risk Equation (KFRE), often lack generalizability across ethnicities and comprehensive systemic indicators. This study aimed to develop and validate a machine learning model for predicting CKD progression by integrating traditional risk factors with novel composite indicators reflecting systemic health. Methods: Data from the China Health and Retirement Longitudinal Study (CHARLS, n = 2500) was used for model training. External validation was performed using independent cohorts from the English Longitudinal Study of Ageing (ELSA, n = 1200) and the Health and Retirement Study (HRS, n = 1500). Multiple machine learning algorithms, including XGBoost, were employed. Feature engineering incorporated composite indicators such as the frailty index (FI), triglyceride–glucose (TyG) index, and aggregate index of systemic inflammation (AISI). Results: The XGBoost model achieved an area under the curve (AUC) of 0.892 in the training set and maintained robust performance in external validation (AUC 0.867 in ELSA, 0.871 in HRS), outperforming the KFRE (AUC 0.745). SHAP analysis identified the FI as the most influential predictor. Decision curve analysis confirmed the model’s clinical utility. Conclusions: This machine learning model demonstrates high accuracy and cross-ethnicity validity, offering a practical tool for early intervention and personalized CKD management. Future work should address limitations such as the retrospective design and expand validation to underrepresented regions. Full article
(This article belongs to the Special Issue Advancing Clinical Medicine Through Artificial Intelligence (AI) and Digital Technology: 2nd Edition)
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18 pages, 1001 KB  
Article
Artificial Intelligence Physician Avatars for Patient Education: A Pilot Study
by Syed Ali Haider, Srinivasagam Prabha, Cesar Abraham Gomez-Cabello, Ariana Genovese, Bernardo Collaco, Nadia Wood, Mark A. Lifson, Sanjay Bagaria, Cui Tao and Antonio Jorge Forte
J. Clin. Med. 2025, 14(23), 8595; https://doi.org/10.3390/jcm14238595 - 4 Dec 2025
Cited by 7 | Viewed by 3810
Abstract
Background: Generative AI and synthetic media have enabled realistic human Embodied Conversational Agents (ECAs) or avatars. A subset of this technology replicates faces and voices to create realistic likenesses. When combined with avatars, these methods enable the creation of “digital twins” of physicians, [...] Read more.
Background: Generative AI and synthetic media have enabled realistic human Embodied Conversational Agents (ECAs) or avatars. A subset of this technology replicates faces and voices to create realistic likenesses. When combined with avatars, these methods enable the creation of “digital twins” of physicians, offering patients scalable, 24/7 clinical communication outside the immediate clinical environment. This study evaluated surgical patient perceptions of an AI-generated surgeon avatar for postoperative education. Methods: We conducted a pilot feasibility study with 30 plastic surgery patients at Mayo Clinic, USA (July–August 2025). A bespoke interactive surgeon avatar was developed in Python using the HeyGen IV model to reproduce the surgeon’s likeness. Patients interacted with the avatar through natural voice queries, which were mapped to predetermined, pre-recorded video responses covering ten common postoperative topics. Patient perceptions were assessed using validated scales of usability, engagement, trust, eeriness, and realism, supplemented by qualitative feedback. Results: The avatar system reliably answered 297 of 300 patient queries (99%). Usability was excellent (mean System Usability Scale score = 87.7 ± 11.5) and engagement high (mean 4.27 ± 0.23). Trust was the highest-rated domain, with all participants (100%) finding the avatar trustworthy and its information believable. Eeriness was minimal (mean = 1.57 ± 0.48), and 96.7% found the avatar visually pleasing. Most participants (86.6%) recognized the avatar as their surgeon, although many still identified it as artificial; voice resemblance was less convincing (70%). Interestingly, participants with prior exposure to deepfakes demonstrated consistently higher acceptance, rating usability, trust, and engagement 5–10% higher than those without prior exposure. Qualitative feedback highlighted clarity, efficiency, and convenience, while noting limitations in realism and conversational scope. Conclusions: The AI-generated physician avatar achieved high patient acceptance without triggering uncanny valley effects. Transparency about the synthetic nature of the technology enhanced, rather than diminished, trust. Familiarity with the physician and institutional credibility likely played a key role in the high trust scores observed. When implemented transparently and with appropriate safeguards, synthetic physician avatars may offer a scalable solution for postoperative education while preserving trust in clinical relationships. Full article
(This article belongs to the Special Issue Advancing Clinical Medicine Through Artificial Intelligence (AI) and Digital Technology: 2nd Edition)
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13 pages, 1177 KB  
Article
AI-Automated Risk Operative Stratification for Severe Aortic Stenosis: A Proof-of-Concept Study
by Dorian Garin, Diego Arroyo, Ioannis Skalidis, Philippe Di Cicco, Charlie Ferry, Wesley Bennar, Serban Puricel, Pascal Meier, Mario Togni and Stéphane Cook
J. Clin. Med. 2025, 14(23), 8304; https://doi.org/10.3390/jcm14238304 - 22 Nov 2025
Cited by 3 | Viewed by 983
Abstract
Background: Accurate operative risk stratification is essential for treatment selection in severe aortic stenosis. We developed an automated workflow using large language models (LLMs) to replicate Heart Team risk assessment. Methods: We retrospectively analyzed 231 consecutive patients with severe aortic stenosis [...] Read more.
Background: Accurate operative risk stratification is essential for treatment selection in severe aortic stenosis. We developed an automated workflow using large language models (LLMs) to replicate Heart Team risk assessment. Methods: We retrospectively analyzed 231 consecutive patients with severe aortic stenosis evaluated by multidisciplinary Heart Teams (January 2022–December 2024). An automated system using GPT-4o was developed, comprising the following: (1) structured data extraction from clinical dossiers; (2) EuroSCORE II calculation via two methods (algorithmic vs. LLM-based); (3) clinical vignette generation; and (4) risk stratification comparing EuroSCORE-based thresholds versus guideline-integrated LLM approaches with/without EuroSCORE values. The primary endpoint was the risk stratification accuracy of each method compared to Heart Team decisions. Results: Mean age was 79.5 ± 7.7 years, with 58.4% female. The automated workflow processed patients in 32.6 ± 6.4 s. The LLM-calculated EuroSCORE II showed a lower mean difference from Heart Team values (−1.42%, 95% CI −2.32 to −0.53) versus algorithmic calculation (−1.88%, 95% CI −2.38 to −1.38). For risk stratification, the guideline-integrated LLM without EuroSCORE achieved the highest accuracy (90.0%) and AUC (0.93), outperforming both the EuroSCORE-based (accuracy 50.2% for high-risk, AUC 0.63) and guideline-integrated LLM with EuroSCORE approaches (accuracy 82.4%, AUC 0.76). However, systematic hallucinations occurred for cardiovascular risk factors when data were missing. Conclusions: LLMs accurately calculated EuroSCORE II and achieved 90% concordance with multidisciplinary Heart Team decisions. However, hallucinations, reproducibility concerns, and the absence of clinical outcome validation preclude direct clinical application. Full article
(This article belongs to the Special Issue Advancing Clinical Medicine Through Artificial Intelligence (AI) and Digital Technology: 2nd Edition)
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