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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 "Intensive Care".

Deadline for manuscript submissions: 25 February 2026 | Viewed by 775

Special Issue 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-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
  • 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 (2 papers)

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Research

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 (registering DOI) - 4 Dec 2025
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
Viewed by 286
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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