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Applications of Artificial Intelligence in Healthcare

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 206

Special Issue Editor


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Guest Editor
International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou 64002, Yunlin, Taiwan
Interests: artificial intelligence for smart grids; health care; wireless networks; blockchain and other artificial intelligence

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is rapidly revolutionizing healthcare globally, offering emerging and transformative solutions for problem diagnosis, patient treatment, patient management, and medical research. This Special Issue aims to explore the latest advancements, methodologies, and real-world applications of AI in healthcare sectors. The Special Issue serves as a valuable platform for researchers, pharmacists, clinicians, doctors, and policymakers interested in harnessing AI to improve healthcare outcomes. We welcome contributions from multidisciplinary domains to foster collaboration and innovation in this rapidly evolving domain.

We invite high-quality original research articles, reviews and surveys, and case studies and use cases that showcase innovative and emerging AI-driven solutions in areas including, but not limited to, the following:

  • Machine learning and deep learning applications in disease prediction;
  • AI-powered diagnostics and medical imaging;
  • AI in personalized medicine and drug discovery;
  • Natural language processing for clinical decision support;
  • Ethical, regulatory, and privacy considerations in AI-driven healthcare;
  • Robotics and automation in surgery and patient care;
  • AI applications in telemedicine and remote patient monitoring.

We look forward to your expert contributions to this Special Issue on the future of AI’s emergence in healthcare.

Prof. Dr. Nadeem Javaid
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. 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

  • artificial intelligence
  • health care
  • disease
  • cardiovascular
  • asthma
  • pregnancy
  • lungs
  • liver
  • pharmacy
  • hospital

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Published Papers (1 paper)

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Research

16 pages, 1284 KiB  
Article
Voxel-Based Multi-Person Multi-View 3D Pose Estimation in Operating Room
by Junjie Luo, Shuxin Xie, Tianrui Quan, Xuesong Ren and Yubin Miao
Appl. Sci. 2025, 15(16), 9007; https://doi.org/10.3390/app15169007 - 15 Aug 2025
Abstract
The localization and pose estimation of clinicians in the operating room is a critical component for building intelligent perception systems, playing a vital role in enhancing surgical standardization and safety. Multi-view, multi-person 3D pose estimation is a highly challenging task—especially in the operating [...] Read more.
The localization and pose estimation of clinicians in the operating room is a critical component for building intelligent perception systems, playing a vital role in enhancing surgical standardization and safety. Multi-view, multi-person 3D pose estimation is a highly challenging task—especially in the operating room, where the presence of sterile clothing, occlusion from surgical instruments, and limited data availability due to privacy concerns exacerbate the difficulty. While voxel-based 3D pose estimation methods have shown promising results in general scenarios, their performance is significantly challenged in surgical environments with limited camera views and severe occlusions. To address these issues, this paper proposes a fine-grained voxel feature reconstruction method enhanced with depth information, effectively mitigating projection errors caused by reduced viewpoints. Additionally, an attention mechanism is integrated into the encoder–decoder architecture to improve the network’s capacity for global information modeling and enhance the accuracy of keypoint regression. Experiments conducted in real-world operating room scenarios, using the Multi-View Operating Room (MVOR) dataset, demonstrate that the proposed method maintains high accuracy even under limited camera views and outperforms existing state-of-the-art multi-view 3D pose estimation approaches. This work provides a novel and efficient solution for human pose estimation (HPE) in complex medical environments. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare)
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