AI-Driven Innovations in Medical Computer Engineering and Healthcare

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "Medical & Healthcare AI".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 433

Special Issue Editors


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Guest Editor
School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou, China
Interests: fetal ultrasound; medical image and video analysis; ubiquitous/pervasive computing; transfer learning; federated learning
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Guest Editor
School of Computer Science, University of Leeds, Leeds, UK
Interests: medical image analysis; machine learning; computer vision; cardiovascular imaging; biomedical signal processing

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Guest Editor
School of Information Technology, Halmstad University, Halmstad, Sweden
Interests: machine learning; explainable AI; privacy-preserving; data analytics, service design and business intelligence

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Guest Editor
Department of Computer Sciences, Ensenada Center for Scientific Research and Higher Education, Ensenada, Mexico
Interests: machine learning; wearable computing; human motion analysis; activity recognition
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Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is driving advancements in diagnostics, treatment planning, and healthcare management by transforming how medical data—including imaging, physiological signals, and electronic health records—are processed, analyzed, and applied in clinical and research settings.

Innovations in machine learning, deep learning, and natural language processing are enhancing the accuracy and accessibility of healthcare solutions, including in resource-limited environments. These developments democratize decision-making support, enabling precise disease detection, predictive analytics, personalized medicine, and more efficient healthcare delivery.

This Special Issue welcomes contributions exploring cutting-edge developments in AI-driven computer engineering for medical applications, including but not limited to the following:

  • AI-powered medical image and video analysis for diagnosis and disease monitoring.
  • Machine learning applications in electronic health records, predictive analytics, and clinical decision support.
  • Computational approaches in personalized medicine.
  • AI-enhanced physiological signal processing (e.g., ECG, EEG, and wearable sensor data).
  • Telemedicine, remote patient monitoring, and AI-enabled healthcare automation.
  • Security, privacy, and ethical considerations in AI-driven medical technologies.
  • Explainability and interpretability of AI models in clinical decision-making and patient care.
  • AI systems with impact on healthcare accessibility, including their use in resource-limited or underserved environments.

Please note that this Special Issue does not cover contributions related to computational chemistry, biomolecular structure and dynamics, or drug discovery.

We aim to highlight interdisciplinary research that bridges AI, computer engineering, and medicine, showcasing how intelligent computational methods are advancing healthcare, improving patient outcomes, and shaping the future of medical practice.

Dr. Netzahualcoyotl Hernandez-Cruz
Dr. Ping Lu
Dr. Jens Lundström
Dr. Irvin Hussein Lopez-Nava
Guest Editors

Manuscript Submission Information

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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. AI is an international peer-reviewed open access monthly 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 1600 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 diagnostics
  • clinical decision support
  • telemedicine
  • remote monitoring
  • wearables
  • predictive analytics
  • biomedical signals
  • personalized medicine
  • healthcare automation
  • explainability

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Published Papers (1 paper)

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Research

21 pages, 1331 KB  
Article
Improved Productivity Using Deep Learning-Assisted Major Coronal Curve Measurement on Scoliosis Radiographs
by Xi Zhen Low, Mohammad Shaheryar Furqan, Kian Wei Ng, Andrew Makmur, Desmond Shi Wei Lim, Tricia Kuah, Aric Lee, You Jun Lee, Ren Wei Liu, Shilin Wang, Hui Wen Natalie Tan, Si Jian Hui, Xinyi Lim, Dexter Seow, Yiong Huak Chan, Premila Hirubalan, Lakshmi Kumar, Jiong Hao Jonathan Tan, Leok-Lim Lau and James Thomas Patrick Decourcy Hallinan
AI 2025, 6(12), 318; https://doi.org/10.3390/ai6120318 - 5 Dec 2025
Abstract
Background: Deep learning models have the potential to enable fast and consistent interpretations of scoliosis radiographs. This study aims to assess the impact of deep learning assistance on the speed and accuracy of clinicians in measuring major coronal curves on scoliosis radiographs. Methods: [...] Read more.
Background: Deep learning models have the potential to enable fast and consistent interpretations of scoliosis radiographs. This study aims to assess the impact of deep learning assistance on the speed and accuracy of clinicians in measuring major coronal curves on scoliosis radiographs. Methods: We utilized a deep learning model (Context Axial Reverse Attention Network, or CaraNet) to assist in measuring Cobb’s angles on scoliosis radiographs in a simulated clinical setting. Four trainee radiologists with no prior experience and four trainee orthopedists with four to six months of prior experience analyzed the radiographs retrospectively, both with and without deep learning assistance, using a six-week washout period. We recorded the interpretation time and mean angle differences, with a consultant spine surgeon providing the reference standard. The dataset consisted of 640 radiographs from 640 scoliosis patients, aged 10–18 years; we divided the dataset into 75% for training, 16% for validation, and 9% for testing. Results: Deep learning assistance achieved non-statistically significant improvements in mean accuracy of 0.32 for trainee orthopedists (95% CI −1.4 to 0.8, p > 0.05) and 0.43 degrees (95% CI −1.6 to 0.8, p > 0.05) for trainee radiologists (non-inferior across all readers). Mean interpretation time decreased by 13.25 s for trainee radiologists, but increased by 3.85 s for trainee orthopedists (p = 0.005). Conclusions: Deep learning assistance for measuring Cobb’s angles was as accurate as unaided interpretation and slightly improved measurement accuracy. It increased the interpretation speeds of trainee radiologists but slightly slowed trainee orthopedists, suggesting that its effect on speed depended on prior experience. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Medical Computer Engineering and Healthcare)
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