AI-Driven Diagnostics: Transforming Healthcare from Data to Clinical Decisions

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1783

Special Issue Editors

Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
Interests: artificial intelligence; bioinformatics; computational biology; medical imaging; pattern recognition
Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
Interests: computational biology; artificial intelligence; bioinformatics; drug discovery; deep learning

Special Issue Information

Dear Colleagues,

This special issue of MDPI Diagnostics focuses on the transformational impact of artificial intelligence (AI) in healthcare diagnostics. The use of AI into diagnostic tools has the potential to change healthcare by improving diagnostic accuracy, efficiency, and accessibility, thus improving patient outcomes.

The articles in this special issue cover a wide range of AI-driven diagnostics-related topics, such as the development and validation of novel AI-based diagnostic tools, the integration of AI into medical imaging and pathology, personalized medicine and precision diagnostics, ethical considerations, comparative studies, case studies, challenges and limitations, and the potential impact of AI-driven diagnostics on healthcare systems.

The goal of this special issue is to encourage academics, doctors, and policymakers to investigate the possibilities of artificial intelligence in increasing diagnostic accuracy, efficiency, and patient outcomes, while also contemplating the ethical implications of this technology. We accept manuscripts of all forms that investigate the most recent breakthroughs in AI-driven diagnostics and their potential to improve healthcare.

We believe that this special issue will help advance the area of AI-driven diagnostics and pave the way for more creative solutions in the future, resulting in improved patient care and results.

Dr. Mobeen Ur Rehman
Prof. Dr. Kil-To Chong
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 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. Diagnostics 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
  • machine learning
  • healthcare systems
  • deep learning
  • big data
  • medical imaging
  • personalized medicine
  • precision diagnostics
  • clinical decision support
  • comparative studies
  • healthcare systems
  • genomics
  • digital pathology
  • diagnostics
  • computational biology
  • bioinformatics

Published Papers (2 papers)

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Research

19 pages, 6984 KiB  
Article
Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging
by Cheng-Tang Pan, Rahul Kumar, Zhi-Hong Wen, Chih-Hsuan Wang, Chun-Yung Chang and Yow-Ling Shiue
Diagnostics 2024, 14(5), 500; https://doi.org/10.3390/diagnostics14050500 - 26 Feb 2024
Viewed by 543
Abstract
The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support [...] Read more.
The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study’s findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases. Full article
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10 pages, 2221 KiB  
Article
Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI
by Kyu-Chong Lee, Yongwon Cho, Kyung-Sik Ahn, Hyun-Joon Park, Young-Shin Kang, Sungshin Lee, Dongmin Kim and Chang Ho Kang
Diagnostics 2023, 13(20), 3254; https://doi.org/10.3390/diagnostics13203254 - 19 Oct 2023
Cited by 1 | Viewed by 883
Abstract
This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) [...] Read more.
This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three musculoskeletal radiologists labeled the rotator cuff tear. The YOLO v8 rotator cuff tear detection model was then trained; training was performed with all imaging planes simultaneously and with axial, coronal, and sagittal images separately. The performances of the models were evaluated and compared using receiver operating curves and the area under the curve (AUC). The AUC was the highest when using all imaging planes (0.94; p < 0.05). Among a single imaging plane, the axial plane showed the best performance (AUC: 0.71), followed by the sagittal (AUC: 0.70) and coronal (AUC: 0.68) imaging planes. The sensitivity and accuracy were also the highest in the model with all-plane training (0.98 and 0.96, respectively). Thus, deep-learning-based automatic rotator cuff tear detection can be useful for detecting torn areas in various regions of the rotator cuff in all three imaging planes. Full article
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