Cn2a-capsnet: a capsule network and CNN-attention based method for COVID-19 chest X-ray image diagnosis
Authors (first, second and last of 5)
Collection
Interdisciplinary: Explainable Artificial Intelligence in Healthcare Applications and Services
The application of artificial intelligence (AI) in the medical field has received a great deal of interest in recent years. AI has demonstrated positive outcomes in a variety of healthcare applications, including diagnostics, drug development, medical image analysis, and personalized therapy, among others. On the other hand, the lack of transparency and interpretability of AI models has been a big worry in the field of healthcare, particularly when it comes to making important choices for the health of patients.
Explainable AI (XAI) has emerged as a promising approach to address this concern. XAI aims to make AI models transparent and interpretable by providing understandable explanations for the decisions they make. XAI has the potential to improve the trustworthiness and adoption of AI in healthcare, as it enables clinicians and patients to understand the underlying reasoning behind the AI model's decision.
This Topical Collection invites researchers to submit original research articles, reviews, and short communications that explore the latest developments and challenges in XAI for healthcare applications and services. The topics of interest include, but are not limited to (1) Interpretable and Explainable Machine Learning Models in Healthcare; (2) Explainable Decision Support Systems in Healthcare; (3) Ethical and legal implications of XAI in healthcare; (4) XAI for Clinical Decision Making; (5) Patient-centric Explainable AI; (6) Interpretability and Explainability of Deep Learning Models in Healthcare; (7) XAI in Medical Imaging and Diagnosis; (8) XAI in Precision Medicine; (9) XAI in personalized medicine and precision health; (10) XAI techniques for healthcare applications such as diagnosis, prognosis, and treatment planning.
This Topical Collection invites researchers to submit original research articles, reviews, and short communications.Editors
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Muhammad Adnan Khan
Dr. Muhammad Adnan Khan, Associate Professor, Riphah International University, Pakistan. Before he joined the above-mentioned University, Khan worked in various academic and industrial roles in Pakistan and the Republic of Korea. Prof. Khan’s research interests primarily include Machine & Deep Learning, Image Processing & Medical Diagnosis, Applications of Computational Intelligence, Federated Machine Learning, and Artificial Intelligence.
Articles (6 in this collection)
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A feasibility study on the adoption of a generative denoising diffusion model for the synthesis of fundus photographs using a small dataset
Authors (first, second and last of 4)
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From video to vital signs: using personal device cameras to measure pulse rate and predict blood pressure using explainable AI
Authors (first, second and last of 4)
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Pseudo labelling workflow, margin losses, hard triplet mining, and PENViT backbone for explainable age and biological gender estimation using dental panoramic radiographs
Authors (first, second and last of 7)
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A machine learning approach to predict the parameters of COVID‐19 severity to improve the diagnosis protocol in Oman
Authors (first, second and last of 4)
