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AI in Bio and Healthcare Informatics
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AI in Bio and Healthcare Informatics

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 1328

Special Issue Editors

Dr. Zubaer Ibna Mannan

E-Mail Website
Guest Editor
Department of Smart Computing, Kyungdong University, 46 Bongpo 4-gil, Goseong-gun, Wonju 24764, Gangwon-do, Republic of Korea
Interests: health informatics; machine learning; deep neural networks; neuromorphic; memristor
Dr. Asif Karim

E-Mail Website
Guest Editor
Faculty of Science and Technology, Charles Darwin University, Casuarina, NT 0810, Australia
Interests: AI-based health informatics; blockchain; cybersecurity
Dr. Nur Alam Md

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Kyungdong University, 46 Bongpo 4-gil, Goseong-gun, Wonju 24764, Gangwon-do, Republic of Korea
Interests: computer vision; neural networks; deep learning

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is transforming bioinformatics and healthcare informatics by providing innovative solutions for analyzing large-scale biological and medical data, enhancing clinical decision-making, personalizing treatment, and optimizing healthcare systems. Natural language processing (NLP), computer vision, deep learning (DL), and machine learning (ML) have facilitated AI-driven breakthroughs in drug discovery, genomics, medical imaging, and healthcare analytics. This Special Issue emphasizes integrating theoretical AI research into real-world clinical implementation by exploring cutting-edge AI applications and methodologies that address critical bioinformatics and healthcare informatics challenges. The scope of this Special Issue encompasses AI-powered approaches for integrating and analyzing multi-omics data, AI-driven diagnostics, disease prediction models, clinical decision support systems, personalized treatment strategies, medical imaging analysis, precision medicine, public health informatics, computational biology, electronic health record (EHR) analytics, disease surveillance, wearable medical technology, and ethical issues related to the use of AI in healthcare. Even though AI applications, such as deep learning for medical imaging and machine learning models for genetic data interpretation, have been well addressed in the literature, there is an increasing demand for a comprehensive perspective that unifies AI applications across the bioinformatics and healthcare domains. The existing literature often focuses on narrow AI-driven solutions, leaving gaps in understanding how AI might be used holistically to integrate multi-omics data, provide personalized healthcare, optimize hospital workflows, and be applied in AI-driven epidemiology. This Special Issue aims to fill these gaps by highlighting research that explores both technological advancements in AI and real-world implementation issues, such as model interpretability, bias, fairness, privacy, data security, and regulatory constraints. This Special Issue will also contribute to the body of literature by discussing the significance of explainable AI (XAI), federated learning, and AI-driven decision support systems in bridging the gap between research and clinical practice.

Dr. Zubaer Ibna Mannan
Dr. Asif Karim
Dr. Nur Alam Md
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. 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 (AI)
  • healthcare informatics
  • bioinformatics
  • machine learning
  • deep neural network
  • medical image analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

20 pages, 351 KiB  
Article
Multi-Level Depression Severity Detection with Deep Transformers and Enhanced Machine Learning Techniques
by Nisar Hussain, Amna Qasim, Gull Mehak, Muhammad Zain, Grigori Sidorov, Alexander Gelbukh and Olga Kolesnikova
AI 2025, 6(7), 157; https://doi.org/10.3390/ai6070157 - 15 Jul 2025
Viewed by 549
Abstract
Depression is now one of the most common mental health concerns in the digital era, calling for powerful computational tools for its detection and its level of severity estimation. A multi-level depression severity detection framework in the Reddit social media network is proposed [...] Read more.
Depression is now one of the most common mental health concerns in the digital era, calling for powerful computational tools for its detection and its level of severity estimation. A multi-level depression severity detection framework in the Reddit social media network is proposed in this study, and posts are classified into four levels: minimum, mild, moderate, and severe. We take a dual approach using classical machine learning (ML) algorithms and recent Transformer-based architectures. For the ML track, we build ten classifiers, including Logistic Regression, SVM, Naive Bayes, Random Forest, XGBoost, Gradient Boosting, K-NN, Decision Tree, AdaBoost, and Extra Trees, with two recently proposed embedding methods, Word2Vec and GloVe embeddings, and we fine-tune them for mental health text classification. Of these, XGBoost yields the highest F1-score of 94.01 using GloVe embeddings. For the deep learning track, we fine-tune ten Transformer models, covering BERT, RoBERTa, XLM-RoBERTa, MentalBERT, BioBERT, RoBERTa-large, DistilBERT, DeBERTa, Longformer, and ALBERT. The highest performance was achieved by the MentalBERT model, with an F1-score of 97.31, followed by RoBERTa (96.27) and RoBERTa-large (96.14). Our results demonstrate that, to the best of the authors’ knowledge, domain-transferred Transformers outperform non-Transformer-based ML methods in capturing subtle linguistic cues indicative of different levels of depression, thereby highlighting their potential for fine-grained mental health monitoring in online settings. Full article
(This article belongs to the Special Issue AI in Bio and Healthcare Informatics)
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32 pages, 6788 KiB  
Article
Knee Osteoarthritis Detection and Classification Using Autoencoders and Extreme Learning Machines
by Jarrar Amjad, Muhammad Zaheer Sajid, Ammar Amjad, Muhammad Fareed Hamid, Ayman Youssef and Muhammad Irfan Sharif
AI 2025, 6(7), 151; https://doi.org/10.3390/ai6070151 - 8 Jul 2025
Viewed by 521
Abstract
Background/Objectives: Knee osteoarthritis (KOA) is a prevalent disorder affecting both older adults and younger individuals, leading to compromised joint function and mobility. Early and accurate detection is critical for effective intervention, as treatment options become increasingly limited as the disease progresses. Traditional diagnostic [...] Read more.
Background/Objectives: Knee osteoarthritis (KOA) is a prevalent disorder affecting both older adults and younger individuals, leading to compromised joint function and mobility. Early and accurate detection is critical for effective intervention, as treatment options become increasingly limited as the disease progresses. Traditional diagnostic methods rely heavily on the expertise of physicians and are susceptible to errors. The demand for utilizing deep learning models in order to automate and improve the accuracy of KOA image classification has been increasing. In this research, a unique deep learning model is presented that employs autoencoders as the primary mechanism for feature extraction, providing a robust solution for KOA classification. Methods: The proposed model differentiates between KOA-positive and KOA-negative images and categorizes the disease into its primary severity levels. Levels of severity range from “healthy knees” (0) to “severe KOA” (4). Symptoms range from typical joint structures to significant joint damage, such as bone spur growth, joint space narrowing, and bone deformation. Two experiments were conducted using different datasets to validate the efficacy of the proposed model. Results: The first experiment used the autoencoder for feature extraction and classification, which reported an accuracy of 96.68%. Another experiment using autoencoders for feature extraction and Extreme Learning Machines for actual classification resulted in an even higher accuracy value of 98.6%. To test the generalizability of the Knee-DNS system, we utilized the Butterfly iQ+ IoT device for image acquisition and Google Colab’s cloud computing services for data processing. Conclusions: This work represents a pioneering application of autoencoder-based deep learning models in the domain of KOA classification, achieving remarkable accuracy and robustness. Full article
(This article belongs to the Special Issue AI in Bio and Healthcare Informatics)
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