AI in Bio and Healthcare Informatics

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.

• artificial intelligence (AI)
• healthcare informatics
• bioinformatics
• machine learning
• deep neural network
• medical image analysis