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Life
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Artificial Intelligence and Computational Models in Understanding Human Diseases
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Artificial Intelligence and Computational Models in Understanding Human Diseases

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: closed (28 November 2025) | Viewed by 3418

Special Issue Editor

Prof. Dr. Sergey K. Aityan

E-Mail Website
Guest Editor
Department of Multidisciplinary Engineering, Northeastern University, Oakland Campus, Oakland, CA 94613, USA
Interests: artificial intelligence; data science; mathematical and computational modeling

Special Issue Information

Dear Colleagues,

Human health is one of the most pressing and critical concerns in modern society. Advances in medicine and healthcare across the globe have significantly improved human health and life expectancy. The application of novel computer technologies, modeling, and, in particular, artificial intelligence (AI) in medical research and healthcare has played a key role in these advancements by identifying the causes of many diseases, enhancing diagnostic accuracy, improving treatment plans, and optimizing healthcare organization.

Computational modeling, simulation, and AI offer a wide range of techniques for a deeper understanding of human diseases, from the biochemical, molecular, genetic, and immune levels to broader behavioral and environmental factors. Accurate diagnostics and treatment plans have long been challenges in complex medical cases. AI empowers medical and healthcare professionals to make precise diagnoses and create individualized treatment plans based on the wealth of knowledge and experience that has accumulated in the field of medicine over many years.

This Special Issue explores how AI and computational models are transforming our understanding of human diseases. It highlights their role in identifying disease origins, improving diagnostic methods, enabling the early detection of life-threatening conditions, and discovering potential treatment options by analyzing large datasets and uncovering complex patterns that may not be easily detectable using traditional methods.

Prof. Dr. Sergey K. Aityan
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Life 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 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
  • AI
  • diagnostics
  • computational medicine
  • medical modeling
  • medical simulation
  • medical data analysis

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Published Papers (1 paper)

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Research

19 pages, 527 KB  
Article
AI-Powered Early Detection of Sepsis in Emergency Medicine
by Sergey Aityan, Rolando Herrero, Abdolreza Mosaddegh, Haitham Tayyar, Ebunoluwa Adebesin, Sai Pranavi Jeedigunta, Hangyeol Kim, Manuel Mersini, Rita Lazzaro, Nicola Iacovazzo and Ciro Gargiulo Isacco
Life 2025, 15(10), 1576; https://doi.org/10.3390/life15101576 - 10 Oct 2025
Viewed by 3019
Abstract
Sepsis remains a critical medical emergency caused by a dysregulated immune response to infection, with timely detection and intervention being essential for improving survival rates. Traditional methods often rely on clinician intuition and structured scoring systems, which may be time-intensive and prone to [...] Read more.
Sepsis remains a critical medical emergency caused by a dysregulated immune response to infection, with timely detection and intervention being essential for improving survival rates. Traditional methods often rely on clinician intuition and structured scoring systems, which may be time-intensive and prone to variability. To address these limitations, Machine Learning (ML) offers a powerful alternative, bringing precision and efficiency to sepsis detection. This study investigates both white-box and complex black-box ML models applied to patient data collected across the continuum of care, including monitoring at the urgent care, en route in ambulances, and diagnostics conducted within hospital emergency department settings themselves. White-box models, such as logistic regression and decision trees, are valued for their interpretability, allowing healthcare providers to understand and trust the reasoning behind predictions. Meanwhile, black-box models like deep neural networks and support vector machines deliver superior accuracy but pose challenges in clinical transparency. This trade-off between explainability and performance is explored in detail, supported by experimental results aimed at identifying the most effective computational strategies for early sepsis recognition across diverse healthcare environments. Full article
(This article belongs to the Special Issue Artificial Intelligence and Computational Models in Understanding Human Diseases)
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