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: 28 November 2025 | Viewed by 109
Special Issue Editor
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
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Keywords
- artificial intelligence
- AI
- diagnostics
- computational medicine
- medical modeling
- medical simulation
- medical data analysis
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