Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management

A special issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: 30 August 2025 | Viewed by 658

Special Issue Editor


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Guest Editor
School of Computer and Cyber Sciences, Augusta University, Augusta, GA, USA
Interests: biomedical signal processing; medical imaging; machine learning; deep learning; neuromorphic computing

Special Issue Information

Dear Colleagues,

Artificial intelligence has revolutionized healthcare by changing how diseases are diagnosed and managed. This technology is not only enhancing the precision of diagnoses but also enabling disease prediction and personalized treatment plans. Through accurate diagnosis and more personalized therapy, AI is significantly improving healthcare research and patient outcomes. The capacity of AI in healthcare to rapidly assess massive amounts of clinical data enables physicians to identify abnormalities and disease biomarkers that would otherwise be undetected. Recognizing and leveraging these transformative technologies to enhance patient care is crucial. As biomedical informatics continues to evolve, there is a growing need to integrate AI-driven approaches into healthcare.

This Special Issue invites submissions presenting solutions focusing on cutting-edge AI methodologies applied to various medical challenges. The issue will feature research on AI-driven disease prediction, diagnosis, and management, leveraging electronic health records, genomics, medical imaging, and wearable sensor data to inform clinical decision-making. Topics of this Special Issue include, but are not restricted to, the following:

  • AI-based clinical decision support systems;
  • Medical image analysis for computer-aided diagnosis;
  • Large language models in health informatics;
  • Time-series analysis for disease progression modeling;
  • Multimodal fusion for disease detection and treatment;
  • Medical AI for wearable and pervasive sensing;
  • Digital twin and cognitive AI;
  • AI tools for healthcare management;
  • Disease biomarker identification for systemic conditions;
  • Biomedical Generative AI;
  • Personalized medicine and treatment response prediction;
  • Integration of multi-omics data using AI for disease characterization. 

Dr. Hisham Daoud
Guest Editor

Manuscript Submission Information

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Keywords

  • computer-aided diagnostics and treatment
  • deep learning
  • machine learning
  • medical image analysis
  • health informatics
  • personalized medicine
  • disease prediction
  • disease diagnosis
  • healthcare management

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Published Papers (1 paper)

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Review

34 pages, 3510 KiB  
Review
Advancing Brain Tumor Analysis: Current Trends, Key Challenges, and Perspectives in Deep Learning-Based Brain MRI Tumor Diagnosis
by Namya Musthafa, Qurban A. Memon and Mohammad M. Masud
Eng 2025, 6(5), 82; https://doi.org/10.3390/eng6050082 - 22 Apr 2025
Viewed by 418
Abstract
Brain tumors pose a significant challenge in medical research due to their associated morbidity and mortality. Magnetic Resonance Imaging (MRI) is the premier imaging technique for analyzing these tumors without invasive procedures. Recent years have witnessed remarkable progress in brain tumor detection, classification, [...] Read more.
Brain tumors pose a significant challenge in medical research due to their associated morbidity and mortality. Magnetic Resonance Imaging (MRI) is the premier imaging technique for analyzing these tumors without invasive procedures. Recent years have witnessed remarkable progress in brain tumor detection, classification, and progression analysis using MRI data, largely fueled by advancements in deep learning (DL) models and the growing availability of comprehensive datasets. This article investigates the cutting-edge DL models applied to MRI data for brain tumor diagnosis and prognosis. The study also analyzes experimental results from the past two decades along with technical challenges encountered. The developed datasets for diagnosis and prognosis, efforts behind the regulatory framework, inconsistencies in benchmarking, and clinical translation are also highlighted. Finally, this article identifies long-term research trends and several promising avenues for future research in this critical area. Full article
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