JMATech-JOURNAL OF MEDICAL AI & TECHNOLOGY

Artificial Intelligence-Based Healthcare Technology: A Semantic Literature Review on Disease Diagnosis and Prediction Systems for Heart Disease, Diabetes, and Cancer Using Machine Learning and Deep Learning Algorithms

2026-01-08T08:06:02+00:00

The rapid advancement of Artificial Intelligence (AI) has significantly influenced the development of intelligent healthcare systems, particularly in disease diagnosis and prediction. Machine learning and deep learning techniques have been widely applied to analyze complex medical data, enabling improved diagnostic accuracy and early disease detection. Despite extensive research in this area, existing studies are often fragmented, focusing on specific diseases or algorithms, which limits comprehensive understanding and cross-domain comparison. This study presents a semantic literature review of AI-based healthcare technologies for disease diagnosis and prediction, with a focus on heart disease, diabetes, and cancer. The review systematically analyzes recent peer-reviewed studies published within the last two years, examining employed datasets, machine learning and deep learning algorithms, evaluation metrics, and application contexts. A semantic categorization framework is adopted to identify relationships among disease domains, data types, algorithmic approaches, and performance indicators. The results reveal prevailing research trends, commonly used models, and emerging methodological practices, including the integration of hybrid models, visualization-based evaluation, and explainable AI techniques. Furthermore, this study highlights existing research gaps and challenges related to data heterogeneity, evaluation standardization, and real-world clinical applicability. The findings provide a structured overview of current advancements and offer valuable insights for future research and development of robust AI-driven healthcare systems.

Semantic Literature Analysis and Deep Learning Algorithm Comparison for Identifying Paru-Paru Diseases from X-rays

2026-01-08T07:44:03+00:00

The rapid development of artificial intelligence, particularly deep learning, has significantly improved the performance of medical image analysis, including chest X-ray interpretation for lung disease detection. This study presents a semantic literature analysis combined with a comparative review of deep learning algorithms used to identify pulmonary diseases from chest X-ray images. Rather than proposing a novel algorithm, this research systematically examines and synthesizes existing studies that employ convolutional neural networks (CNN), transfer learning models, and hybrid deep learning architectures for detecting lung-related conditions such as pneumonia, tuberculosis, COVID-19, and lung cancer. The semantic analysis approach is applied to identify research patterns, dominant methodologies, datasets, evaluation metrics, and performance trends across selected publications. Furthermore, a comparative analysis is conducted to evaluate the strengths and limitations of commonly used deep learning models based on accuracy, sensitivity, specificity, and computational efficiency. The findings reveal that transfer learning-based CNN models generally outperform traditional architectures, particularly when applied to limited datasets. This study provides a comprehensive overview of current research trends and offers valuable insights for researchers and practitioners in selecting appropriate deep learning approaches for lung disease identification using X-ray images.

A Review of Semantic Literature on Stroke, Mental Health, and Predictive Genomics: Artificial Intelligence in Medical Diagnosis

2026-01-08T07:43:04+00:00

Artificial Intelligence (AI) has increasingly become a transformative force in modern healthcare by enabling early disease detection, improving diagnostic accuracy, supporting personalized treatment strategies, and enhancing data-driven clinical decision-making. This study presents a semantic literature review that systematically examines and synthesizes existing research on the application of AI in medical diagnosis across three high-impact and clinically significant domains: stroke and ischemic brain events, mental health disorders, and predictive genomics, including neurodegenerative diseases such as Parkinson’s disease and complex genetic conditions like age-related macular degeneration (AMD). Rather than proposing a novel algorithmic model, this review focuses on analyzing and categorizing prior studies that employ machine learning (ML), deep learning (DL), and hybrid AI approaches for disease diagnosis and prediction. A total of 25 internationally peer-reviewed journal articles published between 2015 and 2025 were selected based on predefined inclusion criteria, emphasizing methodological rigor, dataset characteristics, algorithmic performance, and clinical relevance. The semantic review approach allows for the identification of thematic patterns, comparative trends, and knowledge gaps across the selected medical domains. The findings indicate that AI-based diagnostic systems demonstrate strong potential in improving diagnostic sensitivity and specificity, particularly in stroke imaging analysis, mental health disorder classification using behavioral and neuroimaging data, and genomics-based risk prediction. However, challenges related to data heterogeneity, model interpretability, dataset bias, and clinical integration remain significant barriers to widespread implementation. This review highlights emerging research directions and future opportunities for AI-assisted diagnostics, emphasizing the need for explainable AI, standardized datasets, and interdisciplinary collaboration between clinicians and data scientists. Overall, this study provides a comprehensive overview of current advancements and limitations of AI in medical diagnosis, offering valuable insights for researchers, healthcare practitioners, and policymakers.

Integrating Semantic Literature Review and Simple Experiments: Comparison of AI Algorithms in Tropical Disease Case Studies

2026-01-08T08:05:25+00:00

Tropical diseases represent a global health challenge requiring innovative approaches for diagnosis and prediction. This study integrates semantic literature review with comparative experiments of artificial intelligence algorithms to analyze the effectiveness of various AI methods in handling tropical disease case studies. Through systematic analysis of 127 scientific publications from 2018-2025, we identified trends in using machine learning, deep learning, and natural language processing algorithms in tropical health domains. Comparative experiments were conducted on five main algorithms: Support Vector Machine (SVM), Random Forest, Neural Network, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) using datasets of malaria, dengue, and tuberculosis. Results show that CNN achieved the highest accuracy of 94.7% for malaria microscopic image classification, while LSTM performed best for dengue outbreak prediction with an F1-score of 0.89 using temporal data. Random Forest showed the most consistent performance across different diseases with an average accuracy of 87.3%. Semantic analysis revealed research gaps in multi-modal data integration and personalized medicine for tropical diseases. This study provides a comprehensive roadmap for AI implementation in tropical countries' healthcare systems.