AI-Based Medical Image Analysis for Disease Detection

Dávid Tárnoki

AI-Based Medical Image Analysis for Disease Detection

Author(s)	Dávid Tárnoki
Country	Hungary
Abstract	Artificial intelligence-based medical image analysis has emerged as an important technological approach for supporting disease detection, diagnostic assessment, and clinical decision-making. Medical imaging modalities such as X-ray, computed tomography, magnetic resonance imaging, ultrasound, mammography, and digital pathology generate large volumes of complex visual information that can be difficult to evaluate efficiently through conventional approaches alone. AI-based image analysis uses machine learning and deep learning techniques to identify patterns, abnormalities, and clinically relevant features within medical images. This study examines AI-based medical image analysis for disease detection, focusing on image preprocessing, feature extraction, deep learning, diagnostic classification, clinical decision support, and implementation requirements. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that AI can assist in detecting abnormalities associated with cancer, cardiovascular disorders, neurological conditions, pulmonary diseases, and other clinical conditions. Convolutional neural networks and related deep learning architectures can automate feature identification and image classification. However, the reliability of AI-based medical image analysis depends on dataset quality, model validation, interpretability, clinical integration, cybersecurity, ethical governance, and appropriate professional oversight. Challenges also include dataset bias, limited generalizability, computational requirements, regulatory considerations, and resistance to technological adoption. The study concludes that AI-based medical image analysis can strengthen disease detection when integrated with clinical expertise and validated through rigorous healthcare evaluation. Future research should emphasize multimodal imaging, explainable AI, federated learning, real-time analysis, personalized diagnostics, and robust clinical validation.
Keywords	Artificial Intelligence, Medical Image Analysis, Disease Detection, Deep Learning, Machine Learning, Medical Imaging, Computer Vision, Diagnostic Support, Healthcare AI, Clinical Decision Support
Field	Engineering
Published In	Volume 5, Issue 4, July-August 2023
Published On	2023-08-18

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American International Journal of Advanced Research and Development

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AI-Based Medical Image Analysis for Disease Detection

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