Main Risk Factors for The Development of Diabetic Foot
Keywords:
Artificial intelligence, machine learning, early diagnosisAbstract
Artificial intelligence (AI) has emerged as a transformative technology in modern biomedical research and clinical practice. Advances in machine learning and deep learning algorithms have enabled automated analysis of complex biomedical datasets, including medical imaging, genomic information, and electronic health records. These technologies have demonstrated promising capabilities in the early detection of diseases such as cancer, cardiovascular disorders, and neurological conditions. Early diagnosis is critical for improving patient outcomes and reducing healthcare costs. This paper reviews recent developments in AI-driven diagnostic systems, explores their applications across different medical fields, and discusses the challenges associated with implementation, including data quality, algorithm bias, ethical concerns, and regulatory considerations. While AI has the potential to significantly enhance diagnostic accuracy and support clinical decisionmaking, successful integration into healthcare systems requires robust validation, transparency, and interdisciplinary collaboration. The review highlights emerging trends and future opportunities for AI in early disease detection
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