APPLICATIONS OF AI-DRIVEN DATA ANALYTICS FOR EARLY DIAGNOSIS IN COMPLEX MEDICAL CONDITIONS
Keywords:
Artificial Intelligence, Early Diagnosis, Machine Learning, Deep Learning, HealthcareAbstract
Artificial intelligence (AI) has emerged as a transformative tool in the early diagnosis of complex medical conditions, enhancing diagnostic accuracy and enabling timely interventions that improve patient outcomes. This paper explores the applications of AI-driven data analytics in the early detection of diseases such as cardiovascular disorders, neurological conditions, and cancers. A comprehensive literature review highlights key studies and AI methodologies, including deep learning and machine learning models, that are advancing diagnostic precision. Case studies demonstrate real-world implementations, illustrating the impact of AI in clinical settings and its potential to support healthcare providers. Comparative analyses reveal that AI models, when properly optimized and tailored to specific diagnostic tasks, outperform traditional methods across various performance metrics. Despite its promise, the integration of AI into healthcare presents ethical challenges, particularly regarding patient privacy, data security, model bias, and transparency. This paper underscores the need for rigorous data governance and ethical considerations to ensure the responsible use of AI in diagnostics. By addressing these challenges, AI-driven analytics can become a powerful asset in early diagnosis, revolutionizing preventative medicine and improving patient care.
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