A Comprehensive Review of AI-Based Diagnostic Tools for Early Disease Detection in Healthcare
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Abstract
The rapid evolution of artificial intelligence (AI) has unlocked transformative potential in healthcare, particularly in the early detection of diseases. This review paper provides a comprehensive analysis of AI-based diagnostic tools developed to aid in the early diagnosis of critical conditions, such as cancer, cardiovascular diseases, and neurological disorders. By exploring diverse AI techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), we evaluate their applications in analyzing complex medical data sources like medical imaging, genomics, and electronic health records (EHR). The paper categorizes diagnostic tools based on disease type and AI model architecture, assessing their performance, interpretability, and integration with existing clinical workflows. Furthermore, we address key challenges, including data privacy, ethical considerations, and model generalizability, which impact the adoption of these technologies in healthcare settings. Lastly, this review highlights promising future directions in AI-enabled early disease detection, advocating for improved model transparency, rigorous clinical validation, and advancements in personalized diagnostics.
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