ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSTICS: ACCURACY, EFFICIENCY, AND CLINICAL IMPACT

Abstract

Artificial Intelligence (AI) has become one of the most transformative technologies in modern healthcare, particularly in the field of medical diagnostics. The increasing complexity of medical data, combined with the limitations of human cognitive capacity, has created a demand for advanced computational tools capable of improving diagnostic accuracy and efficiency. AI technologies, including machine learning (ML), deep learning (DL), and neural networks, have demonstrated remarkable potential in analyzing large-scale clinical datasets, medical imaging, and electronic health records. This study aims to provide a comprehensive evaluation of the role of AI in medical diagnostics, focusing on its impact on accuracy, efficiency, and clinical outcomes. A mixed-methods approach was used, integrating quantitative analysis of diagnostic performance with qualitative insights from healthcare professionals. The findings reveal that AI significantly enhances diagnostic precision, reduces human error, and improves workflow efficiency. However, challenges such as algorithm bias, lack of explainability, and ethical concerns remain critical barriers. Overall, AI is positioned to revolutionize medical diagnostics, but its integration requires careful regulatory and clinical validation.