EXPLORING THE ROLE OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING MEDICAL IMAGING FOR DIAGNOSTICS

This paper investigates the role of artificial intelligence (AI) in the medical field. This research uses descriptive qualitative methods and gathers data from related previous studies. The findings show that AI contributes to the medical field, particularly in medical imaging and diagnosis, enhancing accuracy and efficiency. AI systems, such as deep learning models and transformers, are being increasingly utilized to analyze medical images, detect abnormalities, and assist in early disease detection, such as cancer, eye diseases, and COVID-19. Transformer-based models have shown great potential in improving diagnostic accuracy by efficiently analyzing large and complex medical data. These models can capture long-range dependencies, making them ideal for handling diverse medical imaging modalities, such as X-rays, CT scans, and MRIs. Moreover, AI has proven to be highly effective in areas like retinal disease detection, lung cancer diagnosis, and infectious disease identification. Transformer models continue to demonstrate promise, with the potential to revolutionize medical diagnostics by offering faster, more precise, and personalized treatment options.