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Abstract This study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep learning models. The aim is to improve detection processes and diagnose diseases effectively. The study emphasizes the importance of teamwork in harnessing AI’s full potential for image analysis. Collaboration between doctors and AI experts is crucial for developing AI tools that bridge the gap between concepts and practical applications. The study demonstrates the effectiveness of machine learning classifiers, such as forest algorithms and deep learning models, in image analysis. These techniques enhance accuracy and expedite image analysis, aiding in the development of accurate medications. The study evidenced that technologically assisted medical image analysis significantly improves efficiency and accuracy across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. The outcomes were supported by the reduced diagnosis time. The exploration also helps us to understand the ethical considerations related to the privacy and security of data, bias, and fairness in algorithms, as well as the role of medical consultation in ensuring responsible AI use in healthcare.
Chauhan et al. (Fri,) studied this question.