Revolutionizing Radiology: AI-driven Innovations in Medical Imaging
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Abstract
The incorporation of the above parameters, AI has brought some sort of innovation in radiology by enhancing the diagnosis to be competent, quick, and efficient. Applications of this paper include the following: A) machine learning, B) deep learning, and C) natural language processing in medical imagery X-ray, CT, MRI, and ultrasound. Some of these are the evaluation of the efficiency of the proposed AI solutions with special reference to CNNs and UNet algorithms, the issues of data protection, compliance, and model bias. Among the open issues that the authors pointed out some of them are as follows: There are very few extensive assessments of the methods. In the literature review, the development of AI in radiology shall also show its powers in disease detection, the enhancement of the images, and the organization’s functioning. The method of research applies the performance testing of the selected algorithms with public datasets of medical images, analysis of interview data, evaluation of the efficiency of algorithms, and possibilities of ethical and regulatory aspects of the AI. Analyzing the outcomes of the AI model, it has been noted that CNNs have an Accuracy rate of 96%. From the investigations carried out it was observed that the new set of features increased the model accuracy to 3% and reduced considerable time in the diagnosis process. In conclusion, it again highlights AI’s capacity to progress radiology but also the importance of well-set rules and collaborative relationships to address the questions of ethical and functional aspects.
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