Keywords
Diagnosis
Deep learning
Generative Artificial Intelligence
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Abstract
Objective: To review the application of Artificial Intelligence (AI) in cancer diagnosis through imaging tests, analyzing trends and recent advances at the intersection of AI and diagnostic medicine. Methods: A bibliographic search was conducted in electronic databases, selecting scientific articles, systematic reviews, and meta-analyses published between 2017 and 2023. The studies included AI algorithms, machine learning techniques, and neural networks applied to imaging tests for cancer detection, classification, and diagnosis. Results: The analysis focused on image recognition models for cancer diagnosis, prioritizing sensitivity and specificity metrics. Studies that compared the performance of radiologists with AI systems were highlighted, showing that in some cases AI outperformed the professionals and, in others, significantly improved the performance of radiologists when used as assistance. Final considerations: AI has proven to be a promising tool in cancer diagnosis through imaging. The combination of AI with clinical data may improve diagnostic metrics. Limitations include the quality and quantity of images for training, but new technologies such as Generative AI are emerging to overcome these challenges.
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