USING ARTIFICIAL INTELLIGENCE TO ANALYZE CANCER IMAGES
Technology is playing an increasingly important role in medicine, especially in image analysis for cancer diagnosis. New computational tools make it possible to identify patterns in scans more quickly and accurately, helping in the early detection of the disease and in targeting more effective treatments. Traditional methods, such as manual analysis of biopsies, are still essential, but they can be time-consuming and subject to variations in interpretation. Automating this process helps to reduce errors and standardize results. Among the most promising applications are the segmentation of tumours, the prediction of disease progression and the personalization of treatments according to the individual characteristics of each patient. However, challenges such as the need for large databases to train these tools and the transparency of results still need to be overcome. In addition, the regulation of these new health technologies is essential to ensure their safety and accessibility. In the coming years, the trend is for these innovations to be integrated with other fronts of medicine, such as genetic analysis and telemedicine, making cancer diagnosis even more accurate and accessible. With the constant advance of these technologies, it is hoped that there will be a significant improvement in patients' quality of life and in the efficiency of cancer treatments.
USING ARTIFICIAL INTELLIGENCE TO ANALYZE CANCER IMAGES
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DOI: https://doi.org/10.22533/at.ed.1595192526045
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Palavras-chave: Artificial Intelligence, Oncological images; Image analysis; Diagnosis.
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Keywords: Artificial Intelligence, Oncological images; Image analysis; Diagnosis.
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Abstract:
Technology is playing an increasingly important role in medicine, especially in image analysis for cancer diagnosis. New computational tools make it possible to identify patterns in scans more quickly and accurately, helping in the early detection of the disease and in targeting more effective treatments. Traditional methods, such as manual analysis of biopsies, are still essential, but they can be time-consuming and subject to variations in interpretation. Automating this process helps to reduce errors and standardize results. Among the most promising applications are the segmentation of tumours, the prediction of disease progression and the personalization of treatments according to the individual characteristics of each patient. However, challenges such as the need for large databases to train these tools and the transparency of results still need to be overcome. In addition, the regulation of these new health technologies is essential to ensure their safety and accessibility. In the coming years, the trend is for these innovations to be integrated with other fronts of medicine, such as genetic analysis and telemedicine, making cancer diagnosis even more accurate and accessible. With the constant advance of these technologies, it is hoped that there will be a significant improvement in patients' quality of life and in the efficiency of cancer treatments.
- Alexandre Vital dos Santos Souza
- LUIZA OLIVEIRA DE MACEDO