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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.08747 |
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| _version_ | 1866909534741594112 |
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| author | del Campo, Virginia Malaina, Iker |
| author_facet | del Campo, Virginia Malaina, Iker |
| contents | Cognitive delegation to artificial intelligence (AI) systems is transforming scientific research by enabling the automation of analytical processes and the discovery of new patterns in large datasets. This study examines the ability of AI to complement and expand knowledge in the analysis of breast cancer using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Building on a previous study, we assess the extent to which AI can generate novel approaches and successfully solve them. For this purpose, AI models, specifically ChatGPT-4o, were used for data preprocessing, hypothesis generation, and the application of clustering techniques, predictive modeling, and correlation network analysis. The results obtained were compared with manually computed outcomes, revealing limitations in process transparency and the accuracy of certain calculations. However, as AI reduces errors and improves reasoning capabilities, an important question arises regarding the future of scientific research: could automation replace the human role in science? This study seeks to open the debate on the methodological and ethical implications of a science dominated by artificial intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08747 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IA generativa aplicada a la detección del cáncer a través de Resonancia Magnética del Campo, Virginia Malaina, Iker Quantitative Methods Cognitive delegation to artificial intelligence (AI) systems is transforming scientific research by enabling the automation of analytical processes and the discovery of new patterns in large datasets. This study examines the ability of AI to complement and expand knowledge in the analysis of breast cancer using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Building on a previous study, we assess the extent to which AI can generate novel approaches and successfully solve them. For this purpose, AI models, specifically ChatGPT-4o, were used for data preprocessing, hypothesis generation, and the application of clustering techniques, predictive modeling, and correlation network analysis. The results obtained were compared with manually computed outcomes, revealing limitations in process transparency and the accuracy of certain calculations. However, as AI reduces errors and improves reasoning capabilities, an important question arises regarding the future of scientific research: could automation replace the human role in science? This study seeks to open the debate on the methodological and ethical implications of a science dominated by artificial intelligence. |
| title | IA generativa aplicada a la detección del cáncer a través de Resonancia Magnética |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2503.08747 |