Ethical Framework for Responsible Foundational Models in Medical Imaging
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913393935384576 |
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| author | Das, Abhijit Jha, Debesh Sanjotra, Jasmer Susladkar, Onkar Sarkar, Suramyaa Rauniyar, Ashish Tomar, Nikhil Sharma, Vanshali Bagci, Ulas |
| author_facet | Das, Abhijit Jha, Debesh Sanjotra, Jasmer Susladkar, Onkar Sarkar, Suramyaa Rauniyar, Ashish Tomar, Nikhil Sharma, Vanshali Bagci, Ulas |
| contents | Foundational models (FMs) have tremendous potential to revolutionize medical imaging. However, their deployment in real-world clinical settings demands extensive ethical considerations. This paper aims to highlight the ethical concerns related to FMs and propose a framework to guide their responsible development and implementation within medicine. We meticulously examine ethical issues such as privacy of patient data, bias mitigation, algorithmic transparency, explainability and accountability. The proposed framework is designed to prioritize patient welfare, mitigate potential risks, and foster trust in AI-assisted healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_11868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ethical Framework for Responsible Foundational Models in Medical Imaging Das, Abhijit Jha, Debesh Sanjotra, Jasmer Susladkar, Onkar Sarkar, Suramyaa Rauniyar, Ashish Tomar, Nikhil Sharma, Vanshali Bagci, Ulas Computers and Society Artificial Intelligence Foundational models (FMs) have tremendous potential to revolutionize medical imaging. However, their deployment in real-world clinical settings demands extensive ethical considerations. This paper aims to highlight the ethical concerns related to FMs and propose a framework to guide their responsible development and implementation within medicine. We meticulously examine ethical issues such as privacy of patient data, bias mitigation, algorithmic transparency, explainability and accountability. The proposed framework is designed to prioritize patient welfare, mitigate potential risks, and foster trust in AI-assisted healthcare. |
| title | Ethical Framework for Responsible Foundational Models in Medical Imaging |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2406.11868 |