Abstract
Yapay zekanın radyoloji üzerindeki dönüştürücü etkisi, acil vakalardan kronik hastalıklara ve onkolojik durumlara kadar geniş bir yelpazeye yayılmaktadır. Belgelenen başarılar, bu alanda devam eden araştırmaların kritik rolünün altını çizmekte, teşhis doğruluğunu artırma, hayat kurtarma ve sağlık çalışanlarına daha iyi hizmetler sunma konusundaki büyük önemini vurgulamaktadır
Ethical Statement
The procedure followed institutional ethics and complied with the WMA Declaration of Helsinki for medical research involving human subjects.
Supporting Institution
No funding was obtained for this study.
Thanks
Not applicable.
References
- Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221-48.
- Driver CN, Bowles BS, Bartholmai BJ, Greenberg-Worisek AJ. Artificial intelligence in radiology: a call for thoughtful application. Clin Transl Sci. 2020;13:216.
- Chassagnon G, Vakalopoulou M, Paragios N, Revel MP. Artificial intelligence applications for thoracic imaging. Eur J Radiol. 2020; 123:108774.
- Lipkova J, Chen RJ, Chen B, Lu MY, Barbieri M, Shao D et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 2022;40:1095-110.
- Couture HD, Williams LA, Geradts J, Nyante SJ, Butler EN, Marron JS et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer. 2018;4:30.
- Cellina M, Cè M, Irmici G, Ascenti V, Caloro E, Bianchi L et al. Artificial intelligence in emergency radiology: where are we going? Diagnostics. 2022;12:3223.
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Abstract
The transformative impact of AI on radiology extends across a broad spectrum from emergency cases to chronic diseases and oncological conditions. The documented successes underscore the critical role of ongoing research in this field, emphasizing its paramount importance in enhancing diagnostic accuracy, saving lives, and providing improved services to healthcare professionals
References
- Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221-48.
- Driver CN, Bowles BS, Bartholmai BJ, Greenberg-Worisek AJ. Artificial intelligence in radiology: a call for thoughtful application. Clin Transl Sci. 2020;13:216.
- Chassagnon G, Vakalopoulou M, Paragios N, Revel MP. Artificial intelligence applications for thoracic imaging. Eur J Radiol. 2020; 123:108774.
- Lipkova J, Chen RJ, Chen B, Lu MY, Barbieri M, Shao D et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 2022;40:1095-110.
- Couture HD, Williams LA, Geradts J, Nyante SJ, Butler EN, Marron JS et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer. 2018;4:30.
- Cellina M, Cè M, Irmici G, Ascenti V, Caloro E, Bianchi L et al. Artificial intelligence in emergency radiology: where are we going? Diagnostics. 2022;12:3223.
- Stahl BC, Antoniou J, Ryan M, Macnish K, Jiya T. Organisational responses to the ethical issues of artificial intelligence. AI Soc. 2022;37:23-37.
- Coughlin S, Roberts D, O’Neill K, Brooks P. Looking to tomorrow’s healthcare today: a participatory health perspective. Intern Med J. 2018;48:92-6.
Details
| Primary Language | English |
|---|---|
| Subjects | Diagnostic Radiography |
| Journal Section | Letter to the Editor |
| Authors | |
| Publication Date | September 30, 2024 |
| Submission Date | October 16, 2023 |
| Acceptance Date | January 20, 2024 |
| Published in Issue | Year 2024 Volume: 49 Issue: 3 |
