Advances in imaging: Exploring the potential of artificial intelligence in radiology
Keywords:
Artificial Intelligence, Radiology, 2D X-rays, clinical workplace, patient careAbstract
Radiology has seen significant advances in its quest to move from descriptive to diagnostic, and more recently to predictive. The long-standing reliance on traditional 2D X-rays has evolved into the application of high-powered magnets and CT scanners, which provide improved tissue contrast and 3D capabilities. These advances have led to more accurate identification of diagnostic imaging markers and increased the ability to identify biomarkers for diagnostic and predictive imaging. While these advances still fall within the realm of human interpretation, there is continued momentum to accelerate efforts that are reshaping the clinical workplace. Whether it is the need to harness the vast and largely untapped potential of artificial intelligence, the need to increase efficiency and reduce reading times, or the call to creatively improve patient care by integrating imaging and biomarker-related technologies, the community is pushing the discipline to evolve further. To help bring these necessary tools to clinical applications, collaborative efforts between clinicians, data scientists, and engineers will need to come together. The scientific community must work together to enable the clinical translation of technologies that combine diverse physical and biological imaging findings.
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Ahmad, Z., Rahim, S., Zubair, M., & Abdul-Ghafar, J. (2021). Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact …. Diagnostic pathology. springer.com DOI: https://doi.org/10.1186/s13000-021-01085-4
Ahn, J. C., Connell, A., Simonetto, D. A., Hughes, C., & Shah, V. H. (2021). Application of artificial intelligence for the diagnosis and treatment of liver diseases. Hepatology, 73(6), 2546-2563. [HTML] DOI: https://doi.org/10.1002/hep.31603
Chamorro, E. M., Tascón, A. D., Sanz, L. I., Vélez, S. O., & Nacenta, S. B. (2021). Radiologic diagnosis of patients with COVID-19. Radiología (English Edition), 63(1), 56-73. nih.gov DOI: https://doi.org/10.1016/j.rxeng.2020.11.001
Elahi, A., Dako, F., Zember, J., Ojetayo, B., Gerus, D. A., Schweitzer, A., ... & Awan, O. (2020). Overcoming challenges for successful PACS installation in low-resource regions: our experience in Nigeria. Journal of Digital Imaging, 33, 996-1001. nih.gov DOI: https://doi.org/10.1007/s10278-020-00352-y
Hussain, S., Mubeen, I., Ullah, N., Shah, S. S. U. D., Khan, B. A., Zahoor, M., ... & Sultan, M. A. (2022). Modern diagnostic imaging technique applications and risk factors in the medical field: a review. BioMed research international, 2022(1), 5164970. wiley.com DOI: https://doi.org/10.1155/2022/5164970
Iqbal, M. J., Javed, Z., Sadia, H., Qureshi, I. A., Irshad, A., Ahmed, R., ... & Sharifi-Rad, J. (2021). Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer cell international, 21(1), 270. springer.com DOI: https://doi.org/10.1186/s12935-021-01981-1
Konstantinidis, K. & Apostolakis, I. (2020). The Investigation of RIS/PACS Information Systems' Incorporation in Greek Public Hospitals: Results from a National Web-based Survey. Radiography Open. oslomet.no DOI: https://doi.org/10.7577/radopen.4007
Shung, D. L., & Sung, J. J. (2021). Challenges of developing artificial intelligence‐assisted tools for clinical medicine. Journal of Gastroenterology and Hepatology, 36(2), 295-298. [HTML] DOI: https://doi.org/10.1111/jgh.15378
Thormann, M., Neumann, H., Behme, D., & Surov, A. (2023). Digital hands-on learning in radiology—design and evaluation of a PACS-based concept for medical students. Die Radiologie. [HTML] DOI: https://doi.org/10.1007/s00117-023-01185-w
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