Liječnički vjesnik, Vol. 146 No. 9-10, 2024.
Review article
https://doi.org/10.26800/LV-146-9-10-4
Implementation of Artificial Intelligence Systems in Radiology: Historical Development, Trends, and Future Perspectives
Neva Coce
*
Maja Prutki
Luka Filipović-Grčić
Tinamarel Mandić
Ivana Kuhtić
* Corresponding author.
Abstract
Unlike traditional computer programs that operate based on explicitly defined instructions given by a programmer, artificial intelligence (AI), based on machine learning and the use of neural networks, can independently improve its performance as it receives more data, exhibiting the ability to learn. Such systems in radiology are trained on vast datasets to recognize pathological changes in radiological images, including tumors, fractures, and other abnormalities. AI systems in medicine are dramatically changing as diseases are diagnosed and treated. Machine learning enables advanced analysis of medical images and more accurate identification of pathology. These systems, surpassing traditional methods, speed up the diagnostic process and enhance image quality, leading to quicker and more accurate diagnoses. The application of AI also allows for better patient monitoring and disease progression prediction, contributing to a personalized approach to treatment. However, challenges
like integration into healthcare systems, data privacy, processing, and ethical issues present obstacles to broader adoption. There is a need for rigorous validation of AI algorithms, as well as training medical staff in the use of these technologies. Despite these challenges, the future of AI in radiology looks promising. Further development of more precise and efficient AI algorithms and their integration with other medical innovations are expected. This will lead to better patient monitoring and the provision of personalized care. AI in radiology has the potential not only to improve the quality of healthcare but also to transform the way care is delivered, with a focus on careful regulation and continuous education to maximize benefits and minimize risks.
Keywords
ARTIFICIAL INTELLIGENCE – trends; RADIOLOGY; ALGORITHMS; MACHINE LEARNING; NEURAL NETWORKS, COMPUTER
Hrčak ID:
322408
URI
Publication date:
18.11.2024.
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