Volume 6 Number 1 2025
Advancing Cancer Imaging with Artificial Intelligence Clinical Application and Challenges
Md Nazmul Alam Bhuiyan1*, Rabi Sankar Mondal1, Lamia Akter2
Journal of Primeasia 6(1) 1-8 https://doi.org/10.25163/primeasia.6110322
Submitted: 25 May 2025 Revised: 11 August 2025 Published: 12 August 2025
Abstract
Artificial intelligence (AI) is transforming cancer imaging in remarkable ways. It is not just improving how accurately we can diagnose cancer; it is also speeding up processes in clinical settings and helping to tailor treatments specifically for each patient. With the help of advanced algorithms, such as machine learning and deep learning, AI can identify subtle patterns in medical images that the human eye may overlook. This capability enables earlier and more precise cancer detection using various imaging techniques, including CT scans, MRIs, PET scans, and ultrasounds. In exploring how AI is being used in clinical practice, we also need to consider the advantages it brings, such as increased diagnostic accuracy and efficiency, as well as potential cost savings. However, there are real challenges to its widespread use in healthcare. We also need to consider how to effectively communicate AI’s decisions to doctors and patients, and ensure that healthcare professionals receive the necessary training and support to utilize these tools effectively. There are exciting avenues to explore in AI development, such as federated learning and enhancing the transparency and understandability of AI systems. Collaborating across different fields will be crucial to the safe and effective implementation of AI in healthcare. The promise of AI can help bridge gaps in diagnostic access, particularly in regions lacking healthcare resources, making it a valuable ally in the fight against global healthcare inequalities. Ultimately, AI is not about replacing healthcare professionals; it is about partnering with them to enhance cancer diagnosis and treatment, ultimately leading to better patient outcomes.
Keywords: Artificial Intelligence, Cancer Imaging, Machine Learning, Radiomics, Precision Medicine.
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