International Journal For Multidisciplinary Research

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Systematic Review: Comparing AI-Based Algorithms and Radiologists in Identifying Lung Nodules on CT Scans

Author(s) Nishita Gandhi, Dhruvil A Kheni, Shahpoor A. Shirzada, Harsh Bansal
Country India
Abstract Lung cancer is one of the most lethal cancers worldwide, largely due to its late diagnosis and progression before symptoms manifest. Lung nodules, detected primarily through CT imaging, are a key indicator of early lung cancer, and timely identification is critical for effective intervention. Recently, artificial intelligence (AI), particularly deep learning algorithms like convolutional neural networks (CNNs), has shown potential for enhancing the detection accuracy of these nodules. This systematic review compares the diagnostic accuracy, sensitivity, specificity, and time efficiency of AI algorithms and radiologists in lung nodule detection on CT scans.
We conducted an exhaustive search across PubMed, Cochrane Library, IEEE Xplore, and Embase, covering studies published between 2010 and 2023. Fifty studies meeting inclusion criteria were analyzed, focusing on performance metrics and the algorithms used. Results indicated that AI models achieved higher sensitivity, especially with nodules <6mm, and reduced detection times; however, specificity remained variable. This study underscores AI’s role in advancing early lung cancer detection but highlights the need for integration strategies, ethical frameworks, and further clinical trials.
Keywords Artificial intelligence, radiologists, lung nodules, CT scan, lung cancer, diagnostic accuracy, convolutional neural networks, deep learning.
Field Medical / Pharmacy
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-30
Cite This Systematic Review: Comparing AI-Based Algorithms and Radiologists in Identifying Lung Nodules on CT Scans - Nishita Gandhi, Dhruvil A Kheni, Shahpoor A. Shirzada, Harsh Bansal - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.30712
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.30712
Short DOI https://doi.org/g8sg7k
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