Introduction Artificial intelligence (AI) is a rapidly developing technology that is being used in various applications with the hope of providing better accuracy, improved efficiency, and enhanced performance. One such use is in medical imaging, where AI tools can be used to assist radiologists in diagnosis by analyzing medical images. However, AI tools are not infallible, and some cases of failure have been attributed to technical factors, such as the quality of the medical image acquisition and image reconstruction. A new study published in the American Journal of Roentgenology (AJR) examined the technical issues that can lead to AI tool failure, and found that many of these issues are preventable through proper acquisition and reconstruction protocols. What is AI? In the context of medical imaging, AI tools are computer programs which are designed to analyze medical images, such as CT or MRI scans, and provide a diagnostic report. These tools have the potential to reduce both the time it takes to analyze an image and the number of misdiagnoses due to human error. However, AI tools are still in the early stages of development, and are subject to a number of technical issues which can lead to failure. Results of the AJR Study According to the AJR study, the two most common reasons for AI tool failure were inadequate image quality and improper image reconstruction. Inadequate image quality can lead to AI tools misinterpreting medical images, resulting in incorrect diagnoses. Similarly, improper image reconstruction can cause AI algorithms to miss important details, resulting in false positives and false negatives. The study concluded that the majority of AI tool failures due to technical issues can be prevented through the use of proper acquisition and reconstruction protocols. Preventative Measures The AJR study recommended several preventative measures to reduce the risk of AI tool failure due to technical issues. These include: • Ensuring that medical images are acquired using high-quality scanners and imaging protocols. • Using appropriate optimization parameters during image reconstruction. • Utilizing image pre-processing techniques to improve image quality. Conclusion AI tools are a promising technology that has the potential to revolutionize medical imaging, but they are still in the early stages of development, and are subject to technical issues which can lead to failure. According to the AJR study, most of these technical issues can be prevented through the use of proper acquisition and reconstruction protocols. By following the preventative measures suggested in the study, radiologists can ensure that their AI tools are working optimally and providing accurate and reliable diagnoses.
https://www.lifetechnology.com/blogs/life-technology-medical-news/technical-adequacy-of-artificial-intelligence-body-composition-assessed-in-external-ct
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