Is Artificial Intelligence the New Friend for Radiologists? A Review Article

Abstract

Artificial intelligence (AI) is a path-breaking advancement for many industries, including the health care sector. The expeditious development of information technology and data processing has led to the formation of recent tools known as artificial intelligence. Radiology has been a portal for medical technological advancements, and AI will likely be no dissimilar. Radiology is the platform for many technological advances in the medical field; AI can undoubtedly impact every step of a radiologist's workflow. AI can simplify every activity like ordering and scheduling, protocoling and acquisition, image interpretation, reporting, communication, and billing. AI has eminent potential to augment efficiency and accuracy throughout radiology, but it also possesses inherent drawbacks and biases. We collected studies that were published in the past five years using PubMed as our database. We chose studies that were relevant to artificial intelligence in radiology. We mainly focused on the overview of AI in radiology, components included in the functioning of AI, AI assisting in the radiologists' workflow, ethical aspects of AI, challenges, and biases that AI experiencing together with some clinical applications of AI. Of all 33 studies, we found 15 articles discussed the overview and components of AI, five articles about AI affecting radiologist's workflow, five articles related to challenges and biases in AI, two articles discussed ethical aspects of AI, and six articles about practical implications of AI. We found out that the application of AI could make time-dependent tasks that can be performed effortlessly, permitting radiologists more time and opportunities to engage in patient care via increased time for consultation and development in imaging and extracting useful data from those images. AI could only be an aid to radiologists but will not replace a radiologist. Radiologists who use AI to their benefit, rather than to avoid it out of fear, might supersede those radiologists who do not. Substantial research should be done regarding the practical implications of AI algorithms for residents curriculum and the benefits of AI in radiology.

Bibliometrics research on radiomics of lung cancer

“…The recent traction of computer deep learning and artificial intelligence has seen the focus Radiomics refers to the use of computer technology to screen and analyze image features extracted from regions of interest such as CT, magnetic resonance imaging, and PET through quantitative methods, to quantitatively describe the biological characteristics and heterogeneity of tumors. This high-throughput quantitative analysis method avoids the bias caused by the subjectivity of imaging physicians (23,24). Radiomics usually includes five processes including standardized image acquisition and reconstruction, lesion image segmentation, feature extraction and selection, prediction model construction and verification, and model classification and prediction (25).…”

Teletıp Araştırmalarının Görsel Haritalama Tekniği ile Bibliyometrik Analizi

“…Yapay zeka kullanımı tıpta, özellikle radyolojide hızla ilerlemektedir [45]. Yapay zeka, radyolojik görüntülerin yorumlanması ve raporlanması süreçlerini basitleştirebilmektedir [46]. nonphysician graders and smart phone reporting Tablo 1'de teletıp alanında en fazla atıf alan yayınlar ve yazarları yer almaktadır.…”

Tracing the Inside of Pigs Non-Invasively: Recent Developments

“…Meanwhile, various groups try to automate [20,28] or have automated [29][30][31] the segmentation of CT images into the most important tissues for pork production, as are lean (muscle tissue), fat (adipose tissue), and bone (bone mineral and bone marrow) by excluding the gut and lung volumes in order to provide a virtual dissection and "in vivo diagnostics" resulting in a vast number of "old" and "new" phenotypes. Artificial intelligence-based techniques are further options to automate the segmentation process-not only for CT images [32,33]. New CT phenotypes are, for example, shoulder (scapula) or joint lesion scores related to locomotion and leg health of potential breeding pigs [34,35].…”