Clinical Artificial Intelligence Applications in Radiology

Lee, Sungwon; Summers, Ronald M.

doi:10.1016/j.rcl.2021.07.001

Cited by 20 publications

(10 citation statements)

References 178 publications

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“…Recently, there have been similar efforts to provide an all-in-one AI-approach [ 104 , 105 , 106 , 107 ]. One example is AI-Rad Companion (Siemens Healthineers, Erlangen), an official medical product workflow solution interpreting CT thorax images [ 108 ].…”

Section: Discussionmentioning

confidence: 99%

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine

et al. 2022

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Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.

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Section: Discussionmentioning

confidence: 99%

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine

et al. 2022

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“…Conversely, other areas, such as predicting postoperative complications in perioperative medicine, have not yet produced the desired results. Although many predictive models have been published, most are still in the research stage, and a valid and universally applicable intelligent tool for clinical practice has yet to be developed [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ].…”

Section: Clinical Practice and Research Perspectivesmentioning

confidence: 99%

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

et al. 2023

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Artificial intelligence (AI) is a powerful tool that can assist researchers and clinicians in various settings. However, like any technology, it must be used with caution and awareness as there are numerous potential pitfalls. To provide a creative analogy, we have likened research to the PAC-MAN classic arcade video game. Just as the protagonist of the game is constantly seeking data, researchers are constantly seeking information that must be acquired and managed within the constraints of the research rules. In our analogy, the obstacles that researchers face are represented by “ghosts”, which symbolize major ethical concerns, low-quality data, legal issues, and educational challenges. In short, clinical researchers need to meticulously collect and analyze data from various sources, often navigating through intricate and nuanced challenges to ensure that the data they obtain are both precise and pertinent to their research inquiry. Reflecting on this analogy can foster a deeper comprehension of the significance of employing AI and other powerful technologies with heightened awareness and attentiveness.

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“…Radiologic procedures need to be broken down into the following tasks for implementation of AI: segmentation (measuring an organ or lesion), detection (detecting the abnormal region), classification (providing a diagnosis), and prediction (predicting pathology or prognosis). 15 Currently, AI methods thrive in fields with large public data sets, such as computer tomography of liver tumors. The accuracy of segmentation algorithms usually depends on the organ; most algorithms developed thus far have focused on a specific organ or disease and have an accuracy of approximately 80%.…”

Section: Imagingmentioning

confidence: 99%

Clinical Applications of Artificial Intelligence in Gastroenterology: Excitement and Evidence

Ahuja

Kefalakes

2022

Gastroenterology

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Clinical Artificial Intelligence Applications in Radiology

Cited by 20 publications

References 178 publications

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

Clinical Applications of Artificial Intelligence in Gastroenterology: Excitement and Evidence

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