Arti ﬁ cial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success

By MD James H. Thrall, PhD Xiang Li, PhD Quanzheng Li, MD Cinthia Cruz, PhD • Synho Do, Keith J. Dreyer, P. Do, M. Brink

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Citations in the corpus

Deep learning for smart fish farming: applications, opportunities and challenges (2020, methodology): “The data and algorithms used are the two main elements of AI (Thrall et al., 2018).”
Deep Learning-based Image Segmentation on Multimodal Medical Imaging (2019, background): “Successes in applying deep convolutional neural networks (CNNs) for natural image [9] and medical image [10], [11] processing have been recently reported.”
The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. (2019, unknown): “7,87 Split sample validation and k-fold cross-validation (CV) are two common techniques used to develop AI models.”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “This could be solved by recruiting scientists with backgrounds in AI, but also by establishing educational programmes in radiology professional services (Nguyen & Shetty, 2018; Thrall et al., 2018).”
Federated learning for predicting clinical outcomes in patients with COVID-19 (2021, methodology): “This can be mitigated by training with diverse data from multiple sites without centralization of data 33 using methods such as transfer learning 34,35 or FL. FL is a method used to train AI models on disparate data sources, without the data being transported or exposed outside their original…”
Real-world Evidence versus Randomized Controlled Trial: Clinical Research Based on Electronic Medical Records (2018, background): “27 Deep learning-based artificial intelligence ultimately needs to the utilization of an extensive range of accumulated data.”
Radiomics with artificial intelligence: a practical guide for beginners. (2019, background): “Artificial intelligence (AI) is broadly a set of systems that can accurately perform inferences from a large amount of data, based on advanced computational algorithms (16).”