AI in imaging and therapy: innovations, ethics, and impact - introductory editorial
Br J Radiol. 2023 Oct;96(1150):20239004. doi: 10.1259/bjr.20239004.NO ABSTRACTPMID:38011226 | PMC:PMC10546442 | DOI:10.1259/bjr.20239004 (Source: The British Journal of Radiology)
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Issam El Naqa Karen Drukker Source Type: research
Federated learning for medical imaging radiology
Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.ABSTRACTFederated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the rev...
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Muhammad Habib Ur Rehman Walter Hugo Lopez Pinaya Parashkev Nachev James T Teo Sebastin Ourselin M Jorge Cardoso Source Type: research
AI in imaging and therapy: innovations, ethics, and impact - introductory editorial
Br J Radiol. 2023 Oct;96(1150):20239004. doi: 10.1259/bjr.20239004.NO ABSTRACTPMID:38011226 | PMC:PMC10546442 | DOI:10.1259/bjr.20239004 (Source: The British Journal of Radiology)
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Issam El Naqa Karen Drukker Source Type: research
Federated learning for medical imaging radiology
Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.ABSTRACTFederated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the rev...
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Muhammad Habib Ur Rehman Walter Hugo Lopez Pinaya Parashkev Nachev James T Teo Sebastin Ourselin M Jorge Cardoso Source Type: research
AI in imaging and therapy: innovations, ethics, and impact - introductory editorial
Br J Radiol. 2023 Oct;96(1150):20239004. doi: 10.1259/bjr.20239004.NO ABSTRACTPMID:38011226 | PMC:PMC10546442 | DOI:10.1259/bjr.20239004 (Source: The British Journal of Radiology)
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Issam El Naqa Karen Drukker Source Type: research
Federated learning for medical imaging radiology
Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.ABSTRACTFederated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the rev...
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Muhammad Habib Ur Rehman Walter Hugo Lopez Pinaya Parashkev Nachev James T Teo Sebastin Ourselin M Jorge Cardoso Source Type: research
AI in imaging and therapy: innovations, ethics, and impact - introductory editorial
Br J Radiol. 2023 Oct;96(1150):20239004. doi: 10.1259/bjr.20239004.NO ABSTRACTPMID:38011226 | PMC:PMC10546442 | DOI:10.1259/bjr.20239004 (Source: The British Journal of Radiology)
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Issam El Naqa Karen Drukker Source Type: research
Federated learning for medical imaging radiology
Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.ABSTRACTFederated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the rev...
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Muhammad Habib Ur Rehman Walter Hugo Lopez Pinaya Parashkev Nachev James T Teo Sebastin Ourselin M Jorge Cardoso Source Type: research
AI in imaging and therapy: innovations, ethics, and impact - introductory editorial
Br J Radiol. 2023 Oct;96(1150):20239004. doi: 10.1259/bjr.20239004.NO ABSTRACTPMID:38011226 | PMC:PMC10546442 | DOI:10.1259/bjr.20239004 (Source: The British Journal of Radiology)
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Issam El Naqa Karen Drukker Source Type: research
Federated learning for medical imaging radiology
Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.ABSTRACTFederated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the rev...
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Muhammad Habib Ur Rehman Walter Hugo Lopez Pinaya Parashkev Nachev James T Teo Sebastin Ourselin M Jorge Cardoso Source Type: research
AI in imaging and therapy: innovations, ethics, and impact - introductory editorial
Br J Radiol. 2023 Oct;96(1150):20239004. doi: 10.1259/bjr.20239004.NO ABSTRACTPMID:38011226 | PMC:PMC10546442 | DOI:10.1259/bjr.20239004 (Source: The British Journal of Radiology)
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Issam El Naqa Karen Drukker Source Type: research
Federated learning for medical imaging radiology
Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.ABSTRACTFederated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the rev...
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Muhammad Habib Ur Rehman Walter Hugo Lopez Pinaya Parashkev Nachev James T Teo Sebastin Ourselin M Jorge Cardoso Source Type: research
AI in imaging and therapy: innovations, ethics, and impact - introductory editorial
Br J Radiol. 2023 Oct;96(1150):20239004. doi: 10.1259/bjr.20239004.NO ABSTRACTPMID:38011226 | PMC:PMC10546442 | DOI:10.1259/bjr.20239004 (Source: The British Journal of Radiology)
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Issam El Naqa Karen Drukker Source Type: research
Federated learning for medical imaging radiology
Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.ABSTRACTFederated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the rev...
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Muhammad Habib Ur Rehman Walter Hugo Lopez Pinaya Parashkev Nachev James T Teo Sebastin Ourselin M Jorge Cardoso Source Type: research
AI in imaging and therapy: innovations, ethics, and impact - introductory editorial
Br J Radiol. 2023 Oct;96(1150):20239004. doi: 10.1259/bjr.20239004.NO ABSTRACTPMID:38011226 | PMC:PMC10546442 | DOI:10.1259/bjr.20239004 (Source: The British Journal of Radiology)
Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Issam El Naqa Karen Drukker Source Type: research
