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Literature DB >> 29685530

The future of radiology augmented with Artificial Intelligence: A strategy for success.

Abstract

The rapid development of Artificial Intelligence/deep learning technology and its implementation into routine clinical imaging will cause a major transformation to the practice of radiology. Strategic positioning will ensure the successful transition of radiologists into their new roles as augmented clinicians. This paper describes an overall vision on how to achieve a smooth transition through the practice of augmented radiology where radiologists-in-the-loop ensure the safe implementation of Artificial Intelligence systems.

Keywords: Artificial Intelligence; Business; Deep learning; Health policy; Informatics; Strategy

Mesh：

Year: 2018 PMID： 29685530 DOI： 10.1016/j.ejrad.2018.03.019

Source DB: PubMed Journal: Eur J Radiol ISSN： 0720-048X Impact factor: 3.528

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Cited

40 in total

1. CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm.

Authors: Shayan Mostafaei; Hamid Abdollahi; Shiva Kazempour Dehkordi; Isaac Shiri; Abolfazl Razzaghdoust; Seyed Hamid Zoljalali Moghaddam; Afshin Saadipoor; Fereshteh Koosha; Susan Cheraghi; Seied Rabi Mahdavi
Journal: Radiol Med Date: 2019-09-24 Impact factor: 3.469

Review 2. Current applications and future directions of deep learning in musculoskeletal radiology.

Authors: Pauley Chea; Jacob C Mandell
Journal: Skeletal Radiol Date: 2019-08-04 Impact factor: 2.199

Review 3. Artificial intelligence for precision education in radiology.

Authors: Michael Tran Duong; Andreas M Rauschecker; Jeffrey D Rudie; Po-Hao Chen; Tessa S Cook; R Nick Bryan; Suyash Mohan
Journal: Br J Radiol Date: 2019-07-26 Impact factor: 3.039

4. Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [¹⁸F]-fluorodeoxyglucose positron emission tomography/computed tomography.

Authors: Wei-Chih Shen; Shang-Wen Chen; Kuo-Chen Wu; Te-Chun Hsieh; Ji-An Liang; Yao-Ching Hung; Lian-Shung Yeh; Wei-Chun Chang; Wu-Chou Lin; Kuo-Yang Yen; Chia-Hung Kao
Journal: Eur Radiol Date: 2019-05-27 Impact factor: 5.315

5. Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents.

Authors: Walter F Wiggins; M Travis Caton; Kirti Magudia; Sha-Har A Glomski; Elizabeth George; Michael H Rosenthal; Glenn C Gaviola; Katherine P Andriole
Journal: Radiol Artif Intell Date: 2020-11-04

The future of radiology augmented with Artificial Intelligence: A strategy for success.

1. CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm.

Review 2. Current applications and future directions of deep learning in musculoskeletal radiology.

Review 3. Artificial intelligence for precision education in radiology.

4. Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [¹⁸F]-fluorodeoxyglucose positron emission tomography/computed tomography.

5. Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents.

Review 6. Artificial intelligence in assessment of hepatocellular carcinoma treatment response.

Review 7. The Enterprise Imaging Value Proposition.

Review 8. Artificial Intelligence: A Primer for Breast Imaging Radiologists.

9. Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases.

10. Automatic Deep Learning-assisted Detection and Grading of Abnormalities in Knee MRI Studies.

The future of radiology augmented with Artificial Intelligence: A strategy for success.

1. CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm.

Review 2. Current applications and future directions of deep learning in musculoskeletal radiology.

Review 3. Artificial intelligence for precision education in radiology.

4. Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography.

5. Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents.

Review 6. Artificial intelligence in assessment of hepatocellular carcinoma treatment response.

Review 7. The Enterprise Imaging Value Proposition.

Review 8. Artificial Intelligence: A Primer for Breast Imaging Radiologists.

9. Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases.

10. Automatic Deep Learning-assisted Detection and Grading of Abnormalities in Knee MRI Studies.

4. Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [¹⁸F]-fluorodeoxyglucose positron emission tomography/computed tomography.