Literature DB >> 31965813

Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century.

Issam El Naqa1, Masoom A Haider2, Maryellen L Giger3, Randall K Ten Haken1.   

Abstract

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.

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Year:  2020        PMID: 31965813      PMCID: PMC7055429          DOI: 10.1259/bjr.20190855

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  135 in total

1.  Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.

Authors:  Motonori Akagi; Yuko Nakamura; Toru Higaki; Keigo Narita; Yukiko Honda; Jian Zhou; Zhou Yu; Naruomi Akino; Kazuo Awai
Journal:  Eur Radiol       Date:  2019-04-11       Impact factor: 5.315

2.  What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images.

Authors:  Kenneth A Philbrick; Kotaro Yoshida; Dai Inoue; Zeynettin Akkus; Timothy L Kline; Alexander D Weston; Panagiotis Korfiatis; Naoki Takahashi; Bradley J Erickson
Journal:  AJR Am J Roentgenol       Date:  2018-11-07       Impact factor: 3.959

3.  Adversarial attacks on medical machine learning.

Authors:  Samuel G Finlayson; John D Bowers; Joichi Ito; Jonathan L Zittrain; Andrew L Beam; Isaac S Kohane
Journal:  Science       Date:  2019-03-22       Impact factor: 47.728

4.  Artificial intelligence in medicine. Where do we stand?

Authors:  W B Schwartz; R S Patil; P Szolovits
Journal:  N Engl J Med       Date:  1987-03-12       Impact factor: 91.245

Review 5.  Machine Learning in Medical Imaging.

Authors:  Maryellen L Giger
Journal:  J Am Coll Radiol       Date:  2018-02-02       Impact factor: 5.532

6.  Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI.

Authors:  Yading Yuan; Maryellen L Giger; Hui Li; Neha Bhooshan; Charlene A Sennett
Journal:  Acad Radiol       Date:  2010-09       Impact factor: 3.173

7.  Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters.

Authors:  Andrew J Hope; Patricia E Lindsay; Issam El Naqa; James R Alaly; Milos Vicic; Jeffrey D Bradley; Joseph O Deasy
Journal:  Int J Radiat Oncol Biol Phys       Date:  2006-05-01       Impact factor: 7.038

8.  Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.

Authors:  Wentian Guo; Hui Li; Yitan Zhu; Li Lan; Shengjie Yang; Karen Drukker; Elizabeth Morris; Elizabeth Burnside; Gary Whitman; Maryellen L Giger; Yuan Ji
Journal:  J Med Imaging (Bellingham)       Date:  2015-09-23

Review 9.  Artificial intelligence in cancer imaging: Clinical challenges and applications.

Authors:  Wenya Linda Bi; Ahmed Hosny; Matthew B Schabath; Maryellen L Giger; Nicolai J Birkbak; Alireza Mehrtash; Tavis Allison; Omar Arnaout; Christopher Abbosh; Ian F Dunn; Raymond H Mak; Rulla M Tamimi; Clare M Tempany; Charles Swanton; Udo Hoffmann; Lawrence H Schwartz; Robert J Gillies; Raymond Y Huang; Hugo J W L Aerts
Journal:  CA Cancer J Clin       Date:  2019-02-05       Impact factor: 508.702

10.  A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning.

Authors:  Dan Nguyen; Troy Long; Xun Jia; Weiguo Lu; Xuejun Gu; Zohaib Iqbal; Steve Jiang
Journal:  Sci Rep       Date:  2019-01-31       Impact factor: 4.379

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  14 in total

Review 1.  Artificial Intelligence in radiotherapy: state of the art and future directions.

Authors:  Giulio Francolini; Isacco Desideri; Giulia Stocchi; Viola Salvestrini; Lucia Pia Ciccone; Pietro Garlatti; Mauro Loi; Lorenzo Livi
Journal:  Med Oncol       Date:  2020-04-22       Impact factor: 3.064

Review 2.  Clinical Artificial Intelligence Applications: Breast Imaging.

Authors:  Qiyuan Hu; Maryellen L Giger
Journal:  Radiol Clin North Am       Date:  2021-11       Impact factor: 1.947

Review 3.  Artificial Intelligence for Response Evaluation With PET/CT.

Authors:  Lise Wei; Issam El Naqa
Journal:  Semin Nucl Med       Date:  2020-11-11       Impact factor: 4.446

4.  125 years of BJR and radiological research: reflecting on the anniversary series in celebration of the world's oldest radiology journal.

Authors:  Simon A Jackson; Kevin M Prise
Journal:  Br J Radiol       Date:  2021-01-01       Impact factor: 3.039

Review 5.  Oncology Informatics: Status Quo and Outlook.

Authors:  Paul Martin Putora; Michael Baudis; Beth M Beadle; Issam El Naqa; Frank A Giordano; Nils H Nicolay
Journal:  Oncology       Date:  2020-05-14       Impact factor: 2.935

6.  Promises of artificial intelligence in neuroradiology: a systematic technographic review.

Authors:  Allard W Olthof; Peter M A van Ooijen; Mohammad H Rezazade Mehrizi
Journal:  Neuroradiology       Date:  2020-04-22       Impact factor: 2.804

Review 7.  Lessons learned in transitioning to AI in the medical imaging of COVID-19.

Authors:  Issam El Naqa; Hui Li; Jordan Fuhrman; Qiyuan Hu; Naveena Gorre; Weijie Chen; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2021-10-01

8.  Saudi Radiology Personnel's Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study.

Authors:  Abdulaziz A Qurashi; Rashed K Alanazi; Yasser M Alhazmi; Ahmed S Almohammadi; Walaa M Alsharif; Khalid M Alshamrani
Journal:  J Multidiscip Healthc       Date:  2021-11-23

Review 9.  Requirements and reliability of AI in the medical context.

Authors:  Yoganand Balagurunathan; Ross Mitchell; Issam El Naqa
Journal:  Phys Med       Date:  2021-03-13       Impact factor: 2.685

Review 10.  Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers.

Authors:  Andrew Hope; Maikel Verduin; Thomas J Dilling; Ananya Choudhury; Rianne Fijten; Leonard Wee; Hugo Jwl Aerts; Issam El Naqa; Ross Mitchell; Marc Vooijs; Andre Dekker; Dirk de Ruysscher; Alberto Traverso
Journal:  Cancers (Basel)       Date:  2021-05-14       Impact factor: 6.639

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