Literature DB >> 32510520

Is It Time to Get Rid of Black Boxes and Cultivate Trust in AI?

Aimilia Gastounioti1, Despina Kontos1.   

Abstract

Year:  2020        PMID： 32510520      PMCID： PMC7259191          DOI： 10.1148/ryai.2020200088

Source DB:  PubMed          Journal:  Radiol Artif Intell        ISSN： 2638-6100


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  5 in total

1.  Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

Authors:  Alejandro Rodriguez-Ruiz; Kristina Lång; Albert Gubern-Merida; Mireille Broeders; Gisella Gennaro; Paola Clauser; Thomas H Helbich; Margarita Chevalier; Tao Tan; Thomas Mertelmeier; Matthew G Wallis; Ingvar Andersson; Sophia Zackrisson; Ritse M Mann; Ioannis Sechopoulos
Journal:  J Natl Cancer Inst       Date:  2019-09-01       Impact factor: 13.506

Review 2.  On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities.

Authors:  Mauricio Reyes; Raphael Meier; Sérgio Pereira; Carlos A Silva; Fried-Michael Dahlweid; Hendrik von Tengg-Kobligk; Ronald M Summers; Roland Wiest
Journal:  Radiol Artif Intell       Date:  2020-05-27

3.  Human-level control through deep reinforcement learning.

Authors:  Volodymyr Mnih; Koray Kavukcuoglu; David Silver; Andrei A Rusu; Joel Veness; Marc G Bellemare; Alex Graves; Martin Riedmiller; Andreas K Fidjeland; Georg Ostrovski; Stig Petersen; Charles Beattie; Amir Sadik; Ioannis Antonoglou; Helen King; Dharshan Kumaran; Daan Wierstra; Shane Legg; Demis Hassabis
Journal:  Nature       Date:  2015-02-26       Impact factor: 49.962

Review 4.  Artificial intelligence in radiology.

Authors:  Ahmed Hosny; Chintan Parmar; John Quackenbush; Lawrence H Schwartz; Hugo J W L Aerts
Journal:  Nat Rev Cancer       Date:  2018-08       Impact factor: 60.716

Review 5.  Designing and interpreting 'multi-omic' experiments that may change our understanding of biology.

Authors:  Robert Haas; Aleksej Zelezniak; Jacopo Iacovacci; Stephan Kamrad; StJohn Townsend; Markus Ralser
Journal:  Curr Opin Syst Biol       Date:  2017-12
  5 in total
  6 in total

1.  An explainable machine learning model for predicting in-hospital amputation rate of patients with diabetic foot ulcer.

Authors:  Puguang Xie; Yuyao Li; Bo Deng; Chenzhen Du; Shunli Rui; Wu Deng; Min Wang; Johnson Boey; David G Armstrong; Yu Ma; Wuquan Deng
Journal:  Int Wound J       Date:  2021-09-14       Impact factor: 3.099

2.  Probing an AI regression model for hand bone age determination using gradient-based saliency mapping.

Authors:  Zhiyue J Wang
Journal:  Sci Rep       Date:  2021-05-19       Impact factor: 4.379

3.  Automated grading of enlarged perivascular spaces in clinical imaging data of an acute stroke cohort using an interpretable, 3D deep learning framework.

Authors:  Brady J Williamson; Vivek Khandwala; David Wang; Thomas Maloney; Heidi Sucharew; Paul Horn; Mary Haverbusch; Kathleen Alwell; Shantala Gangatirkar; Abdelkader Mahammedi; Lily L Wang; Thomas Tomsick; Mary Gaskill-Shipley; Rebecca Cornelius; Pooja Khatri; Brett Kissela; Achala Vagal
Journal:  Sci Rep       Date:  2022-01-17       Impact factor: 4.996

Review 4.  Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.

Authors:  Aimilia Gastounioti; Shyam Desai; Vinayak S Ahluwalia; Emily F Conant; Despina Kontos
Journal:  Breast Cancer Res       Date:  2022-02-20       Impact factor: 8.408

5.  Artificial intelligence in breast cancer screening: primary care provider preferences.

Authors:  Nathaniel Hendrix; Brett Hauber; Christoph I Lee; Aasthaa Bansal; David L Veenstra
Journal:  J Am Med Inform Assoc       Date:  2021-06-12       Impact factor: 4.497

Review 6.  Interpretation and visualization techniques for deep learning models in medical imaging.

Authors:  Daniel T Huff; Amy J Weisman; Robert Jeraj
Journal:  Phys Med Biol       Date:  2021-02-02       Impact factor: 3.609
  6 in total

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