Literature DB >> 34809860

Trustworthy Artificial Intelligence in Medical Imaging.

Navid Hasani1, Michael A Morris2, Arman Rhamim3, Ronald M Summers4, Elizabeth Jones4, Eliot Siegel5, Babak Saboury6.   

Abstract

Trust in artificial intelligence (AI) by society and the development of trustworthy AI systems and ecosystems are critical for the progress and implementation of AI technology in medicine. With the growing use of AI in a variety of medical and imaging applications, it is more vital than ever to make these systems dependable and trustworthy. Fourteen core principles are considered in this article aiming to move the needle more closely to systems that are accurate, resilient, fair, explainable, safe, and transparent: toward trustworthy AI.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Ethics of AI; Machine learning; Trustworthiness; Trustworthy artificial intelligence

Mesh:

Year:  2022        PMID: 34809860      PMCID: PMC8785402          DOI: 10.1016/j.cpet.2021.09.007

Source DB:  PubMed          Journal:  PET Clin        ISSN: 1556-8598


  32 in total

Review 1.  Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

Authors:  Jacob L Jaremko; Marleine Azar; Rebecca Bromwich; Andrea Lum; Li Hsia Alicia Cheong; Martin Gibert; François Laviolette; Bruce Gray; Caroline Reinhold; Mark Cicero; Jaron Chong; James Shaw; Frank J Rybicki; Casey Hurrell; Emil Lee; An Tang
Journal:  Can Assoc Radiol J       Date:  2019-04-05       Impact factor: 2.248

2.  The potential for artificial intelligence in healthcare.

Authors:  Thomas Davenport; Ravi Kalakota
Journal:  Future Healthc J       Date:  2019-06

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.  Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

Authors:  J Raymond Geis; Adrian P Brady; Carol C Wu; Jack Spencer; Erik Ranschaert; Jacob L Jaremko; Steve G Langer; Andrea Borondy Kitts; Judy Birch; William F Shields; Robert van den Hoven van Genderen; Elmar Kotter; Judy Wawira Gichoya; Tessa S Cook; Matthew B Morgan; An Tang; Nabile M Safdar; Marc Kohli
Journal:  Radiology       Date:  2019-10-01       Impact factor: 11.105

Review 5.  Ethical and Legal Challenges of Artificial Intelligence in Nuclear Medicine.

Authors:  Geoffrey Currie; K Elizabeth Hawk
Journal:  Semin Nucl Med       Date:  2020-09-11       Impact factor: 4.446

6.  Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

Authors:  J Raymond Geis; Adrian P Brady; Carol C Wu; Jack Spencer; Erik Ranschaert; Jacob L Jaremko; Steve G Langer; Andrea Borondy Kitts; Judy Birch; William F Shields; Robert van den Hoven van Genderen; Elmar Kotter; Judy Wawira Gichoya; Tessa S Cook; Matthew B Morgan; An Tang; Nabile M Safdar; Marc Kohli
Journal:  Can Assoc Radiol J       Date:  2019-10-01       Impact factor: 2.248

7.  Comparison of 11 automated PET segmentation methods in lymphoma.

Authors:  Amy J Weisman; Minnie W Kieler; Scott Perlman; Martin Hutchings; Robert Jeraj; Lale Kostakoglu; Tyler J Bradshaw
Journal:  Phys Med Biol       Date:  2020-11-27       Impact factor: 3.609

Review 8.  Artificial Intelligence and Machine Learning Applied at the Point of Care.

Authors:  Zuzanna Angehrn; Liina Haldna; Anthe S Zandvliet; Eva Gil Berglund; Joost Zeeuw; Billy Amzal; S Y Amy Cheung; Thomas M Polasek; Marc Pfister; Thomas Kerbusch; Niedre M Heckman
Journal:  Front Pharmacol       Date:  2020-06-18       Impact factor: 5.810

9.  Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health.

Authors:  Richard Ribón Fletcher; Audace Nakeshimana; Olusubomi Olubeko
Journal:  Front Artif Intell       Date:  2021-04-15

Review 10.  The ethical adoption of artificial intelligence in radiology.

Authors:  Keshav Shree Mudgal; Neelanjan Das
Journal:  BJR Open       Date:  2020-01-01
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  2 in total

1.  Large-scale extraction of interpretable features provides new insights into kidney histopathology - A proof-of-concept study.

Authors:  Laxmi Gupta; Barbara Mara Klinkhammer; Claudia Seikrit; Nina Fan; Nassim Bouteldja; Philipp Gräbel; Michael Gadermayr; Peter Boor; Dorit Merhof
Journal:  J Pathol Inform       Date:  2022-05-25

Review 2.  Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.

Authors:  Virginia Liberini; Riccardo Laudicella; Michele Balma; Daniele G Nicolotti; Ambra Buschiazzo; Serena Grimaldi; Leda Lorenzon; Andrea Bianchi; Simona Peano; Tommaso Vincenzo Bartolotta; Mohsen Farsad; Sergio Baldari; Irene A Burger; Martin W Huellner; Alberto Papaleo; Désirée Deandreis
Journal:  Eur Radiol Exp       Date:  2022-06-15
  2 in total

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