Literature DB >> 32574353

Latent bias and the implementation of artificial intelligence in medicine.

Matthew DeCamp1, Charlotta Lindvall2,3,4.   

Abstract

Increasing recognition of biases in artificial intelligence (AI) algorithms has motivated the quest to build fair models, free of biases. However, building fair models may be only half the challenge. A seemingly fair model could involve, directly or indirectly, what we call "latent biases." Just as latent errors are generally described as errors "waiting to happen" in complex systems, latent biases are biases waiting to happen. Here we describe 3 major challenges related to bias in AI algorithms and propose several ways of managing them. There is an urgent need to address latent biases before the widespread implementation of AI algorithms in clinical practice.
© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Keywords:  artificial intelligence; bias; clinical decision support; health informatics; machine learning

Mesh:

Year:  2020        PMID: 32574353      PMCID: PMC7727353          DOI: 10.1093/jamia/ocaa094

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  8 in total

1.  Ensuring Fairness in Machine Learning to Advance Health Equity.

Authors:  Alvin Rajkomar; Michaela Hardt; Michael D Howell; Greg Corrado; Marshall H Chin
Journal:  Ann Intern Med       Date:  2018-12-04       Impact factor: 25.391

2.  Artificial Intelligence in Health Care: A Report From the National Academy of Medicine.

Authors:  Michael E Matheny; Danielle Whicher; Sonoo Thadaney Israni
Journal:  JAMA       Date:  2019-12-17       Impact factor: 56.272

3.  Dissecting racial bias in an algorithm used to manage the health of populations.

Authors:  Ziad Obermeyer; Brian Powers; Christine Vogeli; Sendhil Mullainathan
Journal:  Science       Date:  2019-10-25       Impact factor: 47.728

4.  Implementing Machine Learning in Health Care - Addressing Ethical Challenges.

Authors:  Danton S Char; Nigam H Shah; David Magnus
Journal:  N Engl J Med       Date:  2018-03-15       Impact factor: 91.245

5.  Machine learning in medicine: Addressing ethical challenges.

Authors:  Effy Vayena; Alessandro Blasimme; I Glenn Cohen
Journal:  PLoS Med       Date:  2018-11-06       Impact factor: 11.069

6.  Computerization and the future of primary care: A survey of general practitioners in the UK.

Authors:  Charlotte Blease; Michael H Bernstein; Jens Gaab; Ted J Kaptchuk; Joe Kossowsky; Kenneth D Mandl; Roger B Davis; Catherine M DesRoches
Journal:  PLoS One       Date:  2018-12-12       Impact factor: 3.240

7.  Artificial intelligence, bias and clinical safety.

Authors:  Robert Challen; Joshua Denny; Martin Pitt; Luke Gompels; Tom Edwards; Krasimira Tsaneva-Atanasova
Journal:  BMJ Qual Saf       Date:  2019-01-12       Impact factor: 7.035

Review 8.  The medical AI insurgency: what physicians must know about data to practice with intelligent machines.

Authors:  D Douglas Miller
Journal:  NPJ Digit Med       Date:  2019-06-28
  8 in total
  15 in total

1.  Setting the agenda: an informatics-led policy framework for adaptive CDS.

Authors:  Jeffery Smith
Journal:  J Am Med Inform Assoc       Date:  2020-12-09       Impact factor: 4.497

2.  Use of Artificial Intelligence in Non-Oncologic Interventional Radiology: Current State and Future Directions.

Authors:  Rohil Malpani; Christopher W Petty; Neha Bhatt; Lawrence H Staib; Julius Chapiro
Journal:  Dig Dis Interv       Date:  2021-07-17

3.  Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know.

Authors:  David Chen; Clifton Fulmer; Ilyssa O Gordon; Sana Syed; Ryan W Stidham; Niels Vande Casteele; Yi Qin; Katherine Falloon; Benjamin L Cohen; Robert Wyllie; Florian Rieder
Journal:  J Crohns Colitis       Date:  2022-03-14       Impact factor: 10.020

Review 4.  Top Ten Tips Palliative Care Clinicians Should Know About Delivering Antiracist Care to Black Americans.

Authors:  Katie Fitzgerald Jones; Esther Laury; Justin J Sanders; Lauren T Starr; William E Rosa; Staja Q Booker; Melissa Wachterman; Christopher A Jones; Susan Hickman; Jessica S Merlin; Salimah H Meghani
Journal:  J Palliat Med       Date:  2021-11-16       Impact factor: 2.947

5.  Mapping value sensitive design onto AI for social good principles.

Authors:  Steven Umbrello; Ibo van de Poel
Journal:  AI Ethics       Date:  2021-02-01

6.  Trust and medical AI: the challenges we face and the expertise needed to overcome them.

Authors:  Thomas P Quinn; Manisha Senadeera; Stephan Jacobs; Simon Coghlan; Vuong Le
Journal:  J Am Med Inform Assoc       Date:  2021-03-18       Impact factor: 4.497

7.  Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper.

Authors:  Carolyn Petersen; Jeffery Smith; Robert R Freimuth; Kenneth W Goodman; Gretchen Purcell Jackson; Joseph Kannry; Hongfang Liu; Subha Madhavan; Dean F Sittig; Adam Wright
Journal:  J Am Med Inform Assoc       Date:  2021-03-18       Impact factor: 4.497

Review 8.  Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.

Authors:  Ellen E Lee; John Torous; Munmun De Choudhury; Colin A Depp; Sarah A Graham; Ho-Cheol Kim; Martin P Paulus; John H Krystal; Dilip V Jeste
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2021-02-08

9.  The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice.

Authors:  Brian R Jackson; Ye Ye; James M Crawford; Michael J Becich; Somak Roy; Jeffrey R Botkin; Monica E de Baca; Liron Pantanowitz
Journal:  Acad Pathol       Date:  2021-02-16

Review 10.  Trustworthy Augmented Intelligence in Health Care.

Authors:  Elliott Crigger; Karen Reinbold; Chelsea Hanson; Audiey Kao; Kathleen Blake; Mira Irons
Journal:  J Med Syst       Date:  2022-01-12       Impact factor: 4.460

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