Literature DB >> 30794121

How Should AI Be Developed, Validated, and Implemented in Patient Care?

Michael Anderson1, Susan Leigh Anderson2.   

Abstract

Should an artificial intelligence (AI) program that appears to have a better success rate than human pathologists be used to replace or augment humans in detecting cancer cells? We argue that some concerns-the "black-box" problem (ie, the unknowability of how output is derived from input) and automation bias (overreliance on clinical decision support systems)-are not significant from a patient's perspective but that expertise in AI is required to properly evaluate test results.
© 2019 American Medical Association. All Rights Reserved.

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Year:  2019        PMID: 30794121     DOI: 10.1001/amajethics.2019.125

Source DB:  PubMed          Journal:  AMA J Ethics


  7 in total

1.  Do the Right Thing.

Authors:  Charles E Kahn
Journal:  Radiol Artif Intell       Date:  2019-03-27

2.  Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator.

Authors:  William K Diprose; Nicholas Buist; Ning Hua; Quentin Thurier; George Shand; Reece Robinson
Journal:  J Am Med Inform Assoc       Date:  2020-04-01       Impact factor: 4.497

3.  Patient Data-Sharing for AI: Ethical Challenges, Catholic Solutions.

Authors:  Jean Baric-Parker; Emily E Anderson
Journal:  Linacre Q       Date:  2020-05-15

Review 4.  A short guide for medical professionals in the era of artificial intelligence.

Authors:  Bertalan Meskó; Marton Görög
Journal:  NPJ Digit Med       Date:  2020-09-24

Review 5.  Special Section on Ethics in Health Informatics.

Authors:  Carolyn Petersen; Vignesh Subbian
Journal:  Yearb Med Inform       Date:  2020-08-21

Review 6.  Ethics of Artificial Intelligence in Medicine and Ophthalmology.

Authors:  Yasser Ibraheem Abdullah; Joel S Schuman; Ridwan Shabsigh; Arthur Caplan; Lama A Al-Aswad
Journal:  Asia Pac J Ophthalmol (Phila)       Date:  2021 May-Jun 01

7.  Predicting Depression in Patients With Knee Osteoarthritis Using Machine Learning: Model Development and Validation Study.

Authors:  Zuzanna Nowinka; M Abdulhadi Alagha; Khadija Mahmoud; Gareth G Jones
Journal:  JMIR Form Res       Date:  2022-09-13
  7 in total

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