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Ethical Issues01:27

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Ethical Standards I01:25

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Ethical Standards II01:23

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Identifying Ethical Considerations for Machine Learning Healthcare Applications.

Danton S Char1, Michael D Abràmoff2,3, Chris Feudtner4,5

  • 1Stanford University School of Medicine.

The American Journal of Bioethics : AJOB
|October 26, 2020
PubMed
Summary

This study presents a systematic pipeline model to identify and manage ethical concerns in machine learning healthcare applications (ML-HCAs). It aids diverse stakeholders in evaluating these technologies from development to implementation.

Keywords:
Artificial intelligenceeffectivenessethicsmachine learningsafetytest characteristics

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Area of Science:

  • Healthcare Ethics
  • Artificial Intelligence in Medicine
  • Technology Assessment

Background:

  • Machine learning healthcare applications (ML-HCAs) offer potential benefits but also present significant ethical challenges.
  • Evaluating these complex technologies requires a structured approach for diverse stakeholders.
  • Existing ethical frameworks may not fully address the unique issues posed by ML-HCAs.

Purpose of the Study:

  • To outline a systematic approach for identifying ethical concerns in ML-HCAs.
  • To develop a conceptual pipeline model for evaluating ML-HCAs throughout their lifecycle.
  • To facilitate interdisciplinary collaboration for managing the ethical implications of ML-HCAs.

Main Methods:

  • Development of a conceptual pipeline model detailing ML-HCA conception, development, implementation, and oversight.
  • Integration of key value-based questions and a literature-based review of ethical considerations.
  • Identification of novel ethical considerations requiring further attention.

Main Results:

  • A structured pipeline model framework for systematic ethical appraisals of ML-HCAs.
  • Identification of both known and emerging ethical concerns associated with ML-HCAs.
  • A foundation for interdisciplinary dialogue and management of ethical risks.

Conclusions:

  • The proposed pipeline model provides a robust framework for ethical evaluation of ML-HCAs.
  • Systematic ethical appraisal is crucial for responsible innovation and deployment of ML-HCAs.
  • Effective management of ML-HCA ethical implications necessitates collaboration among diverse stakeholders.