Literature DB >> 34907006

Responsibility, second opinions and peer-disagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts.

Hendrik Kempt1, Saskia K Nagel2.   

Abstract

In this paper, we first classify different types of second opinions and evaluate the ethical and epistemological implications of providing those in a clinical context. Second, we discuss the issue of how artificial intelligent (AI) could replace the human cognitive labour of providing such second opinion and find that several AI reach the levels of accuracy and efficiency needed to clarify their use an urgent ethical issue. Third, we outline the normative conditions of how AI may be used as second opinion in clinical processes, weighing the benefits of its efficiency against concerns of responsibility attribution. Fourth, we provide a 'rule of disagreement' that fulfils these conditions while retaining some of the benefits of expanding the use of AI-based decision support systems (AI-DSS) in clinical contexts. This is because the rule of disagreement proposes to use AI as much as possible, but retain the ability to use human second opinions to resolve disagreements between AI and physician-in-charge. Fifth, we discuss some counterarguments. © Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  applied and professional ethics; clinical ethics; social control of science/technology; technology/risk assessment

Mesh:

Year:  2021        PMID: 34907006     DOI: 10.1136/medethics-2021-107440

Source DB:  PubMed          Journal:  J Med Ethics        ISSN: 0306-6800            Impact factor:   2.903


  2 in total

1.  "Many roads lead to Rome and the Artificial Intelligence only shows me one road": an interview study on physician attitudes regarding the implementation of computerised clinical decision support systems.

Authors:  Daan Van Cauwenberge; Wim Van Biesen; Johan Decruyenaere; Tamara Leune; Sigrid Sterckx
Journal:  BMC Med Ethics       Date:  2022-05-06       Impact factor: 2.834

2.  Enabling Fairness in Healthcare Through Machine Learning.

Authors:  Thomas Grote; Geoff Keeling
Journal:  Ethics Inf Technol       Date:  2022-08-31
  2 in total

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