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The ethics of machine learning-based clinical decision support: An analysis through the lens of professionalisation theory

 
: Heyen, Nils B.; Salloch, Sabine

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Volltext urn:nbn:de:0011-n-6401103 (936 KByte PDF)
MD5 Fingerprint: 9eac0e07067e8254a0fca73621f3ed20
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Erstellt am: 2.9.2021


BMC medical ethics 22 (2021), Art. 112, 9 S.
ISSN: 1472-6939
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer ISI ()
algorithms; artificial intelligence; clinical decision support systems; ethics; machine learning; patient autonomy; physician; physician-patient relationship; profession; professionalisation

Abstract
Background: Machine learning-based clinical decision support systems (ML CDSS) are increasingly employed in various sectors of health care aiming at supporting clinicians’ practice by matching the characteristics of individual patients with a computerised clinical knowledge base. Some studies even indicate that ML CDSS may surpass physicians’ competencies regarding specific isolated tasks. From an ethical perspective, however, the usage of ML CDSS in medical practice touches on a range of fundamental normative issues. This article aims to add to the ethical discussion by using professionalisation theory as an analytical lens for investigating how medical action at the micro level and the physician-patient relationship might be affected by the employment of ML CDSS.
Main text: Professionalisation theory, as a distinct sociological framework, provides an elaborated account of what constitutes client-related professional action, such as medical action, at its core and why it is more than pure expertise-based action. Professionalisation theory is introduced by presenting five general structural features of professionalised medical practice: (i) the patient has a concern; (ii) the physician deals with the patient’s concern; (iii) s/he gives assistance without patronising; (iv) s/he regards the patient in a holistic manner without building up a private relationship; and (v) s/he applies her/his general expertise to the particularities of the individual case. Each of these five key aspects are then analysed regarding the usage of ML CDSS, thereby integrating the perspectives of professionalisation theory and medical ethics.
Conclusions: Using ML CDSS in medical practice requires the physician to pay special attention to those facts of the individual case that cannot be comprehensively considered by ML CDSS, for example, the patient’s personality, life situation or cultural background. Moreover, the more routinized the use of ML CDSS becomes in clinical practice, the more that physicians need to focus on the patient’s concern and strengthen patient autonomy, for instance, by adequately integrating digital decision support in shared decision-making.

: http://publica.fraunhofer.de/dokumente/N-640110.html