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

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.

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study

“…However, the interaction between users, in this case the medical community, and the results provided by the algorithms are rarely discussed as a potential limitation of the use of such ML models. The difficulties of implementing a machine learning model in clinical practice are not only due to technical difficulties in developing the algorithms, but may be also due to ethical difficulties [ 27 ] and the diversity of users and developers involved [ 28 ], among other challenges.…”

Irradiation immunity interactions

“…To define the scope of these, initial research in preclinical models of cancer and RT schedules may be more practical, allowing more flexibility and control over prospective aims, and is made more relevant with new state‐of‐the‐art preclinical RT platforms now being implemented 174 . While technical advances allow several features to be extracted from biopsies for this type of research, 175,176 interpreting such multivariate data suits a machine learning approach to build models on data as it comes to hand to supplement clinical decisions 177,178 . Indeed, machine learning approaches are seen as being integral to future cancer treatment management 179 and so a greater understanding of these approaches will be helpful as the clinical environment evolves to include these approaches of immune biomarker‐based treatment personalisation (Fig.…”

“…174 While technical advances allow several features to be extracted from biopsies for this type of research, 175,176 interpreting such multivariate data suits a machine learning approach to build models on data as it comes to hand to supplement clinical decisions. 177,178 Indeed, machine learning approaches are seen as being integral to future cancer treatment management 179 and so a greater understanding of these approaches will be helpful as the clinical environment evolves to include these approaches of immune biomarker-based treatment personalisation (Fig. 1).…”

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation (Preprint)

“…While initially being rule-focused, these systems now increasingly incorporate machine learning. This enables them to extract patterns and new insights from datasets that may be challenging for humans to analyze, and to improve their performance (eg, recommendations) based on new data [3,15,[19][20][21]. Anticipated progress in AI-DSSs, therefore, suggests a growing role in proactively supporting nurses and other stakeholders in decision-making about person-centered care strategies by harnessing relevant data.…”