There exists in academic medicine a core ethical issue that is seldom pursued: trainees are frequently not the best person in the operating room at a given intervention being performed, and yet as a profession we understand a fundamental need to afford them opportunities to perform. Academic centres are traditionally associated with a higher quality of care than non-academic centres, suggesting that practical measures exist within teaching hospitals that effectively mask the clinical discrepancies between trainees and their preceptors. Nonetheless, we (...) are bound by our ethical commitments as physicians to balance the obligations of care with the duty to teach. In order to ethically validate the model of ‘surgeon as teacher’, we propose that there must be a reconciliation of the tensions between traditional professional values in medicine with the constraints inherent in a time-bound utilitarian medical system. Ultimately, we must consciously accept that ensuring the longitudinal availability of skilled surgeons in society aligns more closely with our core ethical obligations as outlined in the social contract that medical professionals maintain with the general public than does the ethical demand to provide unreservedly individual-focused patient care. It is the duty of individual practitioners, as a necessity of lineage to maintain and fulfil our greater duties to society, to foster deontological relationships where possible within this utilitarian system while accepting short-term imperfection in our practice. There are no data in this manuscript. (shrink)

Objective: To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application. Study Design and Setting: This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC. Results: We find that concerns regarding explainability are (...) not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself. We examine the role of evidence-based medicine in evaluating unexplainable treatments and technologies, and highlight the analogy between the concept of explainability in MLHC and the related concept of mechanistic reasoning in evidence-based medicine. Conclusion: Ultimately, we conclude that the value of explainability in MLHC is not intrinsic, but is instead instrumental to achieving greater imperatives such as performance and trust. We caution against the uncompromising pursuit of explainability, and advocate instead for the development of robust empirical methods to successfully evaluate increasingly inexplicable algorithmic systems. (shrink)