The COVID-19 pandemic has given rise to a broad range of research from fields alongside and beyond the core concerns of infectiology, epidemiology, and immunology. One significant subset of this work centers on machine learning-based approaches to supporting medical decision-making around COVID-19 diagnosis. To date, various challenges, including IT issues, have meant that, notwithstanding this strand of research on digital diagnosis of COVID-19, the actual use of these methods in medical facilities remains incipient at best, despite their potential to relieve pressure on scarce medical resources, prevent instances of infection, and help manage the difficulties and unpredictabilities surrounding the emergence of new mutations. The reasons behind this research-application gap are manifold and may imply an interdisciplinary dimension. We argue that the discipline of AI ethics can provide a framework for interdisciplinary discussion and create a roadmap for the application of digital COVID-19 diagnosis, taking into account all disciplinary stakeholders involved. This article proposes such an ethical framework for the practical use of digital COVID-19 diagnosis, considering legal, medical, operational managerial, and technological aspects of the issue in accordance with our diverse research backgrounds and noting the potential of the approach we set out here to guide future research.

This article presents a prediction model of the optimal dual variables for the cutting stock problem. For this purpose, we first analyze the influence of different attributes on the optimal dual variables within an instance for the cutting stock problem. We apply and compare our predictions in a stabilization technique for column generation. In most studies, the parameters for stabilized column generation are determined by numerical tests, that is, the same problem is solved several times with different settings. We develop two learning algorithms that predict the best algorithm configuration based on the predicted optimal dual variables and thus omit the numerical study. Our extensive computational study shows the tradeoff between the learning algorithms using full and sparse instance information. We show that both algorithms can efficiently predict the optimal dual variables and dominate the common update mechanism in a generic stabilized column generation approach. Although the learning algorithm with full instance information is applicable when one has to solve the problem mainly for a fixed set of items, the algorithm with sparse instance information is applicable when there is more variability in the number of items between the different instances. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2023.1277 .

Scheduling physicians is a key success factor in hospitals. Heterogeneous demand and 24/7 service make the problem challenging. Approaches in the literature use flexible shift patterns to match demand with scarce resources. In these approaches, demand is usually assumed to be deterministic. However, surgery durations and emergency arrivals are both uncertain, leading to massive staff overtime. We introduce stochastic demand for physicians using a scenario-based approach. To incorporate this in scheduling, we allow variable shift extensions. If a variable shift extension is scheduled, the physician knows that with a given probability he or she may have to work a few periods longer. Thus, we ensure a matching of supply with demand, and at the same time we increase predictability of working hours. We propose a mixed-integer model and a column generation heuristic to solve our problem and provide experimental data from a German university hospital. Our approach reduces unplanned overtime by more than 80%, given a constant workforce. In cases of similar levels of unplanned overtime, the required workforce level can be decreased by 20%. Our approach aims at improving physicians’ work–life balance and provides insights for hospitals’ contract design processes.