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General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

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High-quality designs of distributed systems and services are essential for our digital economy and society. Threatening to slow down the stream of working designs, we identify the mounting pressure of scale and complexity of (eco-)system, of ill-defined and wicked problems, and of unclear processes, methods, and tools. We envision design itself as a core research topic in distributed systems, to understand and improve the science and practice of distributed (eco-)system design. Toward this vision, we propose the ATLARGE design framework, accompanied by a set of 8 core design principles. We also propose 10 key challenges, which we hope the community can address in the following 5 years. In our experience so far, the proposed framework and principles are practical, and lead to pragmatic and innovative designs for large-scale distributed systems. arXiv:1902.05416v1 [cs.DC] 14 Feb 2019 2. Why Focus on MCS Design?We argue in this section for the timely and important need to focus on MCS design. Not only is (good) design needed (Section 2.1), but we identify an increasing need for good design (Section 2.2) and designers (Section 2.3).We also analyze what good design needs to address, that is, complex challenges from system design (Section 2.4) and from MCS design (Section 2.5).3. We anonymize the venue, but consider it relevant because its held year is after 2014, the venue is a conference, and its ranking is A in CORE18 and green in MSAR14. For comparison, ICDCS has these rankings too.4. We anonymize the university, but consider the course relevant because it is large, it took place after 2014, and the university is ranked in the top-150 (in computer science) in both the THE and the QS 2018 World University Rankings (out of nearly 1,000 universities), and in Webometrics of July 18 (out of over 28,000).

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Background The emergence of Artificial Intelligence (AI) has been proven beneficial in several health care areas. Nevertheless, the uptake of AI in health care delivery remains poor. Despite the fact that the acceptance of AI-based technologies among medical professionals is a key barrier to their implementation, knowledge about what informs such attitudes is scarce. Objective The aim of this study was to identify and examine factors that influence the acceptability of AI-based technologies among medical professionals. Methods A survey was developed based on the Unified Theory of Acceptance and Use of Technology model, which was extended by adding the predictor variables perceived trust, anxiety and innovativeness, and the moderator profession. The web-based survey was completed by 67 medical professionals in the Netherlands. The data were analyzed by performing a multiple linear regression analysis followed by a moderating analysis using the Hayes PROCESS macro (SPSS; version 26.0, IBM Corp). Results Multiple linear regression showed that the model explained 75.4% of the variance in the acceptance of AI-powered care pathways (adjusted R2=0.754; F9,0=22.548; P<.001). The variables medical performance expectancy (β=.465; P<.001), effort expectancy (β=–.215; P=.005), perceived trust (β=.221; P=.007), nonmedical performance expectancy (β=.172; P=.08), facilitating conditions (β=–.160; P=.005), and professional identity (β=.156; P=.06) were identified as significant predictors of acceptance. Social influence of patients (β=.042; P=.63), anxiety (β=.021; P=.84), and innovativeness (β=.078; P=.30) were not identified as significant predictors. A moderating effect by gender was found between the relationship of facilitating conditions and acceptance (β=–.406; P=.09). Conclusions Medical performance expectancy was the most significant predictor of AI-powered care pathway acceptance among medical professionals. Nonmedical performance expectancy, effort expectancy, perceived trust, and professional identity were also found to significantly influence the acceptance of AI-powered care pathways. These factors should be addressed for successful implementation of AI-powered care pathways in health care delivery. The study was limited to medical professionals in the Netherlands, where uptake of AI technologies is still in an early stage. Follow-up multinational studies should further explore the predictors of acceptance of AI-powered care pathways over time, in different geographies, and with bigger samples.