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Background: Machine learning models have shown high performance, particularly in the diagnosis and prognosis of oral cancer. However, in actual everyday clinical practice, the diagnosis and prognosis using these models remain limited. This is due to the fact that these models have raised several ethical and morally laden dilemmas. Purpose: This study aims to provide a systematic stateof-the-art review of the ethical and social implications of machine learning models in oral cancer management. Methods: We searched the OvidMedline, PubMed, Scopus, Web of Science and Institute of Electrical and Electronics Engineers databases for articles examining the ethical issues of machine learning or artificial intelligence in medicine, healthcare or care providers. The Preferred Reporting Items for Systematic Review and Meta-Analysis was used in the searching and screening processes. Findings: A total of 33 studies examined the ethical challenges of machine learning models or artificial intelligence in medicine, healthcare or diagnostic analytics. Some ethical concerns were data privacy and confidentiality, peer disagreement (contradictory diagnostic or prognostic opinion between the model and the clinician), patient's liberty to decide the type of treatment to follow may be violated, patients–clinicians' relationship may change and the need for ethical and legal frameworks. Conclusion: Government, ethicists, clinicians, legal experts, patients' representatives, data scientists and machine learning experts need to be involved in the development of internationally standardised and structured ethical review guidelines for the machine learning model to be beneficial in daily clinical practice. ; Copyright © 2020 for this paper by its authors. Use permitted under Creative CommonsLicense Attribution 4.0 International (CC BY 4.0). ; fi=vertaisarvioitu|en=peerReviewed|

This study identifies micro-level value co-creation mechanisms that support the design of digital services. As services are now becoming digital—or at least digitally enabled—how to design digital services that enable value co-creation between a service provider and customers has become an increasingly important question. Our qualitative research study provides one answer to this question. Based on 113 in-depth laddering interviews analyzed using interpretive structural modeling, our study shows that value co-creation mechanisms differ between business-to-business and customer-to-customer digital service types. We identify five mechanisms to support value co-creation in the design of digital services: (1) Social use, (2) Customer orientation and decision making, (3) Service experience, (4) Service use context, and (5) Customer values and goals. We claim that firms can readily utilize these mechanisms to improve their customers' service experiences.