BackgroundArtificial intelligence has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: (...) What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective?MethodsEight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed.ResultsUpon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries.ConclusionsThe ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere. (shrink)

In contexts of exposure to atypical stress or adversity, individual and collective resilience refers to the process of sustaining wellbeing by leveraging biological, psychological, social and environmental protective and promotive factors and processes. This multisystemic understanding of resilience is generating significant interest but has been difficult to operationalize in psychological research where studies tend to address only one or two systems at a time, often with a primary focus on individual coping strategies. We show how multiple systems implicated in human (...) resilience can be researched in the same study using a longitudinal, six-phase transformative sequential mixed methods study of 14- to 24-year-olds and their elders in two communities dependent on oil and gas industries. Data collection occurred over a 5-year period, and included: community engagement and the identification of youth health and well-being priorities; participatory youth-centric qualitative research using one-on-one semi-structured interviews and arts-based methods; survey of 500 youth at three time points to assess psychosocial health indicators and outcomes; collection of hair samples to assess stress biomarkers over time; youth-led ecological data collection and assessment of historical socio-economic development data; and community resource mapping with community elders. Analyzing data from these multiple systems will allow us to understand the interrelationship and impact of PPFPs within and across systems. To date, we have undertaken thematic and narrative qualitative analyses, and descriptive analyses of the preliminary ecological and survey data. As we proceed, we will combine these and grounded theory approaches with innovative techniques such as latent transition analysis and network analysis, as well as modeling of economic conditions and spatial analysis of human geographies to understand patterns of PPFPs and their inter-relationships. By analyzing the complexity of data collected across systems we are demonstrating the possibility of conducting multisystemic resilience research which expands the way psychological research accounts for positive development under stress in different contexts. This comprehensive examination of resilience may offer an example of how the study of resilience can inform socially and contextually relevant interventions and policies. (shrink)