BackgroundRecent studies demonstrate the potential of Artificial Intelligence to support diagnosis, mortality assessment, and clinical decisions in low-and-middle-income countries (LMICs). However, explicit evidence of strategies to overcome the particular challenges for transformed health systems in these countries does not exist.ObjectiveThe present study undertakes a review of research on the current status of artificial intelligence (AI) to identify requirements, gaps, challenges, and possible strategies to strengthen the large, complex, and heterogeneous health systems in LMICs.DesignAfter introducing the general challenges developing countries face, the methodology of systematic reviews and the meta-analyses extension for scoping reviews (PRISMA-ScR) is introduced according to the preferred reporting items. Scopus and Web of Science databases were used to identify papers published between 2011–2022, from which we selected 151 eligible publications. Moreover, a narrative review was conducted to analyze the evidence in the literature about explicit evidence of strategies to overcome particular AI challenges in LMICs.ResultsThe analysis of results was divided into two groups: primary studies, which include experimental studies or case studies using or deploying a specific AI solution (n = 129), and secondary studies, including opinion papers, systematic reviews, and papers with strategies or guidelines (n = 22). For both study groups, a descriptive statistical analysis was performed describing their technological contribution, data used, health context, and type of health interventions. For the secondary studies group, an in-deep narrative review was performed, identifying a set of 40 challenges gathered in eight different categories: data quality, context awareness; regulation and legal frameworks; education and change resistance; financial resources; methodology; infrastructure and connectivity; and scalability. A total of 89 recommendations (at least one per challenge) were identified.ConclusionResearch on applying AI and ML to healthcare interventions in LMICs is growing; however, apart from very well-described ML methods and algorithms, there are several challenges to be addressed to scale and mainstream experimental and pilot studies. The main challenges include improving the quality of existing data sources, training and modeling AI solutions based on contextual data; and implementing privacy, security, informed consent, ethical, liability, confidentiality, trust, equity, and accountability policies. Also, robust eHealth environments with trained stakeholders, methodological standards for data creation, research reporting, product certification, sustained investment in data sharing, infrastructures, and connectivity are necessary.Systematic review registration[https://rb.gy/frn2rz].

Background: Health care has evolved to support the involvement of individuals in decision making by, for example, using mobile apps and wearables that may help empower people to actively participate in their treatment and health monitoring. While the term "participatory health informatics" (PHI) has emerged in literature to describe these activities, along with the use of social media for health purposes, the scope of the research field of PHI is not yet well defined. Objective: To propose a preliminary definition of PHI and define the scope of the field. Methods: We used an adapted Delphi study design to gain consensus from participants on a definition developed from a previous review of literature. From the literature we derived a set of attributes describing PHI as comprising 18 characteristics, 14 aims, and 4 relations. We invited researchers, health professionals, and health informaticians to score these characteristics and aims of PHI and their relations to other fields over three survey rounds. In the first round participants were able to offer additional attributes for voting. Results: The first round had 44 participants, with 28 participants participating in all three rounds. These 28 participants were gender-balanced and comprised participants from industry, academia, and health sectors from all continents. Consensus was reached on 16 characteristics, 9 aims, and 6 related fields. Discussion: The consensus reached on attributes of PHI describe PHI as a multidisciplinary field that uses information technology and delivers tools with a focus on individual-centered care. It studies various effects of the use of such tools and technology. Its aims address the individuals in the role of patients, but also the health of a society as a whole. There are relationships to the fields of health informatics, digital health, medical informatics, and consumer health informatics. Conclusion: We have proposed a preliminary definition, aims, and relationships of PHI based on literature and expert consensus. These can begin to be used to support development of research priorities and outcomes measurements.

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The Global Digital Health Strategy emphasizes digital health workforce development to reach a sustainable health system. In Chile, a digital health capability framework to support the transition towards digital health and workforce development is still missing. A survey will be applied at a national level. The Development of a Chilean Nursing Digital Health Capability Framework will identify the capabilities of nurses in digital health innovation and improve the quality and safety of healthcare nationwide.

As an indigenous scientist, I have dedicated all my professional life to protecting people using informatics for public policy to the privacy of users, patients, clients, and citizens as a human right and obligation as part of the United Nations international development goals. I am reflecting on my earliest knowledge of the impact of data and information privacy on my journey as scientist. I was just a number out of many other numbers as a indigenous child. The aim of this paper is to share my own personal experience together with one of my students. Now working with data as a scientific task within the data modeling to measure poverty. As a datum with human value, I was a 1) Female child with young parents, 2) Low socioeconomic status & 3) Identified as an indigenous person within a minor language group. These three data descriptions described me as a person who needed protection of my human dignity and identity as a child, based on all the protocols of social services for providing help. In conclusion, as scientists, we need to remember when using client data in vulnerable contexts and protection of their privacy, due to the potential risk of active discrimination. Thanks to my extensive education in Australia, I became an outlying datum that deviated from the data modeling applied to me. Today, I work for Privacy digital standards to impact real life with respect to human dignity and obtain accurate scientific interpretations of human beings’ realities.

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