About this Research Topic
The use of Artificial Intelligence (AI) in Primary Health Care (PHC) is transforming health care systems across the world and its potential applications in Low- and Middle-Income Countries (LMIC) has promoted a number of initiatives driven by governments and global institutions, e.g. IDRC (Canada), Bill and Melinda Gates Foundation, Rockefeller Foundation, USAID, and many others.
The sudden fascination with and the promises of AI are based on a growing body of demonstrated capabilities, such as (i) finding new patterns / risk factors / contributors to pathologies and adverse outcomes, (ii) digesting mountains of underused data and squeezing actionable meaning out of them, (iii) optimizing system performance, and (iv) shifting healthcare analytics towards a more “business intelligence” approach.
This Article Collection, sponsored and supported by Fondation Botnar, focuses on exploring how AI/ML technologies can be leveraged to reimagine and improve health systems in LMICs, with the aim of curating a collection of papers that speak to understanding AI/ML, discussing their applications, articulating frameworks for AI application in these contexts, as well as exploring ethical, legal, and social dimensions. The below structure provides an overview of the topic areas we are most interested in, yet the editors are open to contributions that go beyond these individual parts.
Part 1: Understanding AI
Reviews of what AI is and where we might expect AI to be used in the future in improving the quality of and access to primary health care services.
Part 2: AI applied
How is AI currently being used and what are the likely future uses for it in planning, managing and evaluating health care delivery in LMICs. This will include case studies and literature reviews of how AI is influencing the delivery of health care as well as well referenced views of what are the likely future uses and implication of AI. We welcome both papers that focus on LMICs and on other current uses of AI that have implications for LMICs. We also welcome descriptions of projects that use AI in a practical application in the field.
Part 3: Frameworks for AI/ML in LMIC Health Systems
How can people interested in AI understand both the applications and implications of its use? What are the theoretical frameworks that have been most helpful in explaining AI and enabling non-computer scientists to leverage its potential? Are there continuous quality improvement frameworks or quality monitoring frameworks that can guide the use of outputs to modify programs and ensure adherence to quality.
Part 4: Ethical / Legal / Social dimensions
Focus on the pragmatic, enabling ecosystem for AI/ML - with a view to identifying what can go wrong and how we maintain Hippocratic principles of "doing no harm" while unleashing the potential of AI/ML.
We place additional emphasis on perspectives that explore how AI, as well as users of AI technologies, are likely to affect health processes, health systems, and health results. As such, we are interested in papers that explore both AI capabilities as well as examples of uses, including but by no means limited to:
- what types of settings or uses will most benefit from AI; when is AI unlikely to provide breakthroughs;
- diagnostic protocols at both population, primary care and secondary/tertiary care;
- pattern recognition;
- predictive modelling, including epidemiology;
- optimization pattern recognition;
- Natural Language Processing and recognition;
- bias, whether intended or unintentional;
- misuse, privacy, inequity;
- data quality;
- lack of reproducibility of results;
- safety of patients;
- manpower needs for effective use of AI in LMICs.
Keywords: #primaryhealthcare, #LMICs, #AI, #machinelearning, #healthsystems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.