Abstract
BackgroundOur objective was to determine the impacts of artificial intelligence (AI) on public health practice.MethodsWe used a fundamental qualitative descriptive study design, enrolling 15 experts in public health and AI from June 2018 until July 2019 who worked in North America and Asia. We conducted in-depth semi-structured interviews, iteratively coded the resulting transcripts, and analyzed the results thematically.ResultsWe developed 137 codes, from which nine themes emerged. The themes included opportunities such as leveraging big data and improving interventions; barriers to adoption such as confusion regarding AI’s applicability, limited capacity, and poor data quality; and risks such as propagation of bias, exacerbation of inequity, hype, and poor regulation.ConclusionsExperts are cautiously optimistic about AI’s impacts on public health practice, particularly for improving disease surveillance. However, they perceived substantial barriers, such as a lack of available expertise, and risks, including inadequate regulation. Therefore, investment and research into AI for public health practice would likely be beneficial. However, increased access to high-quality data, research and education regarding the limitations of AI, and development of rigorous regulation are necessary to realize these benefits.
Highlights
Our objective was to determine the impacts of artificial intelligence (AI) on public health practice
We used a fundamental qualitative descriptive study design [18] to explore the impacts of AI on public health practice (see Additional file 1 for the consolidated criteria for reporting qualitative research (COREQ) checklist)
Our findings are consistent with an evolution of opportunities previously identified in public health informatics, which has progressed to integrating increasing amounts of data with less latency, permitting timelier action [30]
Summary
Our objective was to determine the impacts of artificial intelligence (AI) on public health practice. After more than 60 years of evolution as a field [1], artificial intelligence (AI) has become ubiquitous in the last decade These changes have prompted both excitement and trepidation regarding potential impacts in virtually all human endeavours, including public health. In the early 2000s, increasing computational power, the ability to record and access vast amounts of data, and several enabling theoretical developments encouraged a renewed focus on data-driven approaches to AI [1] Many of these approaches fall under the subfield of machine learning, which can be loosely defined as a “field of study that gives computers the ability to learn without being explicitly programmed.” [4] Machine learning forms the foundation for most modern applications of AI, including targeted online advertising, conversational AI-assistants, and movie recommendations. These approaches are data-hungry, often relying on big data, or information flows with abundant volume, velocity, and variety [5]
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