Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In BMC public health ; h5-index 82.0

BACKGROUND : Our objective was to determine the impacts of artificial intelligence (AI) on public health practice.

METHODS : We 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.

RESULTS : We 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.

CONCLUSIONS : Experts 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.

Morgenstern Jason D, Rosella Laura C, Daley Mark J, Goel Vivek, Sch√ľnemann Holger J, Piggott Thomas

2021-Jan-06

Big data, Community medicine, Epidemiology, Machine learning, Population health, Preventive medicine, Qualitative