Artificial Intelligence for Participatory Health: Applications, Impact, and Future Implications

Overview

Journal Yearb Med Inform

Publisher Thieme

Specialty Medical Informatics

Date 2019 Apr 26

PMID 31022749

Citations 11

Authors

Kerstin Denecke

Elia Gabarron

Rebecca Grainger

Stathis Th Konstantinidis

Annie Lau

Octavio Rivera-Romero

Talya Miron-Shatz

Mark Merolli

Affiliations

Soon will be listed here.

Abstract

Objective: Artificial intelligence (AI) provides people and professionals working in the field of participatory health informatics an opportunity to derive robust insights from a variety of online sources. The objective of this paper is to identify current state of the art and application areas of AI in the context of participatory health.

Methods: A search was conducted across seven databases (PubMed, Embase, CINAHL, PsychInfo, ACM Digital Library, IEEExplore, and SCOPUS), covering articles published since 2013. Additionally, clinical trials involving AI in participatory health contexts registered at clinicaltrials.gov were collected and analyzed.

Results: Twenty-two articles and 12 trials were selected for review. The most common application of AI in participatory health was the secondary analysis of social media data: self-reported data including patient experiences with healthcare facilities, reports of adverse drug reactions, safety and efficacy concerns about over-the-counter medications, and other perspectives on medications. Other application areas included determining which online forum threads required moderator assistance, identifying users who were likely to drop out from a forum, extracting terms used in an online forum to learn its vocabulary, highlighting contextual information that is missing from online questions and answers, and paraphrasing technical medical terms for consumers.

Conclusions: While AI for supporting participatory health is still in its infancy, there are a number of important research priorities that should be considered for the advancement of the field. Further research evaluating the impact of AI in participatory health informatics on the psychosocial wellbeing of individuals would help in facilitating the wider acceptance of AI into the healthcare ecosystem.

Citing Articles

How Participatory Health Informatics Catalyzes One Digital Health.

Denecke K, Rivera Romero O, Merolli M, Miron-Shatz T, Gabarron E, Petersen C Yearb Med Inform. 2023; 32(1):48-54.

PMID: 38147849 PMC: 10751117. DOI: 10.1055/s-0043-1768727.

Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review.

He X, Zheng X, Ding H J Med Internet Res. 2023; 25:e50342.

PMID: 38109173 PMC: 10758939. DOI: 10.2196/50342.

USING MACHINE LEARNING METHODS TO ASSESS THE RISK OF ALCOHOL MISUSE IN OLDER ADULTS.

Wickersham M, Bartelo N, Kulm S, Liu Y, Zhang Y, Elemento O Res Sq. 2023; .

PMID: 37886491 PMC: 10602059. DOI: 10.21203/rs.3.rs-3154584/v1.

Defining and Scoping Participatory Health Informatics: An eDelphi Study.

Denecke K, Rivera Romero O, Petersen C, Benham-Hutchins M, Cabrer M, Davies S Methods Inf Med. 2023; 62(3-04):90-99.

PMID: 36787885 PMC: 10462430. DOI: 10.1055/a-2035-3008.

Evolution of Wearable Devices in Health Coaching: Challenges and Opportunities.

Tahri Sqalli M, Al-Thani D Front Digit Health. 2021; 2:545646.

PMID: 34713031 PMC: 8521831. DOI: 10.3389/fdgth.2020.545646.

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