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

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

Data Modeling of Cognitive Structure in Physiotherapy Students Learning Gross Anatomy

Cognitive structures that promote deep learning of gross anatomy are integral to musculoskeletal physiotherapy practice yet poorly understood. This quantitative, criterion-related validation study addressed two data modeling strategies (multidimensional scaling and Pathfinder networks) as a potential visual and quantitative representation of the cognitive structures of physiotherapy students learning gross anatomy. The study was grounded in the Adaptive Control of Thought-Rational theory of cognition. The research questions addressed the agreement (reliability, accuracy, and association) between student and expert cognitive structures and included the derived quantitative parameters as predictor variables in multiple regression to examine potential relationships with unit grades. An online survey of paired comparisons of 20 anatomical concepts relevant to musculoskeletal clinical practice generated the raw data used in the data modeling strategies for cognitive structure mapping. Convenience sampling was used to recruit 31 physiotherapy students, four course instructors, and three domain experts who completed the online survey. The results indicated moderate to high effect sizes regarding the agreement between student and expert. Six predictor variables accounted for 68.9% of the variance in unit grade indicating a large effect size. Preliminary evidence of concurrent and predictive validity was reported. Positive social change is reflected in this innovative use of data modeling strategies to represent cognitive structure and potentially enhance competency-based education critical to effective musculoskeletal physiotherapy practice