Background Social media recruitment for clinical studies holds the promise of being a cost-effective way of attracting traditionally marginalized populations and promoting patient engagement with researchers and a particular study. However, using social media for recruiting clinical study participants also poses a range of ethical issues. Objective This study aims to provide a comprehensive overview of the ethical benefits and risks to be considered for social media recruitment in clinical studies and develop practical recommendations on how to implement these considerations. Methods On the basis of established principles of clinical ethics and research ethics, we reviewed the conceptual and empirical literature for ethical benefits and challenges related to social media recruitment. From these, we derived a conceptual framework to evaluate the eligibility of social media use for recruitment for a specific clinical study. Results We identified three eligibility criteria for social media recruitment for clinical studies: information and consent, risks for target groups, and recruitment effectiveness. These criteria can be used to evaluate the implementation of a social media recruitment strategy at its planning stage. We have discussed the practical implications of these criteria for researchers. Conclusions The ethical challenges related to social media recruitment are context sensitive. Therefore, social media recruitment should be planned rigorously, taking into account the target group, the appropriateness of social media as a recruitment channel, and the resources available to execute the strategy.

No abstract

Social media advertising has revolutionised the advertising world by providing data-driven targeting methods. One area where social media advertising is just gaining a foothold is in the recruitment of clinical study participants. Here, as everywhere, social media advertising promises more yield per money spent because the technology can better reach highly specialised groups. In this article, we point out severe societal risks posed by advertising for clinical studies on social media. We show that social media advertising for clinical studies in many cases violates the privacy of individual users (R1), creates collective privacy risks by helping platform companies train predictive models of medical information that can be applied to all their users (R2), exploits the weaknesses of existing guidelines in (biomedical) research ethics (R3) and is detrimental to the quality of (biomedical) research (R4). We argue that the well-intentioned promises, which are often associated with the use of social media advertising for clinical studies, are untenable from a balanced point of view. Consequently, we call for updates of research ethics guidelines and better regulation of Big Data and inferential analytics. We conclude that social media advertising – especially with vulnerable patient populations – is not suitable as a recruitment tool for clinical studies as long as the processing of (even anonymised) social media usage data and the training of predictive models by data analytics and artificial intelligence companies is not sufficiently regulated.

Background The increasing use of social media opens new opportunities for recruiting patients for research studies. However, systematic evaluations indicate that the success of social media recruitment in terms of cost-effectiveness and representativeness depends on the type of study and its purpose. Objective This study aims to explore the practical benefits and challenges of recruiting study participants with social media in the context of clinical and nonclinical studies and provide a summary of expert advice on how to conduct social media–based recruitment. Methods We conducted semistructured interviews with 6 patients with hepatitis B who use social media and 30 experts from the following disciplines: (1) social media researchers or social scientists, (2) practical experts for social media recruitment, (3) legal experts, (4) ethics committee members, and (5) clinical researchers. The interview transcripts were analyzed using thematic analysis. Results We found diverging expert opinions regarding the challenges and benefits of social media recruitment for research studies in four domains: (1) resources needed, (2) representativeness, (3) web-based community building, and (4) privacy considerations. Moreover, the interviewed experts provided practical advice on how to promote a research study via social media. Conclusions Even though recruitment strategies should always be sensitive to individual study contexts, a multiplatform approach (recruiting via several different social media platforms) with mixed-methods recruitment (web-based and offline recruitment channels) is the most beneficial recruitment strategy for many research studies. The different recruitment methods complement each other and may contribute to improving the reach of the study, the recruitment accrual, and the representativeness of the sample. However, it is important to assess the context- and project-specific appropriateness and usefulness of social media recruitment before designing the recruitment strategy.

Deep learning models are being applied to more and more use cases with astonishing success stories, but how do they perform in the real world? To test a model, a specific cleaned data set is assembled. However, when deployed in the real world, the model will face unexpected, out-of-distribution (OOD) data. In this work, we show that the so-called "radiologist-level" CheXnet model fails to recognize all OOD images and classifies them as having lung disease. To address this issue, we propose in-distribution voting, a novel method to classify out-of-distribution images for multi-label classification. Using independent class-wise in-distribution (ID) predictors trained on ID and OOD data we achieve, on average, 99 % ID classification specificity and 98 % sensitivity, improving the end-to-end performance significantly compared to previous works on the chest X-ray 14 data set. Our method surpasses other output-based OOD detectors even when trained solely with ImageNet as OOD data and tested with X-ray OOD images.