Background: Artificial intelligence (AI) technologies are transforming medicine and healthcare. Scholars and practitioners have debated the philosophical, ethical, legal, and regulatory implications of medical AI, and empirical research on stakeholders’ knowledge, attitude, and practices has started to emerge. This study is a systematic review of published empirical studies of medical AI ethics with the goal of mapping the main approaches, findings, and limitations of scholarship to inform future practice considerations. Methods: We searched seven databases for published peer-reviewed empirical studies on medical AI ethics and evaluated them in terms of types of technologies studied, geographic locations, stakeholders involved, research methods used, ethical principles studied, and major findings. Findings: Thirty-six studies were included (published 2013-2022). They typically belonged to one of the three topics: exploratory studies of stakeholder knowledge and attitude toward medical AI, theory-building studies testing hypotheses regarding factors contributing to stakeholders’ acceptance of medical AI, and studies identifying and correcting bias in medical AI. Interpretation: There is a disconnect between high-level ethical principles and guidelines developed by ethicists and empirical research on the topic and a need to embed ethicists in tandem with AI developers, clinicians, patients, and scholars of innovation and technology adoption in studying medical AI ethics.

Social media health influencers play an increasingly important role in disseminating health-related information to the public. To explore how health influencers in China communicate with their followers, we conducted a content analysis of the top ten health influencers’ posts (n = 1000) on Sina Weibo guided by the Extended Parallel Processing Model (EPPM) and the transportation theory. These posts were coded in terms of demographic information, topics, message properties (informative, persuasive, and interactive), EPPM variables, and types of evidence (statistical and narrative) used. Results showed that these influencers had a clear emphasis on women’s health (OB/GYN diseases and risks related to pregnancy and childcare) and beauty and skincare (in terms of risks and benefits). Overall, they used low fear appeal and high efficacy messages. However, messages containing efficacy information were less likely to be liked. These influencers relied heavily on narrative evidence; however, there was no significant relationship between the use of either narrative or statistical evidence and the number of likes. Differences in the communication strategies in posts about different diseases did exist but were not prevalent.

Background: Social media platforms such as YouTube are hotbeds for the spread of misinformation about vaccines. Objective: The aim of this study was to explore how individuals are exposed to antivaccine misinformation on YouTube based on whether they start their viewing from a keyword-based search or from antivaccine seed videos. Methods: Four networks of videos based on YouTube recommendations were collected in November 2019. Two search networks were created from provaccine and antivaccine keywords to resemble goal-oriented browsing. Two seed networks were constructed from conspiracy and antivaccine expert seed videos to resemble direct navigation. Video contents and network structures were analyzed using the network exposure model. Results: Viewers are more likely to encounter antivaccine videos through direct navigation starting from an antivaccine video than through goal-oriented browsing. In the two seed networks, provaccine videos, antivaccine videos, and videos containing health misinformation were all found to be more likely to lead to more antivaccine videos. Conclusions: YouTube has boosted the search rankings of provaccine videos to combat the influence of antivaccine information. However, when viewers are directed to antivaccine videos on YouTube from another site, the recommendation algorithm is still likely to expose them to additional antivaccine information.

Emerging infectious disease (EID) outbreaks such as the ongoing COVID-19 pandemic create unknown risks, uncertainty, and anxiety around the world. Accurate and timely information can help the public understand the outbreak and manage their lives. Presented here is a study of how residents of Hubei Province, the epicenter of the COVID-19 outbreak in China, use media for information seeking, scanning, and sharing while under lockdown through in-depth interviews. We find that (1) individuals primarily acquire information through information scanning from official governmental sources, (2) information sharing is more frequent with family members through private channels than with one’s extended social networks and the general public through pubic channels mostly due to concerns with censorship, and (3) individuals’ information need and information use change substantially during different stages of the outbreak. These findings provide insights into how individuals in China use different media for information during an unprecedented public health crisis and make sense of the limited and often confusing and contradictory information that is available to them. Such findings can inform future health communication efforts during EID outbreaks.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet