Heterogeneity and predictors of the effects of AI assistance on radiologists.

The integration of artificial intelligence (AI) in medical image interpretation requires effective collaboration between clinicians and AI algorithms. Although previous studies demonstrated the potential of AI assistance in improving overall clinician performance, the individual impact on clinicians remains unclear. This large-scale study examined the heterogeneous effects of AI assistance on 140 radiologists across 15 chest X-ray diagnostic tasks and identified predictors of these effects. Surprisingly, conventional experience-based factors, such as years of experience, subspecialty and familiarity with AI tools, fail to reliably predict the impact of AI assistance. Additionally, lower-performing radiologists do not consistently benefit more from AI assistance, challenging prevailing assumptions. Instead, we found that the occurrence of AI errors strongly influences treatment outcomes, with inaccurate AI predictions adversely affecting radiologist performance on the aggregate of all pathologies and on half of the individual pathologies investigated. Our findings highlight the importance of personalized approaches to clinician-AI collaboration and the importance of accurate AI models. By understanding the factors that shape the effectiveness of AI assistance, this study provides valuable insights for targeted implementation of AI, enabling maximum benefits for individual clinicians in clinical practice.

Providing normative information increases intentions to accept a COVID-19 vaccine.

Despite the availability of multiple safe vaccines, vaccine hesitancy may present a challenge to successful control of the COVID-19 pandemic. As with many human behaviors, people's vaccine acceptance may be affected by their beliefs about whether others will accept a vaccine (i.e., descriptive norms). However, information about these descriptive norms may have different effects depending on the actual descriptive norm, people's baseline beliefs, and the relative importance of conformity, social learning, and free-riding. Here, using a pre-registered, randomized experiment (N = 484,239) embedded in an international survey (23 countries), we show that accurate information about descriptive norms can increase intentions to accept a vaccine for COVID-19. We find mixed evidence that information on descriptive norms impacts mask wearing intentions and no statistically significant evidence that it impacts intentions to physically distance. The effects on vaccination intentions are largely consistent across the 23 included countries, but are concentrated among people who were otherwise uncertain about accepting a vaccine. Providing normative information in vaccine communications partially corrects individuals' underestimation of how many other people will accept a vaccine. These results suggest that presenting people with information about the widespread and growing acceptance of COVID-19 vaccines helps to increase vaccination intentions.

Global survey on COVID-19 beliefs, behaviours and norms.

Policy and communication responses to COVID-19 can benefit from better understanding of people's baseline and resulting beliefs, behaviours and norms. From July 2020 to March 2021, we fielded a global survey on these topics in 67 countries yielding over 2 million responses. This paper provides an overview of the motivation behind the survey design, details the sampling and weighting designed to make the results representative of populations of interest and presents some insights learned from the survey. Several studies have already used the survey data to analyse risk perception, attitudes towards mask wearing and other preventive behaviours, as well as trust in information sources across communities worldwide. This resource can open new areas of enquiry in public health, communication and economic policy by leveraging large-scale, rich survey datasets on beliefs, behaviours and norms during a global pandemic.

Interdependence and the cost of uncoordinated responses to COVID-19.

Social distancing is the core policy response to coronavirus disease 2019 (COVID-19). But, as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. To investigate this concern, we combined daily, county-level data on shelter-in-place policies with movement data from over 27 million mobile devices, social network connections among over 220 million Facebook users, daily temperature and precipitation data from 62,000 weather stations, and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States. Our analysis shows that the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one-third of a state's social and geographic peer states adopt shelter-in-place policies, it creates a reduction in mobility equal to the state's own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the "loss from anarchy" in uncoordinated state policies is increasing in the number of noncooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders.