David Stuckler’s research while affiliated with Bocconi University and other places

Background Artificial intelligence (AI) has significant potential in clinical practice. However, its “black box” nature can lead clinicians to question its value. The challenge is to create sufficient trust for clinicians to feel comfortable using AI, but not so much that they defer to it even when it produces results that conflict with their clinical judgment in ways that lead to incorrect decisions. Explainable AI (XAI) aims to address this by providing explanations of how AI algorithms reach their conclusions. However, it remains unclear whether such explanations foster an appropriate degree of trust to ensure the optimal use of AI in clinical practice. Objective This study aims to systematically review and synthesize empirical evidence on the impact of XAI on clinicians’ trust in AI-driven clinical decision-making. Methods A systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching PubMed and Web of Science databases. Studies were included if they empirically measured the impact of XAI on clinicians’ trust using cognition- or affect-based measures. Out of 778 articles screened, 10 met the inclusion criteria. We assessed the risk of bias using standard tools appropriate to the methodology of each paper. Results The risk of bias in all papers was moderate or moderate to high. All included studies operationalized trust primarily through cognitive-based definitions, with 2 also incorporating affect-based measures. Out of these, 5 studies reported that XAI increased clinicians’ trust compared with standard AI, particularly when the explanations were clear, concise, and relevant to clinical practice. In addition, 3 studies found no significant effect of XAI on trust, and the presence of explanations does not automatically improve trust. Notably, 2 studies highlighted that XAI could either enhance or diminish trust, depending on the complexity and coherence of the provided explanations. The majority of studies suggest that XAI has the potential to enhance clinicians’ trust in recommendations generated by AI. However, complex or contradictory explanations can undermine this trust. More critically, trust in AI is not inherently beneficial, as AI recommendations are not infallible. These findings underscore the nuanced role of explanation quality and suggest that trust can be modulated through the careful design of XAI systems. Conclusions Excessive trust in incorrect advice generated by AI can adversely impact clinical accuracy, just as can happen when correct advice is distrusted. Future research should focus on refining both cognitive and affect-based measures of trust and on developing strategies to achieve an appropriate balance in terms of trust, preventing both blind trust and undue skepticism. Optimizing trust in AI systems is essential for their effective integration into clinical practice.

Artificial intelligence (AI) applications are complex and rapidly evolving, and thus often poorly understood, but have potentially profound implications for public health. We offer a primer for public health professionals that explains some of the key concepts involved and examines how these applications might be used in the response to a future pandemic. They include early outbreak detection, predictive modelling, healthcare management, risk communication, and health surveillance. Artificial intelligence applications, especially predictive algorithms, have the ability to anticipate outbreaks by integrating diverse datasets such as social media, meteorological data, and mobile phone movement data. Artificial intelligence‐powered tools can also optimise healthcare delivery by managing the allocation of resources and reducing healthcare workers' exposure to risks. In resource distribution, they can anticipate demand and optimise logistics, while AI‐driven robots can minimise physical contact in healthcare settings. Artificial intelligence also shows promise in supporting public health decision‐making by simulating the social and economic impacts of different policy interventions. These simulations help policymakers evaluate complex scenarios such as lockdowns and resource allocation. Additionally, it can enhance public health messaging, with AI‐generated health communications shown to be more effective than human‐generated messages in some cases. However, there are risks, such as privacy concerns, biases in models, and the potential for ‘false confirmations’, where AI reinforces incorrect decisions. Despite these challenges, we argue that AI will become increasingly important in public health crises, but only if integrated thoughtfully into existing systems and processes.

Background The high readmission rate following chronic obstructive pulmonary disease (COPD) has created a significant global health challenge, with high healthcare costs and a growing burden of care. Digital interventions, including telemonitoring, Telehealth, web, or mobile-based, can address these challenges, but there has yet to be a systematic review of the impact of digital interventions on reducing hospital readmission for patients with COPD. The aim of this systematic review was to assess the effectiveness of digital interventions in lowering hospitalization and rehospitalization for patients with COPD. Methods We conducted a systematic literature search from PubMed and Scopus to identify randomized controlled trials (RCTs) published in English with outcomes related to hospital admission or readmission within 30 days of hospital discharge. Results The search and selection process followed PRISMA guidelines and resulted in a final sample of 12 RCTs. We found mixed results. Of the 12 included studies, only four studies [mobile-based (n=1), Telehealth (n=1), Telemonitor-ing (n=2)] found positive effect of a digital intervention on hospital readmission. Seven studies [mobile-based (n=1), Telehealth (n=1), Telemonitoring (n=3), Web-based (n=2)] did not demonstrate clear evidence of a significant reduction in hospitalization rate. Based on these findings, the primary factors contributing to the outcome variation were differences in intervention components used and their operator, COPD severity, patient age, and sample size. Conclusions Systematic review provides the first insight into the impact of digital intervention on hospital readmis-sion among patients with COPD. While some studies showed positive results, the evidence is mixed, and further research is needed. COVID-19 has accelerated the use of digital intervention and created the opportunity for comprehensive research and investigation with more updated information and further impact on readmission.

Can AI substitute a human physician’s second opinion? Recently the Journal of Medical Ethics published two contrasting views: Kempt and Nagel advocate for using artificial intelligence (AI) for a second opinion except when its conclusions significantly diverge from the initial physician’s while Jongsma and Sand argue for a second human opinion irrespective of AI’s concurrence or dissent. The crux of this debate hinges on the prevalence and impact of ‘false confirmation’—a scenario where AI erroneously validates an incorrect human decision. These errors seem exceedingly difficult to detect, reminiscent of heuristics akin to confirmation bias. However, this debate has yet to engage with the emergence of explainable AI (XAI), which elaborates on why the AI tool reaches its diagnosis. To progress this debate, we outline a framework for conceptualising decision-making errors in physician–AI collaborations. We then review emerging evidence on the magnitude of false confirmation errors. Our simulations show that they are likely to be pervasive in clinical practice, decreasing diagnostic accuracy to between 5% and 30%. We conclude with a pragmatic approach to employing AI as a second opinion, emphasising the need for physicians to make clinical decisions before consulting AI; employing nudges to increase awareness of false confirmations and critically engaging with XAI explanations. This approach underscores the necessity for a cautious, evidence-based methodology when integrating AI into clinical decision-making.

The literature on populism is divided on whether economic factors are significant and robust causes of populism. To clarify this, we performed the first systematic review and meta-analysis of the evidence of a causal association between economic insecurity and populism. We combined database searches with searching the citations of eligible studies and recently published reviews. We identified and reviewed thirty-six studies and presented a concise narrative summary and numerical synthesis of the key findings. Although we found significant heterogeneity in several dimensions, all studies reported a significant causal association. A recurrent magnitude was that economic insecurity explained around one-third of recent surges in populism. We tested for publication bias by conducting a funnel-plot asymmetry test and a density discontinuity test of the distribution of t-statistics. We found significant evidence of publication bias; however, the causal association between economic insecurity and populism remains significant after controlling for it.

Background Parenting-related leave policies have gained increasing endorsement across Organisation for Economic Co-operation and Development (OECD) countries in recent decades. Previous reviews have focused on the short-term impacts and found predominantly positive effects on children. Although there is a growing interest in the long-term impact during adolescence and young adulthood, a comprehensive assessment of this aspect is currently lacking. Methods We systematically reviewed studies from three electronic databases (Scopus, Web of Science and PubMed), which used quasi-experimental design and examined policies legislating the introduction or expansion of parenting-related leave policies in North America or Europe. We looked at studies focused on well-being beyond the age of 12 and analyzed the findings across different domains of well-being: health, education and labour market outcomes. Results The quasi-experimental evidence is rather limited. The introduction of leave policies or gender-specific quotas produces substantial benefits in the long run. Further, maternal socioeconomic and educational background appears to play a substantial moderating role between leave and adolescents’ well-being. Adolescents with mothers who have higher levels of education have demonstrated a more pronounced advantage from the extended time spent together, thereby accentuating pre-existing disparities. Conclusions Though the expansion of already long leaves might not generate significant outcomes, the introduction of leave policies or gender-specific quotas produces substantial long-term benefits. This evidence entails considerable policy implications for countries that lack a national leave policy or offer only short durations of paid leave, such as the USA.

Mistreatment in the workplace is a recurring and persistent threat to employee health and organizational productivity. Research has shown that there are five times as many cases of workplace mistreatment reported in China as in the United States. Therefore, we established the mistreatment-employee health relationship in Chinese companies. The search was conducted in the Web of Science, EBSCOhost, PsycInfo, and Google Scholar databases. The search terms used were “China,” “mistreatment,” “abuse,” “neglect,” “exploitation,” and “violence.” The search was not limited by the year of publication. The search found 1,527 articles, 65 of which met the inclusion/exclusion criteria and were used for data analysis and quality assessment. Our results show that the overall prevalence of abuse varies significantly but ranges from 18.5% to 94.6%. Psychological aggression by supervisors and customers was common in the healthcare industry. The experience of abuse was positively correlated with adverse consequences such as emotional fatigue, addiction, and suicidal ideation. By providing evidence of the effects of mistreatment, this study aims to help researchers and practitioners align their policies with global labor standards.

Over three years since the first COVID-19 vaccine was approved, many countries still have suboptimal vaccination rates despite holding great amounts of vaccines. Overall, there is little evidence on which policies are more effective to encourage vaccination, particularly in countries where a large share of the population remains unvaccinated. In this study, we examine the effectiveness of a community-based intervention carried out in March 2022 in North Macedonia, a country with a large and persistent share of the population that remains unvaccinated. The intervention, spearheaded by the Ministry of Health and supported by the World Health Organization and UNICEF, consisted of a mobile caravan offering vaccination and public health advice to different locations across the country on different days. Results from our staggered difference-in-difference model show that the mobile vaccination caravan increased daily vaccination rates by 7.7 vaccines per 100,000 inhabitants during the three weeks after the day of the caravan visit. This corresponds to a 35 % increase with respect to pre-intervention vaccination rates. We estimate a cost-effectiveness of 25.4 US dollars (USD) per additional vaccination induced. These results point to mobile caravan vaccines as an effective and cost-effective strategy to increase COVID-19 vaccination rates, even in a context of persistently suboptimal uptake.

People who reach old age enjoying good physical and mental health can be defined as (health) “superheroes”, given their relatively low impact on healthcare expenditure and the desirable model they represent. To evaluate prevalence and possible determinants of being “physical superheroes” (i.e., free from the ten major chronic conditions, plus obesity), “mental superheroes” (i.e., free from major mental symptoms), and “superheroes” (i.e., both mental and physical superheroes). A telephone-based cross-sectional study (LOST in Lombardia) was conducted in November 2020 (i.e., during the COVID-19 pandemic) on a representative sample of 4,400 adults aged ≥ 65 years from Lombardy region, northern Italy. All participants provided both current data and data referring to one year before. Mental and physical superheroes were 59.0% and 17.6%, respectively. Superheroes were 12.8% overall, 15.1% among men, and 11.1% among women; 20.2% among individuals aged 65–69 years, 11.3% among 70–74, 10.0% among 75–79, and 8.3% among ≥ 80 years. Multivariable analysis showed that female sex, higher age, disadvantaged socio-economic status, and physical inactivity (p for trend < 0.001) were inversely related to being superheroes. People not smoking (adjusted odds ratio, aOR = 1.40), alcohol abstainers (aOR = 1.30), and those free from feelings of hopelessness (aOR = 5.92) more frequently met the definition of superheroes. During COVID-19 pandemic, the proportion of superheroes decreased by 16.3%. Differences in the older adults’ health status are largely attributable to their lifestyles but are also likely due to gender, educational, and socio-economic disparities, which should be properly addressed by public health policies.