This four-paper Series and accompanying comments examine the growing but under-recognised burden of chronic liver disease in Europe, with a focus on steatotic liver disease, including metabolic dysfunction-associated steatotic liver disease, and alcohol-related liver disease, along with viral hepatitis B and C. The Series provides country-level data on MASLD policies, analyses systemic failures in prevention, timely detection, and equitable access to care across diverse populations and health systems; and evaluates policy gaps, health system responses, and innovations in diagnostics, care models, and public health strategies. The Series calls for the rapid integration of metabolic liver health into non-communicable disease frameworks, strengthened surveillance and care pathways, and coordinated, equity-driven, and preventative approaches to identify the “missing millions” and reduce the rising human and economic toll of chronic liver disease across the region.

Higher education must rethink assessment practices in response to the growing integrity challenges posed by generative artificial intelligence (GenAI), say authors in this Policy Forum. They analyzed data on student use of this technology across 20 major public research universities in the United States. The impact of GenAI on higher education is highly debated. In many ways, the technology is making common forms of evaluation, such as tests, projects, or term papers, less reliable as a measure of student capability. This highlights the need for a better understanding of where GenAI use is most prevalent and where misuse is most likely to occur. Igor Chirikov and colleagues analyzed survey data from more than 95,000 students across 20 U.S. research universities during the 2023-2024 academic year. Their findings reveal widespread GenAI use among students: roughly two-thirds of students reported using it over the study period, with 37% using it regularly. However, usage patterns differed considerably by discipline, with higher adoption in STEM fields. For example, 62% of computer science students reported regular usage, compared with only 24% of students in the arts. Notably, some social science disciplines, such as business and economics, also demonstrated high levels of adoption. Patterns of GenAI-assisted cheating also varied across disciplines. Estimated rates of misuse were generally higher in non-STEM fields, with economics (17%) and journalism (16%) showing relatively high rates, whereas biology (5%) was among the lowest. The study also found significant demographic disparities in GenAI use, with higher adoption among male, White, and Asian students than among female and underrepresented minority students. Although differences tied to socioeconomic status and disability were smaller, the authors suggest that the findings raise concerns about unequal access to AI tools and literacy. Chirikov et al. propose several paths forward. They note that there is no single “AI-proof” assessment model and suggest reforms tailored to individual disciplines. They place a focus on preparing students to use AI responsibly in professional contexts.

CIRES-led research reveals two-thirds of US television news segments about climate change don’t mention policy at all.

As temperatures rise across the UK, new research from the University of East London warns that overheating in homes, hospitals and care settings is becoming a major public health threat, with older people and vulnerable groups most at risk. The study finds that current building regulations and heat adaptation measures are failing to keep pace with climate pressures, leaving millions exposed to unsafe indoor temperatures. Researchers are calling for stronger overheating rules, better public guidance and greater integration of heat resilience into housing, healthcare and planning policy.

Launching today at the 79th World Health Assembly with more than 14,000 entries across all of healthcare, the first global database of its kind.

What psychological factors influence public support for decarbonisation policies? A team at the University of Geneva (UNIGE) has developed an artificial intelligence–based model to predict the level of public support for energy transition measures, whether related to electricity, heating or mobility. Based on fourteen key factors, the model shows that emotions play a decisive role than purely rational considerations. Published in Nature Energy, this research provides policymakers with practical tools to design transparent communication and strengthen public acceptance of energy transition policies.

When health policy researchers at the Brown University School of Public Health tracked data from primary care practices that had been acquired by private equity firms, they found something surprising: not all of the changes were detrimental.