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Background Screening for the social determinants of health in clinical practice is still widely debated. Methods A scoping review was used to (1) explore the various screening tools that are available to identify social risk, (2) examine the impact that screening for social determinants has on health and social outcomes, and (3) identify factors that promote the uptake of screening in routine clinical care. Results Over the last two decades, a growing number of screening tools have been developed to help frontline health workers ask about the social determinants of health in clinical care. In addition to clinical practice guidelines that recommend screening for specific areas of social risk (e.g., violence in pregnancy), there is also a growing body of evidence exploring the use of screening or case finding for identifying multiple domains of social risk (e.g., poverty, food insecurity, violence, unemployment, and housing problems). Conclusion There is increasing traction within the medical field for improving social history taking and integrating more formal screening for social determinants of health within clinical practice. There is also a growing number of high-quality evidence-based reviews that identify interventions that are effective in promoting health equity at the individual patient level, and at broader community and structural levels.

Stable diffusion revolutionized image creation from descriptive text. GPT-2 (ref.  1 ), GPT-3(.5) (ref.  2 ) and GPT-4 (ref.  3 ) demonstrated high performance across a variety of language tasks. ChatGPT introduced such language models to the public. It is now clear that generative artificial intelligence (AI) such as large language models (LLMs) is here to stay and will substantially change the ecosystem of online text and images. Here we consider what may happen to GPT-{ n } once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet.  Analysis shows that indiscriminately training generative artificial intelligence on real and generated content, usually done by scraping data from the Internet, can lead to a collapse in the ability of the models to generate diverse high-quality output.

The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities. Here we identify a set of high-impact potential applications for GMAI and lay out specific technical capabilities and training datasets necessary to enable them. We expect that GMAI-enabled applications will challenge current strategies for regulating and validating AI devices for medicine and will shift practices associated with the collection of large medical datasets. This review discusses generalist medical artificial intelligence, identifying potential applications and setting out specific technical capabilities and training datasets necessary to enable them, as well as highlighting challenges to its implementation.

Background Marginalized Roma communities (MRCs) in Slovakia experience longstanding exclusion from essential services, including healthcare. Roma women, in particular, face compounded vulnerabilities that contribute to unequal access and poorer health outcomes. Despite increasing attention to these issues, a deeper understanding of the lived experiences that shape healthcare access in MRCs remains necessary. Methods A qualitative study was conducted using semi-structured interviews with 13 Roma mothers living in MRCs and 13 professionals working in healthcare, public health, or policy, including six of Roma origin. Data were analyzed using consensual qualitative research and thematic analysis to identify significant access barriers. Results Roma women face multiple, often interconnected barriers to accessing healthcare, many of which are rooted in longstanding structural inequalities. These include distrust of the health system stemming from prior discrimination, difficulties in understanding health-related information and navigating the system, and financial hardship. On the side of healthcare providers, barriers involve shortages in the healthcare workforce, poor care coordination, and discriminatory attitudes. Conclusions Improving access to healthcare for Roma women requires a comprehensive, multi-level strategy. Efforts should focus on building trust, improving communication, addressing financial and systemic obstacles, and investing in culturally sensitive primary care. Health promotion assistants play a crucial role in bridging the gaps between communities and healthcare providers. Culturally sensitive healthcare interventions and inclusive policies are essential to reducing health disparities and promoting equitable access.

Long COVID is an often debilitating illness that occurs in at least 10% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. More than 200 symptoms have been identified with impacts on multiple organ systems. At least 65 million individuals worldwide are estimated to have long COVID, with cases increasing daily. Biomedical research has made substantial progress in identifying various pathophysiological changes and risk factors and in characterizing the illness; further, similarities with other viral-onset illnesses such as myalgic encephalomyelitis/chronic fatigue syndrome and postural orthostatic tachycardia syndrome have laid the groundwork for research in the field. In this Review, we explore the current literature and highlight key findings, the overlap with other conditions, the variable onset of symptoms, long COVID in children and the impact of vaccinations. Although these key findings are critical to understanding long COVID, current diagnostic and treatment options are insufficient, and clinical trials must be prioritized that address leading hypotheses. Additionally, to strengthen long COVID research, future studies must account for biases and SARS-CoV-2 testing issues, build on viral-onset research, be inclusive of marginalized populations and meaningfully engage patients throughout the research process.Long COVID is an often debilitating illness of severe symptoms that can develop during or following COVID-19. In this Review, Davis, McCorkell, Vogel and Topol explore our knowledge of long COVID and highlight key findings, including potential mechanisms, the overlap with other conditions and potential treatments. They also discuss challenges and recommendations for long COVID research and care.

The stability and resilience of the Earth system and human well-being are inseparably linked 1 – 3 , yet their interdependencies are generally under-recognized; consequently, they are often treated independently 4 , 5 . Here, we use modelling and literature assessment to quantify safe and just Earth system boundaries (ESBs) for climate, the biosphere, water and nutrient cycles, and aerosols at global and subglobal scales. We propose ESBs for maintaining the resilience and stability of the Earth system (safe ESBs) and minimizing exposure to significant harm to humans from Earth system change (a necessary but not sufficient condition for justice) 4 . The stricter of the safe or just boundaries sets the integrated safe and just ESB. Our findings show that justice considerations constrain the integrated ESBs more than safety considerations for climate and atmospheric aerosol loading. Seven of eight globally quantified safe and just ESBs and at least two regional safe and just ESBs in over half of global land area are already exceeded. We propose that our assessment provides a quantitative foundation for safeguarding the global commons for all people now and into the future. We find that justice considerations constrain the integrated Earth system boundaries more than safety considerations for climate and atmospheric aerosol loading, and our assessment provides a foundation for safeguarding the global commons for all people.

Background Artificial intelligence (AI)-based systems are being rapidly integrated into the fields of health and social care. Although such systems can substantially improve the provision of care, diverse and marginalized populations are often incorrectly or insufficiently represented within these systems. This review aims to assess the influence of AI on health and social care among these populations, particularly with regard to issues related to inclusivity and regulatory concerns. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six leading databases were searched, and 129 articles were selected for this review in line with predefined eligibility criteria. Results This research revealed disparities in AI outcomes, accessibility, and representation among diverse groups due to biased data sources and a lack of representation in training datasets, which can potentially exacerbate inequalities in care delivery for marginalized communities. Conclusion AI development practices, legal frameworks, and policies must be reformulated to ensure that AI is applied in an equitable manner. A holistic approach must be used to address disparities, enforce effective regulations, safeguard privacy, promote inclusion and equity, and emphasize rigorous validation.