Targeting only a small group of well-connected individuals with smoking reduction interventions produces the largest reductions in smoking, researchers report. The findings provide actionable insights for designing social network-based interventions that leverage peer influence effectively. Peer influence plays a powerful role in shaping adolescent behavior, particularly in health-related habits such as smoking, as behaviors and norms spread through social networks during a developmentally sensitive period. Although prior research has shown that such influences can cascade through friendships and even friends-of-friends, how quickly the influence fades with social distance, and how social network structure affects that process, has remained unclear. Using Stochastic Actor-Oriented Models (SAOMS) and data from 3,154 students across two schools in the National Longitudinal Study of Adolescent to Adult Health (Add Health) dataset, Cheng Wang and colleagues investigated how peer influence decays across social distance. To better understand how behavioral effects spread and diminish, they evaluated the effects of different smoking reduction intervention strategies, targeting either random students or those most central within social networks. Wang et al. found that peer influence in the context of smoking reduction interventions extends up to three degrees of separation from intervention targets. Targeting 10 to 30% of highly connected individuals within a social network produced the greatest reductions in smoking, while increasing coverage beyond 40 to 50% yielded diminishing returns due to network saturation. Moreover, the authors discovered that social network structure mattered. For example, influence spread more widely and decayed more slowly in denser networks, while sparser networks showed more limited diffusion.

A cutting-edge large language model (LLM) outperformed human doctors in common clinical reasoning tasks including emergency room decisions, identifying likely diagnoses, and choosing next steps in management, according to a new study that used real emergency department data. The authors of the study – one of the largest studies to date to compare artificial intelligence and physicians on a wide array of clinical reasoning tasks – are clear that their results do not mean AI systems are ready to practice medicine on their own, or that doctors can be removed from the diagnostic process. The results do, however, raise urgent questions about the future evaluation and implementation of artificial intelligence (AI) tools in clinical care. For more than 65 years, difficult clinical diagnostic cases have been the gold standard for evaluating medical computing systems. Most recently, LLMs have surpassed earlier computational approaches on these complex cases. However, despite this progress, most medical studies of LLMs have examined narrow or highly controlled scenarios and often lacked direct comparison to the performance of human physicians in real-world clinical reasoning tasks. The rapid advancement of LLM-based medical tools now necessitates more rigorous evaluation.

 

Here, Peter Brodeur and colleagues comprehensively evaluated the diagnostic and treatment-planning abilities of an advanced LLM – the OpenAI o1 series – by comparing its performance to that of hundreds of physicians and earlier AI systems, across a range of clinical reasoning tasks. These included both standardized clinical cases and a real-world study involving randomly selected emergency room patients at a major emergency medical center in Massachusetts. Brodeur et al. found that, across all six experiments, the LLM model consistently matched or exceeded human performance in diagnostic and management reasoning. Notably, its advantage was most pronounced in early-stage emergency department triage, where clinicians must make rapid decisions with minimal information. While both humans and AI improved as more clinical data became available, the model demonstrated a distinct strength under conditions of uncertainty, using even fragmented, unstructured health record data effectively. According to the authors, LLMs are rapidly approaching, and in some areas surpassing, human-level clinical reasoning, and although AI-assisted decision-making is often viewed as risky, the findings suggest such tools – when used in collaboration with physicians’ assessments – could reduce diagnostic errors, delays, and disparities in access to care. However, the authors also note several important limitations of the study. For example, its focus was confined to text-based reasoning, whereas clinical practice depends heavily on visual and auditory cues, areas where current AI remains less capable. “Accuracy on a defined task is only one dimension of deployment readiness. Clinical AI must also deliver equitable, cost-effective, and safe outcomes, supported by accountability, transparency, and ongoing monitoring,” write Ashley Hopkins and Erik Cornelisse in a related Perspective. “Without robust demonstrated effectiveness, equity, and safety, many AI systems will remain insufficient for clinical use.”

 

***A related embargoed news briefing was held on Tuesday, 28 April. Recordings are available at https://aaas.zoom.us/rec/share/UpQfIbq6-1hq5AAk8728u9GxhtdmTQjaycbE58CtTdm0qFYI7xbABpyz9Lx7f073.O2z6hgNbQnfWC0rz  passcode: ytiHe.4M***

A long-standing scientific consensus holds that vaccines are not associated with autism. However, a recent shift in messaging by the U.S. Centers for Disease Control and Prevention (CDC) moved from emphasizing this consensus to highlighting uncertainty, potentially implying that a link has not been fully ruled out. Although this policy change could have significant consequences for public health, its effects on how people think, feel, and act regarding vaccination remain poorly understood. Moreover, moving from a clear, evidence-based consensus to messaging that emphasizes uncertainty may do more than influence vaccine decisions, it can also contribute to wider social effects, such as deepening political divisions, weakening trust in institutions, and making individuals more receptive to dismissing scientific evidence, especially in areas where experts largely agree. In this Policy Article, Bohm et al. conducted an online experiment using an opt-in sample of 2,989 U.S. adult participants. Participants were randomly exposed to either a consensus-based or uncertainty-based CDC-style statement, and their responses were measured. They found that exposure to the uncertainty-based message increased perceived vaccine risks and uncertainty, reduced vaccination intentions, lowered trust in the CDC, and increased endorsement of science-denial strategies. These effects occurred regardless of participants’ political orientation. “Overall, the findings underscore the responsibility of public health institutions to communicate scientific uncertainty in ways that protect, rather than inadvertently weaken, population health,” write the authors.

Experts are warning that artificial intelligence (AI) must be carefully evaluated and governed before it is adopted widely in healthcare, saying rapid advances do not automatically translate into safe use for patients.

In a new study, researchers found a large language model (LLM) outperformed physicians across many common clinical reasoning tasks including emergency room decisions, identifying likely diagnoses, and choosing next steps in management.

The scientific consensus is that vaccinations are neither causally nor statistically linked to autism. The US health authority CDC changed its official communication on this matter and instead emphasised a connection could not be scientifically ruled out. An international research team from Vienna, Erfurt, Hamburg, and Copenhagen, led by the University of Vienna, has now investigated the consequences of this. The results are clear: those who read the current statement were less willing to get vaccinated. Furthermore, this led to decreased trust in the health authority and encouraged the endorsement of science denialist thinking. The societal consequences of such communication are therefore manifold; the researchers call for new guidelines to ensure careful and evidence-based communication by health authorities. The findings have recently been published in the renowned journal Science.

Staying active is known to improve sleep, but the research findings are mixed regarding the best workout. Some studies say light walking or stretching is best, others prefer moderate-intensity workouts like jogging — and some even find that vigorous exercise such as swimming makes sleep worse.

Resolving this discrepancy is important because good sleep is known to reduce the risk of developing dementia. Many of the estimated 8 to 10 million older adults in the United States who live with mild cognitive impairment—which sometimes occurs before dementia — struggle to get a good night’s sleep. They tend to sleep about 34 minutes less per night than others, take longer to fall asleep and spend more time awake throughout the night.

Now, researchers with the Center for Community Health and Aging at the Texas A&M University School of Public Health have a clear answer to the exercise question: Both light and vigorous exercise can reduce sleep disturbances for older adults with mild cognitive impairment.