This review discusses the rapid deployment of 5G and the associated debate about potential human health impacts from EMF exposure, particularly at 3.5–26 GHz including millimeter waves. It emphasizes limited published studies in these exposure ranges and highlights EU-funded initiatives and research consortia aimed at closing knowledge gaps. The authors state that guidelines are generally considered adequate at present, but argue that uncertainties—especially regarding long-term exposure—support continued research and precautionary approaches.

This review examines links between extremely low-frequency electromagnetic fields, especially the Schumann resonance at ~7.83 Hz, and biological regulation of bioelectricity. It describes proposed mechanisms involving calcium flux modulation and downstream effects on neural activity (including EEG) and circadian rhythms. The article presents both potential benefits from controlled ELF exposures (e.g., therapeutic applications) and potential harms from artificial EMFs disrupting key physiological processes, while emphasizing the need for further research.

No abstract was provided in the source text. The provided overview indicates this is a case study of self-diagnosed electromagnetic hypersensitivity (EHS), describing symptoms attributed by an individual to EMF exposure. The supplied text emphasizes ongoing concern about potential health risks and calls for rigorous scientific inquiry and support for affected individuals.

This policy brief focuses on how RF-EMF exposure should be quantified in health research, emphasizing the role of near-field sources and proposing cumulative dose (J/kg/day) as a health-relevant metric. It reports mean cumulative dose estimates of 0.29 J/kg/day for the whole body and 0.81 J/kg/day for the brain. The brief notes established RF-EMF effects (heating, microwave hearing under highly pulsed radiation, and stimulation) and discusses indications of biological effects below thermal thresholds, while stating that improved metrics do not by themselves confirm harm.

This analytical study evaluated machine learning models (SVM and Random Forest) to predict health symptoms in adults living near mobile phone base stations. The SVM model reportedly achieved high predictive performance for headache, sleep disturbance, dizziness, vertigo, and fatigue, and outperformed Random Forest and prior models. The abstract concludes that proximity to base stations is connected with increased prevalence of several symptoms and emphasizes distance, age, and duration of residence as key predictors.

This narrative review discusses potential public health consequences of widespread mobile phone use and the controversy around long-term cancer risks. It states that evidence from epidemiological and experimental studies suggests long-term exposure to mobile phone emissions may be linked to a small to moderate increased risk of certain cancers, while also emphasizing that data are insufficient for a final risk assessment. The author argues that even small risks could matter at the population level and that exposure-reduction measures may be indicated given uncertainties.