New research reveals that child maltreatment leaves measurable biological “scars” on DNA, altering brain structure and function.

The “Make Europe Healthy Again” (MEHA) initiative to tackle Europe’s rising chronic disease crisis launched Wednesday at the European Parliament. Backed by medical experts, policymakers, Children’s He...

YouTube video by RTV Privatfernsehen

YouTube video by CSmedicus

By Peter A. McCullough, MD, MPH

YouTube video by RTV Privatfernsehen

Most no longer would need cochlear implants, some gain normal hearing

Background: Trauma exposure is highly prevalent and associated with various #Health issues. However, healthcare professionals can exhibit trauma-related diagnostic overshadowing bias, leading to misdiagnosis and inadequate treatment of trauma-exposed populations. Generative artificial intelligence (GAI) models are increasingly used in healthcare contexts. No research has examined whether generative artificial intelligence (GAI) demonstrates this bias in decision-making, and how rates of this bias may compare to #MentalHealth professionals (MHPs). Objective: This study aimed to 1) assess trauma-related diagnostic overshadowing among frontier GAI models, and 2) compare evidence of trauma-related diagnostic overshadowing between frontier GAI models and MHPs. Methods: #MentalHealth professionals (N = 232, M age = 43.7) completed an experimental paradigm consisting of two vignettes describing adults presenting with obsessive-compulsive symptoms or substance abuse symptoms. One vignette included a trauma exposure history (i.e., sexual trauma or physical trauma), and one vignette did not include a trauma exposure history. Participants answered questions about their preferences for diagnosis and treatment options for clients within the vignettes. GAI models (Gemini 1.5 Flash, ChatGPT-4o mini, Claude Sonnet, Meta Llama 3) completed the same experimental paradigm, with each block being reviewed by each GAI model 20 times. Mann-Whitney U tests and chi-square analyses were used to assess diagnostic and treatment decision-making across vignette factors and respondents. Results: GAI models, like #MentalHealth professionals, demonstrated some evidence of trauma-related diagnostic overshadowing bias, particularly in Likert-based ratings of PTSD diagnosis and treatment when sexual trauma was present (rb = 0.09–0.19, ps < .001). However, GAI models generally exhibited significantly less bias than MHPs across both Likert and forced-choice clinical decision tasks. Compared to MHPs, GAI models assigned higher mean ratings for the target diagnosis and treatment in the OCD case (rb = 0.43–0.63, ps < .001) and for the target treatment in SUD case (rb = 0.57, p < .001) when trauma was present. In forced-choice tasks, GAI models were significantly more accurate than MHPs in selecting the correct diagnosis and treatment for OCD cases (χ²(1)= 48.84–61.07, ps < .001) and for SUD cases involving sexual trauma (χ²(1)= 15.17–101.61, ps < .001). Conclusions: GAI models demonstrate some evidence of trauma-related diagnostic overshadowing bias, yet the degree of bias varied by task and model. Moreover, GAI models generally demonstrated less bias than #MentalHealth professionals in this experimental paradigm. These findings highlight the importance of understanding GAI biases in #MentalHealthcare. More research into bias reduction strategies and responsible implementation of GAI models in #MentalHealthcare is needed.

Much of the political momentum around the antimicrobial resistance crisis has dissipated, but a new report shows the danger to our health has not

The Alliance of Indigenous Nations International Tribunal — recognized by Canada on a Nation-to-Nation basis — has issued a historic global Order intended to take immediate worldwide effect.

Jake Scott and Jeffrey Morris engage in gaslighting cloaked in “expert” syntax in order to fool people into ignoring the ground-breaking Henry Ford vaxxed/unvaxxed study.