By Peter A. McCullough, MD, MPH

YouTube video by RTV Privatfernsehen

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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.

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