Generative AI To Recognize Response Fabrication in PTSD

Abstract Fabricating symptoms of Post-Traumatic Stress Disorder (PTSD) can hinder accurate clinical assessments via structured diagnostic interviews1,2. Symptom simulation or fabrication is a known problem3,4 in PTSD assessments, with diverse motivations including unmet mental health issues, varied socio-economic factors and the potential for external gain from positive diagnoses. Here we introduce an artificial intelligence (AI) framework referred to as Algorithm VeRITAS (Vetting Response Integrity from cross-Talk in Adversarial Surveys), for detecting symptom fabrication in the context of PTSD diagnosis. In contrast to current approaches to fabrication detection which indirectly assess atypical symptom presentations, and have limited reliability, VeRITAS infers statistical dependencies inherent in true response patterns, flagging responses which violate these subtle constraints. With a study sample of n = 651 patients, VeRITAS has an Area Under the Curve (AUC) of ≧ 0:95 ± 0:02, with sensitivity > 95%, specificity > 88%, and positive likelihood ratio between 9:9 - 19:77. Additionally, VeRITAS is difficult-to-impossible to beat with coaching or training; we demonstrate that having advanced training in mental health diagnosis is not helpful in defeating the algorithm. Our tool offers an objective, diseasespecific, fast (average time ≦ 4 min) detection of simulated or feigned PTSD, and on wider adoption, can potentially help resources and disability concessions reach those genuinely in need, while helping to maintain integrity of clinical data. Moreover, reliably identifying patients who might be fabricating symptoms due to unmet mental health needs or socio-economic compulsions can ultimately improve outcomes in disadvantaged communities.