AI-Driven Trauma Detection in Clinical Education Settings: A Social Justice Framework for PTSD Care

Abstract

Abstract-Background: Two distinct methods are employed by physicians to instruct individuals on how to identify post-traumatic stress disorder (PTSD). On average, patients of color are required to wait 2.7 times longer than other patients, and it takes over 8 weeks to receive a response. If you examine stress in numerous distinct groups, it is more probable that your prejudices will persist.

Problem: The current method of screening individuals for post-traumatic stress disorder (PTSD) is neither equitable nor beneficial. These methods do not always identify trauma in groups that have not received sufficient assistance in the past, and they do not consider their concealed biases when evaluating professional students.

Objective: An AI-led framework for trauma diagnosis is being integrated into clinical training and evaluated as part of this investigation. The primary objective of the investigation is to identify more effective methods for identifying health issues and to ensure that all individuals receive the same level of treatment by utilizing algorithmic fairness limits.

Methods: In a period of 18 months, 12 medical institutions collaborated to develop an AI system that was capable of performing multiple tasks. This approach was implemented to analyze 347 cases in which 892 medical students acquired the skills necessary to become physicians. Behavior can be investigated by combining ensemble learning, BERT (transformer-based natural language processing), and three-dimensional convolutional neural networks. There were 23 distinct types of individuals in the fabricated sample. The models and methods of justice-based intersectional analysis that have been demonstrated to be beneficial for individuals with post-traumatic stress disorder (PTSD) were employed to develop it.

Results: The recognition procedure was successful 89.3% of the time, resulting in a 74% reduction in the time required to locate an item, from 8.2 weeks to 2.1 weeks. In the groups that were not receiving sufficient attention, there were 34% more individuals than in the groups that were. The incidence of student bias decreased by 76%, from 0.34 to 0.08. However, only 52.1% of the students who completed the standard course achieved success, despite the fact that 75% of them did.

Conclusion: This method demonstrates that AI has the potential to improve the equity of health care and the accuracy of diagnoses by establishing clear guidelines for the responsible use of AI in trauma-informed clinical education. This also reduces structural inequality by 73.5%.