Clinical Report: Development and Validation of a Multistage Counseling Model Utilizing Conversational AI for Detecting Child Abuse
Overview
This study introduces a framework, Conversational Artificial Intelligence for Child Abuse Detection (CACAD), aimed at detecting child abuse through a multistage counseling model utilizing conversational AI.
Background
Child abuse is a significant global issue, with approximately 1 billion children affected by various forms of violence. Effective early identification and intervention are critical to mitigate the long-term consequences of abuse. However, many countries face challenges in implementing effective prevention and response systems, leading to high workloads and burnout among child protection professionals.
Data Highlights
No numerical data or trial data provided in the source material.
Key Findings
The proposed CACAD framework consists of two stages, with the LLM acting as the primary counseling agent.
Existing rule-based counseling systems lack flexibility and can only detect a single type of abuse.
Children often experience multiple forms of abuse, necessitating a multilabel prediction framework.
Conversational AI has shown promise in other contexts, matching human counselors in performance.
Risks associated with LLMs include generating inaccurate predictions and inappropriate questions during counseling.
Clinical Implications
The CACAD framework aims to provide an AI-driven counseling tool for detecting child abuse.
Conclusion
The development of the CACAD framework represents a step towards integrating conversational AI into child abuse counseling.
