Abstract
This study evaluated the efficacy of three Generative AI tools—ChatGPT, Claude, and Gemini— in identifying Major Depressive Disorder (MDD) from 119 written clinical case descriptions. Each AI tool determined if cases met DSM-5 criteria for MDD. The results showed significant performance variations, with Gemini demonstrating an accuracy of 97%. These findings suggest the potential for AI assistance in clinical practice, while also underscoring the importance of professional judgment. The implications for counselor education include opportunities to integrate AI tools into training programs, particularly in courses on psychopathology and clinical skills. Educators can use these tools to enhance students’ diagnostic skills and critical thinking abilities. For supervisors, the study emphasizes the importance of guiding supervisees in balancing AIassisted insights with clinical expertise. This research contributes to the discourse on AI in mental health care by offering insights into preparing future practitioners for an increasingly techintegrated clinical landscape. The ethical considerations for AI implementation in counseling are also discussed.
Recommended Citation
Rensi, Matthew; Ni, Chung-Fan; Gonzalez, Jr., Leo; Barta, Mindi; Dykeman, Cass; and Geisler, James
(2025)
"Evaluating Generative AI for Depression Diagnosis: Implications for Counselor Education and Supervision,"
Journal of Technology in Counselor Education and Supervision: Vol. 6:
Iss.
1, Article 8.
DOI: https://doi.org/10.61888/2692-4129.1130
Available at:
https://digital.sandiego.edu/tces/vol6/iss1/8
Included in
Clinical Psychology Commons, Counseling Psychology Commons, Student Counseling and Personnel Services Commons
