# Development and Evaluation of HopeBot: an LLM-based chatbot for structured and interactive PHQ-9 depression screening

arXiv: https://arxiv.org/abs/2507.05984 · HTML: https://mlsanity.com/paper/2507.05984/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 78 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support](https://mlsanity.com/paper/2106.01702/) (2021)
- [A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions](https://mlsanity.com/paper/2410.12837/) (2024)
- [Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model](https://mlsanity.com/paper/2305.17116/) (2023)
- [Enhanced Large Language Models for Effective Screening of Depression and Anxiety](https://mlsanity.com/paper/2501.08769/) (2025)
- [Perla: A Conversational Agent for Depression Screening in Digital Ecosystems. Design, Implementation and Validation](https://mlsanity.com/paper/2008.12875/) (2020)

## Built on by (most cited first, 1 of 1)

- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)

## Abstract (arXiv)

Static tools like the Patient Health Questionnaire-9 (PHQ-9) effectively screen depression but lack interactivity and adaptability. We developed HopeBot, a chatbot powered by a large language model (LLM) that administers the PHQ-9 using retrieval-augmented generation and real-time clarification. In a within-subject study, 132 adults in the United Kingdom and China completed both self-administered and chatbot versions. Scores demonstrated strong agreement (ICC = 0.91; 45% identical). Among 75 participants providing comparative feedback, 71% reported greater trust in the chatbot, highlighting clearer structure, interpretive guidance, and a supportive tone. Mean ratings (0-10) were 8.4 for comfort, 7.7 for voice clarity, 7.6 for handling sensitive topics, and 7.4 for recommendation helpfulness; the latter varied significantly by employment status and prior mental-health service use (p < 0.05). Overall, 87.1% expressed willingness to reuse or recommend HopeBot. These findings demonstrate voice-based LLM chatbots can feasibly serve as scalable, low-burden adjuncts for routine depression screening.