Pocket-Dentist-Bench

🚧 Dataset Coming Soon — The full benchmark data will be released upon paper acceptance.
Please check back later or ⭐ star this repo to get notified.

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Links

• 📄 Paper: Coming soon
• 💻 Code: GitHub
• 🤗 Models: Coming soon

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Overview

Pocket-Dentist is a large-scale multimodal benchmark and deployment pipeline for evaluating Vision-Language Models (VLMs) on dental image understanding tasks. The benchmark curates and standardizes seven dental datasets into a unified vision-language evaluation framework.

Key Statistics:

• 🏥 6,000+ patients
• 🖼️ 71,000+ images
• 📷 4 imaging modalities
• 📋 6 task types
• 📊 14 evaluation metrics
• 🤖 14 VLMs benchmarked (including 12 open-weight models with LoRA adaptation)

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Benchmark Pipeline

The Pocket-Dentist evaluation pipeline consists of four stages:

1. Data Collection & Unification — Curating heterogeneous dental datasets into a unified multimodal benchmark
2. Task Design & Annotation — Converting source annotations into shared prompt–response task formats
3. Model Evaluation & Adaptation — Evaluating VLMs under zero-shot, few-shot, and LoRA adaptation settings
4. On-Device Deployment — Measuring local inference efficiency for compact adapted models on mobile hardware

Figure 1: Deploy-aware evaluation pipeline of Pocket-Dentist.

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Benchmark Results

Zero-Shot Performance

Under zero-shot evaluation, no single model dominates across all 14 metrics. Closed-source APIs (Gemini) perform best overall, while compact model performance is fragmented across tasks.

Table 1: Zero-Shot (ZS) Results — Click to expand

Large VLMs (≥7B)

Model	BRAR Acc	BRAR F1	DR F1w	Meta VQA	Meta Cap	Meta Cls	Aariz VQA	Aariz CVM	COde Cls	DenPAR Arch	DenPAR Site	DenPAR MAE↓	Caries Det	Caries Cls
Lingshu-32B	0.49	0.39	0.60	0.63	0.18	0.34	0.26	0.13	0.48	0.59	0.53	0.88	0.56	0.16
MedMO-8B-Next	0.26	0.19	0.53	0.49	0.09	0.08	0.21	0.05	0.26	0.61	0.29	2.90	0.59	0.84
Qwen2.5-VL-7B	0.27	0.17	0.32	0.45	0.15	0.23	0.20	0.00	0.50	0.40	0.35	1.01	0.63	0.14
gemini-2.0-flash	0.57	0.37	0.00	0.63	0.18	0.36	0.29	0.25	0.54	0.84	0.45	0.42	0.50	0.12
gemini-2.5-flash	0.27	0.26	0.62	0.66	0.14	0.24	0.23	0.12	0.58	0.99	0.51	0.47	0.54	0.13

Compact VLMs (≤4B)

Model	BRAR Acc	BRAR F1	DR F1w	Meta VQA	Meta Cap	Meta Cls	Aariz VQA	Aariz CVM	COde Cls	DenPAR Arch	DenPAR Site	DenPAR MAE↓	Caries Det	Caries Cls
Qwen3.5-4B	0.17	0.10	0.54	0.82	0.10	0.16	0.17	0.04	0.11	0.40	0.19	3.02	0.49	0.18
Qwen3-VL-4B	0.44	0.37	0.24	0.58	0.20	0.22	0.23	0.08	0.54	0.44	0.23	0.42	0.63	0.58
gemma-4-E4B-it	0.56	0.24	0.61	0.59	0.18	0.31	0.31	0.04	0.51	0.40	0.51	0.52	0.43	0.30
medgemma-4b-it	0.44	0.33	0.57	0.54	0.14	0.16	0.40	0.03	0.27	0.40	0.23	0.89	0.52	0.11
paligemma2-3b	0.10	0.06	0.00	0.00	0.00	0.00	0.20	0.03	0.00	0.00	0.18	0.89	0.64	0.00
SmolVLM2-2.2B	0.56	0.35	0.56	0.00	0.10	0.15	0.23	0.05	0.10	0.60	0.10	0.89	0.44	0.92
InternVL3.5-2B	0.50	0.27	0.09	0.15	0.00	0.00	0.37	0.00	0.14	0.40	0.22	3.21	0.36	0.12
gemma-4-E2B-it	0.56	0.24	0.24	0.48	0.15	0.25	0.39	0.03	0.50	0.27	0.23	0.73	0.61	0.11
InternVL3.5-1B	0.26	0.14	0.62	0.34	0.00	0.00	0.21	0.07	0.14	0.28	0.19	2.27	0.61	0.11

Bold = best in tier. ↑ higher is better; MAE ↓ lower is better.

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On-Device Deployment

We deploy LoRA-tuned VLMs on an iPhone 17 Pro (A19 Pro SoC, 12 GB Unified Memory) via Metal-accelerated inference using llama.cpp. All computation is performed locally on the device with 100% offline privacy protection.

Model	Total Latency (s) ↓	TTFT (s) ↓	ITPS (t/s) ↑	OTPS (t/s) ↑	RAM (GB) ↓
Pocket-Dentist-4B	6.67	1.22	315.95	17.07	4.09
InternVL3.5-2B	4.74	0.78	434.53	29.47	2.62
Qwen2.5-VL-7B	24.06	2.29	148.93	9.60	6.22

Pocket-Dentist iOS app running Pocket-Dentist-4B locally on an iPhone 17 Pro.

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Key Findings

• 🔍 Zero-shot fragmentation: No single model dominates across all dental tasks under zero-shot evaluation
• 📈 LoRA adaptation closes the gap: Under a uniform LoRA budget, compact VLMs become competitive with substantially larger models
• 🏆 Qwen3-VL-4B achieves the strongest overall performance among compact models, matching or outperforming larger open-weight models (7B–32B) on most primary task metrics
• 📱 Pocket-Dentist-4B (LoRA-tuned Qwen3-VL-4B) runs locally on an iPhone 17 Pro with 6.67s per-sample latency and 4.09 GB RAM
• 🏥 Medical pre-training alone does not guarantee dental task performance — dental-domain LoRA adaptation is more effective

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License & Disclaimer

This benchmark integrates data from multiple publicly available dental imaging datasets, each with its own license. This repository is distributed under CC BY-NC-SA 4.0. Users must also comply with the individual licenses of the constituent datasets.

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