Pocket-Dentist: On-Device Dental Image Understanding via Efficient Multimodal Large Language Models
arXiv:2605.29299v1 Announce Type: cross Abstract: Evaluations of dental vision-language models remain fragmented across datasets, task definitions and metrics, and often ignore their computational cost. This limits their widespread deployment for dental screening outside specialist centres, where timely inference, limited hardware, and local handling of patient images are vital for practical, privacy-preserving clinical prescreening. Here we present Pocket-Dentist, an efficiency-aware benchmark for dental multimodal question answering that brings together three datasets spanning approximately
The proliferation of multimodal large language models (MM-LLMs) combined with the demand for privacy-preserving, on-device AI applications is driving innovation in specialized healthcare domains.
This development signifies a crucial step towards democratizing advanced diagnostic capabilities, enabling early detection and intervention in dental health, particularly in underserved areas.
The ability to run sophisticated dental image analysis on local hardware shifts diagnostic power closer to the patient, reducing reliance on centralized specialist centers and improving accessibility.
- · Patients in remote or resource-limited areas
- · Dental AI software developers
- · On-device AI hardware manufacturers
- · Dentists and hygienists
- · Centralized diagnostic imaging centers reliant solely on specialist interpretati
- · Non-AI enhanced dental diagnostic tools
Widespread adoption of Pocket-Dentist or similar tools will lead to earlier identification of dental issues.
Improved early detection will reduce the incidence of advanced dental diseases and associated healthcare costs.
This paradigm shift could set a precedent for on-device, privacy-preserving AI diagnostics across various medical specialties, decentralizing healthcare delivery.
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Read at arXiv cs.AI