Performant and Small: Can We Have Both? SLMs on Mobile Devices for Healthcare Chatbots
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abstract = {Large language models (LLMs) are increasingly being adopted across diverse healthcare scenarios. However, their deployment on mobile devices is hindered by significant resource demands and privacy concerns associated with cloud-based solutions. Ensuring reliable, private, and accessible healthcare support on mobile devices requires models that are both performant and lightweight. Therefore, small language models (SLMs) present a promising solution for enabling on-device healthcare support. This study explores the trade-offs between model size and performance necessary to effectively execute general medical question-answering tasks on mobile devices. To evaluate this, we present MedicoAI, a cross-platform application designed to support local SLMs inference across mobile, web, and desktop environments. We evaluated four state-of-the-art SLMs with model sizes under 1GB using two prompt templates (a standard baseline and one with medical safety constraints) and three word-limit configurations. Our findings highlight the viability of deploying SLMs for medical question-answering on mobile devices while maintaining user privacy and resource efficiency.},
apice = {SlmmobiledevicesPercomworkshops2026},
author = {Aqila Farahmand and Montagna, Sara and Alessandro Bogliolo and Stefano Ferretti and Magnini, Matteo},
booktitle = {2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)},
dblp = {conf/percom/FarahmandMBFM26},
doi = {10.1109/PerComWorkshops68308.2026.11585382},
isbn = {979-8-3315-7615-8},
keywords = {Small Language Models, Mobile Health, Patient Self-Management, Local Models, Privacy-preserving},
month = {16-20 March},
note = {5th International Workshop on Telemedicine and e-Health in the digital society (TELMED 2026)},
numpages = 6,
openalex = {W7167064743},
pages = {1--6},
publisher = {IEEE},
title = {Performant and Small: Can We Have Both? SLMs on Mobile Devices for Healthcare Chatbots},
url = {https://ieeexplore.ieee.org/document/11585382},
urlpdf = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11585382},
year = 2026
}