Democratizing MLLMs in Healthcare: TinyLLaVA-Med for Efficient Healthcare Diagnostics in Resource-Constrained Settings
This enables efficient healthcare diagnostics in resource-constrained settings like remote medical areas, though it is incremental as it adapts an existing method to a specific domain.
The paper tackled the problem of deploying Multi-Modal Large Language Models (MLLMs) in healthcare by optimizing TinyLLaVA into TinyLLaVA-Med to reduce computational demands, achieving 64.54% accuracy on VQA-RAD and 70.70% on SLAKE with 18.9W power and 11.9GB memory usage.
Deploying Multi-Modal Large Language Models (MLLMs) in healthcare is hindered by their high computational demands and significant memory requirements, which are particularly challenging for resource-constrained devices like the Nvidia Jetson Xavier. This problem is particularly evident in remote medical settings where advanced diagnostics are needed but resources are limited. In this paper, we introduce an optimization method for the general-purpose MLLM, TinyLLaVA, which we have adapted and renamed TinyLLaVA-Med. This adaptation involves instruction-tuning and fine-tuning TinyLLaVA on a medical dataset by drawing inspiration from the LLaVA-Med training pipeline. Our approach successfully minimizes computational complexity and power consumption, with TinyLLaVA-Med operating at 18.9W and using 11.9GB of memory, while achieving accuracies of 64.54% on VQA-RAD and 70.70% on SLAKE for closed-ended questions. Therefore, TinyLLaVA-Med achieves deployment viability in hardware-constrained environments with low computational resources, maintaining essential functionalities and delivering accuracies close to state-of-the-art models.