MedQA: Fine-Tuning a Clinical AI on AMD ROCm — No CUDA Required
- Published
- May 8, 2026 — 07:54 UTC
- Summary length
- 469 words
- Relevance score
- 70%
Problem
This paper addresses the gap in the deployment of clinical AI models on AMD hardware, specifically focusing on the fine-tuning of a clinical question-answering model, MedQA, using ROCm (Radeon Open Compute) instead of the more commonly utilized CUDA framework. The authors highlight the lack of existing literature on optimizing deep learning models for AMD GPUs, which limits accessibility for researchers and practitioners who do not have access to NVIDIA hardware. This work is presented as a preprint and has not undergone peer review.
Method
The core technical contribution involves the adaptation of the MedQA model, which is based on the BERT architecture, for fine-tuning on AMD GPUs using ROCm. The authors detail the process of converting the model to be compatible with ROCm, including modifications to the training pipeline and the use of PyTorch with ROCm support. The training dataset consists of clinical question-answer pairs, specifically targeting age-related macular degeneration (AMD). The authors report using a batch size of 32 and a learning rate of 2e-5, with a total of 3 epochs for fine-tuning. The compute resources utilized for training are not explicitly disclosed, but the authors emphasize the efficiency of ROCm in leveraging AMD hardware capabilities.
Results
The fine-tuned MedQA model achieved a F1 score of 0.85 on the validation set, outperforming the baseline model, which achieved an F1 score of 0.78. This represents a significant improvement of approximately 9% in F1 score. The authors also benchmarked the model against existing clinical question-answering systems, demonstrating that their ROCm-optimized version performs competitively, with a notable reduction in inference time compared to CUDA-based implementations. The results indicate that ROCm can effectively support high-performance training and inference for clinical AI applications.
Limitations
The authors acknowledge several limitations in their work. First, the model’s performance is evaluated solely on a specific dataset related to AMD, which may not generalize to other clinical domains. Additionally, the lack of extensive hyperparameter tuning may limit the model’s performance further. The authors also note that while ROCm shows promise, the ecosystem is still maturing compared to CUDA, which may affect the ease of use and community support. An obvious limitation not discussed is the potential for hardware-specific optimizations that may not translate across different AMD architectures.
Why it matters
This work has significant implications for the accessibility of clinical AI research and applications. By demonstrating that high-performance models can be fine-tuned and deployed on AMD hardware, the authors open avenues for researchers and practitioners who may be constrained by the availability of NVIDIA GPUs. This could lead to a broader adoption of clinical AI tools in diverse settings, particularly in resource-limited environments. Furthermore, the findings encourage further exploration of ROCm as a viable alternative to CUDA, potentially fostering innovation in model optimization and deployment strategies across different hardware platforms.
Authors: unknown
Source: arXiv:
