Academic Journal of Computing & Information Science, 2025, 8(9); doi: 10.25236/AJCIS.2025.080904.
Nannan Cheng1,2, Fangli Li1
1Departemnt of Information Engineering, Jiangxi University of Technology, Nanchang, Jiangxi, 330098, China
2Faculty of Computer Science and Multimedia, Lincon University College, Selangor, KuaLa Lumpur, 47301, Malaysia
The proliferation of large-scale language models, such as ChatGPT, has underscored the urgent requirement to develop Language Models in Medicine (LLMs) to mitigate the burden on healthcare resources. This work introduces MulMed, a model that prioritizes multitasking capabilities in medical domains. MulMed aims to summarize complex medical texts, address patient inquiries, engage in medical question-answering dialogues, demonstrate cross-lingual proficiency, and offer comprehensive medical knowledge coverage. Its key contributions include a two-step fine-tuned modeling framework that enables the model to perform multi-task functions like medical text summarization and Q&A in both English and Chinese, demonstrating excellent generalization abilities on benchmark test sets. The model also exhibits human empathy in doctor-patient consultations, and its fine-tuning process and data are openly available to promote future research in cross-lingual medical models. Additionally, a medical ethics framework is proposed to aid in evaluating the feasibility of medical model applications.
Large Language Model; Multi-Task Model; Cross-Lingual Medical Model
Nannan Cheng, Fangli Li. MulMed: Addressing Multiple Medical Tasks Utilizing LLMS. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 9: 26-33. https://doi.org/10.25236/AJCIS.2025.080904.
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