Weiyan Zhang

Artificial intelligence doctor assistants (AIDAs) help streamline clinical decision-making and reduce physician workload. While existing systems primarily utilize Large Language Models (LLMs) or retrieval-augmented generation (RAG), these methods typically retrieve static facts—whether as text pa...

Training large language models for domain adaptation poses a significant challenge in balancing the acquisition of domain knowledge with the retention of general abilities, often leading to catastrophic forgetting. While curriculum learning offers a promising direction, conventional methods typic...

With the remarkable performance of large language models (LLMs) in medicine, particularly their ability to support clinical decision-making in medical dialogues, a key limitation remains: the static reasoning patterns derived from human expert experience are often inadequate for the dynamic and d...

Medical visual question answering (MedVQA) requires models to provide accurate answers given a medical image and a corresponding question. Recently, instruction tuning of general large vision–language models (LVLMs) has become a dominant paradigm for this task, enabling open-ended predictions and...

Enhancing the task-specific capabilities of Large Language Models (LLMs) primarily requires substantial instruction-tuning datasets. However, the sheer volume of such data imposes a considerable annotation cost, and a lack of optimization methods for tailoring LLMs to specific tasks persists. To...