DeepSeek and Qwen in Healthcare: Pioneering Multimodal Large Language Models for Nextgeneration Disease Prediction

With the rapid evolution of artificial intelligence, Large Language Models (LLMs) are increasingly recognized for their potential in medical applications. This paper presents a comprehensive examination of the foundational principles and architectural frameworks underpinning multimodal LLMs, exemplified by DeepSeek and Qwen. These models leverage a unified semantic space to achieve advanced contextual comprehension and inferential capabilities, thereby paving new avenues for precision medicine. We delve into the application of fine-tuning techniques tailored for disease diagnosis within this domain, validating our findings through an empirical evaluation of DeepSeek and Qwen on a multimodal chest X-ray dataset. Our study also underscores both the strengths and limitations of LLMs, highlighting challenges such as data privacy concerns and algorithmic transparency. Despite these hurdles, multimodal LLMs demonstrate significant advantages in disease prediction and diagnostic accuracy. Future work will concentrate on refining model performance and expanding their utility across diverse medical applications, ultimately aiming to establish intelligent assistance systems that play a pivotal role in fostering proactive health societies.