Application of multilingual large language models in electronic health information synthesis and diagnostic support

The multi-modal and multitask design of large language models (LLMs) have made their deployment in diverse real-world domains, including medical research, medical data analysis, and diagnostic support, more popular in recent times. Existing literature show the potential of these models as powerful tools for synthesizing complex clinical information. This comparison highlights some related works that deploy state-of-the-art domain-agnostic generative LLMs and some medical domain specific counterparts. The compared works show that, the models produce state-of-the-art performances in most test cases, superseding expert human accuracy in some evaluated benchmarks. However, the integration of LLMs into clinical practice is cautiously progressive, focusing on overcoming critical challenges like mitigating hallucination, ensuring robustness against biased data, and navigating stringent regulatory validation to establish their reliability as assistive tools in high-stakes medical environments.