ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data
  and Comprehensive Evaluation
Guangyu Wang Guoxing Yang Zongxin Du Longjun Fan Xiaohu Li
Large language models have exhibited exceptional performance on various
Natural Language Processing (NLP) tasks, leveraging techniques such as the
pre-training, and instruction fine-tuning. Despite these advances, their
effectiveness in medical applications is limited, due to challenges such as
factual inaccuracies, reasoning abilities, and lack grounding in real-world
experience. In this study, we present ClinicalGPT, a language model explicitly
designed and optimized for clinical scenarios. By incorporating extensive and
diverse real-world data, such as medical records, domain-specific knowledge,
and multi-round dialogue consultations in the training process, ClinicalGPT is
better prepared to handle multiple clinical task. Furthermore, we introduce a
comprehensive evaluation framework that includes medical knowledge
question-answering, medical exams, patient consultations, and diagnostic
analysis of medical records. Our results demonstrate that ClinicalGPT
significantly outperforms other models in these tasks, highlighting the
effectiveness of our approach in adapting large language models to the critical
domain of healthcare.