ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation

TL;DR

ClinicalGPT is a specialized large language model trained on diverse medical data, demonstrating superior performance in clinical tasks and comprehensive evaluation compared to existing models.

Contribution

The paper introduces ClinicalGPT, a domain-specific language model for healthcare, trained with extensive medical data and evaluated through a comprehensive clinical framework.

Findings

Outperforms existing models in medical question-answering

Excels in medical exams and patient consultation tasks

Shows improved diagnostic analysis capabilities

Abstract

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.