ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge

By Zihan Li, Kai Zhang, Ruilong Dan, Steven Jiang, You Zhang. Cureus, 2023

741 citations recorded by the source corpus.

Objective The primary aim of this research was to address the limitations observed in the medical knowledge of prevalent large language models (LLMs) such as ChatGPT, by creating a specialized language model with enhanced accuracy in medical advice. Methods We achieved this by adapting and refining the large language model meta-AI (LLaMA) using a large dataset of 100,000 patient-doctor dialogues sourced from a widely used online medical consultation platform. These conversations were cleaned and anonymized to respect privacy concerns. In addition to the model refinement, we incorporated a self-directed information retrieval mechanism, allowing the model to access and utilize real-time information from online sources like Wikipedia and data from curated offline medical databases. Results The fine-tuning of the model with real-world patient-doctor interactions significantly improved the model's ability to understand patient needs and provide informed advice. By equipping the model with self-directed information retrieval from reliable online and offline sources, we observed substantial improvements in the accuracy of its responses. Conclusion Our proposed ChatDoctor, represents a significant advancement in medical LLMs, demonstrating a significant improvement in understanding patient inquiries and providing accurate advice. Given the high stakes and low error tolerance in the medical field, such enhancements in providing accurate and reliable information are not only beneficial but essential.

Subfields: Artificial Intelligence, Health Informatics, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/4a7f6c4e71e20311ade4e76e8d0945d499c31fcd

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Citations in the corpus

ASPEN: High-Throughput LoRA Fine-Tuning of Large Language Models with a Single GPU (2023, neutral): “On the other hand, in scenarios involving fine-tuning jobs addressing distinct domain issues [5, 20, 22, 28, 48], each job would use diverse training datasets.”
Leveraging Large Language Models for Mental Health Prediction via Online Text Data (2023, background): “Researchers have explored the capability of these LLMs in health fields [43, 49, 50, 59, 78, 88].”
MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning (2023, positive): “Our contributions are summarized as follows: (i) To the best of our knowledge, we are the first to propose a multi-agent framework within the medical domain and explore how multi-agent communication within the medical setting can lead to a consensus decision, adding a novel dimension to the current…”
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains (2024, methodology): “More recently, multiple LLMs based on decoder-only architecture have been developed for the medical domain, such as BioGPT (Luo et al., 2022) based on GPT-2 (Radford et al., 2019), ClinicalGPT (Wang et al., 2023), based on BLOOM-7B (Workshop et al., 2023), PMC-LLaMA (Wu et al., 2023), MedAlpaca (Han”
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, neutral): “Besides, we have summarized the relevant computation costs from existing studies in Table 8, which aims to provide clear assessment of computation requirements.”
Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using Jailbreaking (2024, methodology): “We utilized the chatdoctor-dataset [18].”