# Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding

arXiv: https://arxiv.org/abs/2305.12031 · HTML: https://mlsanity.com/paper/2305.12031/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 7 of 7)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data](https://mlsanity.com/paper/2304.08247/) (2023)

## Built on by (most cited first, 30 of 71)

- [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://mlsanity.com/paper/2311.16079/) (2023)
- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [Advancing Multimodal Medical Capabilities of Gemini](https://mlsanity.com/paper/2405.03162/) (2024)
- [ECG-FM: An Open Electrocardiogram Foundation Model](https://mlsanity.com/paper/2408.05178/) (2024)
- [Med42-v2: A Suite of Clinical LLMs](https://mlsanity.com/paper/2408.06142/) (2024)
- [From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery](https://mlsanity.com/paper/2508.14111/) (2025)
- [Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data](https://mlsanity.com/paper/2401.06866/) (2024)
- [MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records](https://mlsanity.com/paper/2308.14089/) (2023)
- [Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches](https://mlsanity.com/paper/2404.14779/) (2024)
- [DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature](https://mlsanity.com/paper/2405.04819/) (2024)
- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks](https://mlsanity.com/paper/2404.00376/) (2024)
- [Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?](https://mlsanity.com/paper/2411.04118/) (2024)
- [ALFA: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning](https://mlsanity.com/paper/2502.14860/) (2025)
- [MedResearcher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework](https://mlsanity.com/paper/2508.14880/) (2025)
- [Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data](https://mlsanity.com/paper/2408.13833/) (2024)
- [OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models](https://mlsanity.com/paper/2402.19371/) (2024)
- [Can Large Language Models abstract Medical Coded Language?](https://mlsanity.com/paper/2403.10822/) (2024)
- [Do Large Language Models Align with Core Mental Health Counseling Competencies?](https://mlsanity.com/paper/2410.22446/) (2024)
- [Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare](https://mlsanity.com/paper/2404.16621/) (2024)
- [Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race](https://mlsanity.com/paper/2506.00253/) (2025)
- [CancerLLM: A Large Language Model in Cancer Domain](https://mlsanity.com/paper/2406.10459/) (2024)
- [The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models](https://mlsanity.com/paper/2411.08870/) (2024)
- [CliBench: A Multifaceted and Multigranular Evaluation of Large Language Models for Clinical Decision Making](https://mlsanity.com/paper/2406.09923/) (2024)
- [BioMamba: Domain-Adaptive Biomedical Language Models](https://mlsanity.com/paper/2408.02600/) (2024)
- [Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs](https://mlsanity.com/paper/2409.14988/) (2024)
- [Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning](https://mlsanity.com/paper/2509.19090/) (2025)
- [Clinical-R1: Empowering Large Language Models for Faithful and Comprehensive Reasoning with Clinical Objective Relative Policy Optimization](https://mlsanity.com/paper/2512.00601/) (2025)
- [Clinical Insights: A Comprehensive Review of Language Models in Medicine](https://mlsanity.com/paper/2408.11735/) (2024)
- [Exploring Efficiency Frontiers of Thinking Budget in Medical Reasoning: Scaling Laws between Computational Resources and Reasoning Quality](https://mlsanity.com/paper/2508.12140/) (2025)

## Abstract (arXiv)

We present Clinical Camel, an open large language model (LLM) explicitly tailored for clinical research. Fine-tuned from LLaMA-2 using QLoRA, Clinical Camel achieves state-of-the-art performance across medical benchmarks among openly available medical LLMs. Leveraging efficient single-GPU training, Clinical Camel surpasses GPT-3.5 in five-shot evaluations on all assessed benchmarks, including 64.3% on the USMLE Sample Exam (compared to 58.5% for GPT-3.5), 77.9% on PubMedQA (compared to 60.2%), 60.7% on MedQA (compared to 53.6%), and 54.2% on MedMCQA (compared to 51.0%). In addition to these benchmarks, Clinical Camel demonstrates its broader capabilities, such as synthesizing plausible clinical notes. This work introduces dialogue-based knowledge encoding, a novel method to synthesize conversational data from dense medical texts. While benchmark results are encouraging, extensive and rigorous human evaluation across diverse clinical scenarios is imperative to ascertain safety before implementation. By openly sharing Clinical Camel, we hope to foster transparent and collaborative research, working towards the safe integration of LLMs within the healthcare domain. Significant challenges concerning reliability, bias, and the potential for outdated knowledge persist. Nonetheless, the transparency provided by an open approach reinforces the scientific rigor essential for future clinical applications.