BiMediX2 : Bio-Medical EXpert LMM with English and Arabic Language Capabilities

Sahal Shaji Mullappilly*, Mohammed Irfan K*, Sara Pieri, Fahad Khan, Rao Muhammad Anwer, Salman Khan, Timothy Baldwin, and Hisham Cholakkal

*Equally contributing first authors

Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), UAE

👩‍⚕️ Overview

Introducing BiMediX2, the first bilingual medical LLM and LMM based on Llama3.1, designed for seamless interaction in both English and Arabic. BiMediX2 facilitates a wide range of medical interactions, including multi-turn chats, multiple-choice question answering, open-ended question answering, along with the ability to understand and analyze medical images. Our model outperforms GPT-4 on English medical benchmarks and achieves state-of-the-art results in various Medical Multimodal evaluations thanks to the high-quality Arabic-English bilingual healthcare dataset and instruction sets.

Our models and datasets will be publicly released in HuggingFace 🤗.

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🏆 Contributions

Our contributions are as follows:

Arabic-English Bilingual Healthcare LLM

• BiMediX2 is the first medical LLM based on Llama3.1 to achieve excellent results on English, Arabic, and bilingual text-based medical LLM benchmarks.
• Our BiMediX2 LLM outperform GPT4 more than 8% on the USMLE benchmark.
• We created high-quality Arabic-English bilingual medical instruction sets using a semi-automated translation pipeline with Llama3 and GPT-3.5, complemented by manual verification. BiMediX2 is instruction-tuned with these bilingual instruction sets.
• Similar to our previous BiMediX version, BiMediX2 supports soliciting follow-up questions to gather more information about patient symptoms.

BiMediX2 VLM: Extension to Medical Image Modalities

• We created an Arabic-English instruction set with 120k image-text pairs across different medical image modalities to train BiMediX2 VLM.
• Our model supports multiple medical image modalities as input, enabling users to upload medical images and clarify their questions in both Arabic and English.
• We developed the first Arabic medical VLM evaluation benchmark that evaluates VLMs across different imaging modalities.
• Our BiMediX2 VLM achieves state-of-the-art results on medical English and Arabic VLM evaluation benchmarks.

🌟 Examples