AI + Biomedicine

In this paper, we introduce an AI-based automatic diagnosis framework, Agent-derived Multi-Specialist Consultation (AMSC), which mimics real-world medical consultations by using language models as virtual medical specialists. This approach outperforms previous methods by efficiently combining agents’ predictions on diseases, requiring less training and parameter updates.

In this paper, we propose a method to enhance in-silico molecule discovery in low-resource settings by using pseudo data generated by Large Language Models (LLMs), demonstrating its efficiency through improved performance, smaller model size, less data, and lower costs, with benefits increasing as more pseudo data is used.

In this report, we introduce the technical details of HuaTuo, a fine-tuned LLaMA Large Language Model, enhanced with generated QA pairs for improved performance in biomedical tasks. This approach equips HuaTuo with more accurate medical knowledge, addressing the expertise gap in standard LLMs.

In this paper, we introduce a method called knowledge-tuning, using structured medical knowledge bases to improve Large Language Models’ (LLMs) understanding and response accuracy in medical contexts, addressing the issue of hallucinated medical facts.