QM-ToT (Quantized Medical Tree of Thought) is a novel path-based reasoning framework that addresses key limitations of Large Language Models in specialized medical contexts when deployed in resource-constrained environments.

Key Innovations:

• Designed specifically for quantized models in medical applications where model size reduction often compromises performance
• Implements a structured reasoning approach that better handles complex medical terminology and clinical insights
• Evaluated using the MedQA-USMLE dataset, a rigorous medical examination benchmark
• Mitigates performance degradation common in quantized models while maintaining medical reasoning accuracy

Business Impact: This research enables more effective deployment of AI in healthcare settings with limited computational resources, potentially expanding access to advanced medical AI tools while maintaining diagnostic quality and clinical reliability.

QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model