Explainable Medical AI: The Gyan Approach
A more transparent alternative to black-box medical LLMs
This research introduces Gyan, an explainable language model architecture designed to address key limitations of traditional LLMs in medical applications.
- Evaluated on PubMedQA dataset for medical question answering
- Offers transparency and explainability unlike black-box medical LLMs
- Addresses critical issues of hallucination and interpretability in medical AI
- Provides a more maintainable alternative requiring less computational resources
For healthcare organizations, this research represents a potential breakthrough in deploying trustworthy AI systems that can support clinical decision-making while maintaining transparency and accountability.
On the Performance of an Explainable Language Model on PubMedQA