Summary of Towards Knowledge-infused Automated Disease Diagnosis Assistant, by Mohit Tomar et al.
Towards Knowledge-Infused Automated Disease Diagnosis Assistant
by Mohit Tomar, Abhisek Tiwari, Sriparna Saha
First submitted to arxiv on: 18 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research aims to develop a diagnosis assistant that identifies diseases based on patient-doctor interaction, leveraging both symptomatology knowledge and diagnostic experience. The two-channel KI-DDI model combines transformer-based encoding of patient-doctor communication with graph attention network embedding of symptom-disease information, infused with deep neural networks for disease identification. The model is evaluated using an empathetic conversational medical corpus, demonstrating significant improvement over existing state-of-the-art models. The study highlights the importance of integrating visual sensory information to enhance diagnostic capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a diagnosis assistant that helps doctors identify diseases by analyzing patient-doctor conversations and medical knowledge. It’s like a superpower for doctors! They use special computers to understand what patients are saying and what symptoms they have, then match them with diseases. This is better than just asking patients questions because it takes into account the doctor’s experience and expertise. The research shows that this approach works really well and could make diagnosis more accurate and efficient. |
Keywords
* Artificial intelligence * Embedding * Graph attention network * Transformer
