Summary of Rad-phi2: Instruction Tuning Phi-2 For Radiology, by Mercy Ranjit et al.
RAD-PHI2: Instruction Tuning PHI-2 for Radiology
by Mercy Ranjit, Gopinath Ganapathy, Shaury Srivastav, Tanuja Ganu, Srujana Oruganti
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This study explores the application of Small Language Models (SLMs) in the medical domain, specifically in radiology. The researchers investigate SLMs’ ability to answer questions related to symptoms, radiological appearances, differential diagnosis, prognosis, and treatment suggestions for various diseases affecting different organ systems. They fine-tune a 2.7 billion parameter SLM, Phi-2, using high-quality educational content from Radiopaedia. The resulting model, RadPhi-2-Base, demonstrates the capacity to address general radiology queries across various systems. Furthermore, they investigate Phi-2’s instruction tuning capabilities, enabling it to perform specific tasks. By fine-tuning Phi-2 on both general and radiology-specific tasks related to chest X-ray reports, they create Rad-Phi2. The study shows that Rad-Phi2 Base and Rad-Phi2 outperform larger models like Mistral-7B-Instruct-v0.2 and GPT-4 in providing concise and precise answers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well small language models can be used to help with radiology tasks, such as understanding symptoms and making diagnoses. The researchers trained a special model using lots of educational content about radiology from Radiopaedia. This model was able to answer general questions about different parts of the body, like the chest and heart. They also showed that this model could be fine-tuned for specific tasks related to reading X-ray reports. The results are promising, showing that these small language models can provide accurate and concise answers, which could help improve radiology practice. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Instruction tuning
