Summary of Can Public Llms Be Used For Self-diagnosis Of Medical Conditions ?, by Nikil Sharan Prabahar Balasubramanian et al.
Can Public LLMs be used for Self-Diagnosis of Medical Conditions ?
by Nikil Sharan Prabahar Balasubramanian, Sagnik Dakshit
First submitted to arxiv on: 18 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 investigates the potential and limitations of publicly available Large Language Models (LLMs) in the task of self-diagnosis of medical conditions based on bias-validating symptoms. The authors prepare a prompt engineered dataset of 10,000 samples to test the performance of GPT-4.0 and Gemini models on this task. They find contrasting accuracies of 63.07% for GPT-4.0 and 6.01% for Gemini, highlighting the challenges and limitations of these models in self-diagnosis. The study also explores the potential for Retrieval Augmented Generation to improve performance in this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well public language models can help people diagnose medical conditions on their own using search engines. Right now, many people use large language models like GPT-4.0 and Gemini to try to figure out what’s wrong with them based on symptoms they look up online. The authors wanted to see how accurate these models are in making diagnoses and what challenges they face. They found that one model did a lot better than the other, but neither was very good at getting it right. They also talked about ways that future research could make language models even better at helping people diagnose medical conditions. |
Keywords
» Artificial intelligence » Gemini » Gpt » Prompt » Retrieval augmented generation
