Summary of How Reliable Ai Chatbots Are For Disease Prediction From Patient Complaints?, by Ayesha Siddika Nipu et al.
How Reliable AI Chatbots are for Disease Prediction from Patient Complaints?
by Ayesha Siddika Nipu, K M Sajjadul Islam, Praveen Madiraju
First submitted to arxiv on: 21 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 paper investigates the reliability of artificial intelligence (AI) chatbots leveraging Large Language Models (LLMs) in predicting diseases from patient complaints in emergency departments. Specifically, it evaluates GPT 4.0, Claude 3 Opus, and Gemini Ultra 1.0, as well as fine-tunes BERT for comparison. Results show varying accuracy among the chatbots, with GPT 4.0 performing well with increased few-shot data, while Gemini Ultra 1.0 excels with fewer examples. However, none of the chatbots are sufficiently reliable for critical medical decision-making, highlighting the need for rigorous validation and human oversight to ensure patient safety. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI chatbots that use Large Language Models (LLMs) could help doctors and nurses in emergency departments by predicting what’s wrong with patients based on their symptoms. The researchers tested three different AI systems, GPT 4.0, Claude 3 Opus, and Gemini Ultra 1.0, to see how well they did this task. They also compared these AI systems to a pre-trained language model called BERT. The results showed that each AI system was good at making predictions, but none of them were perfect. This means that doctors and nurses should still be involved in making decisions about patients’ treatment. |
Keywords
» Artificial intelligence » Bert » Claude » Few shot » Gemini » Gpt » Language model
