Summary of Prompting Large Language Models For Supporting the Differential Diagnosis Of Anemia, by Elisa Castagnari (heka) et al.
Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia
by Elisa Castagnari, Lillian Muyama, Adrien Coulet
First submitted to arxiv on: 20 Sep 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 The proposed study aims to develop artificial intelligence-powered diagnostic pathways for clinicians, inspired by existing clinical guidelines. The research leverages Large Language Models (LLMs) such as Generative Pretrained Transformer 4 (GPT-4), Large Language Model Meta AI (LLaMA), and Mistral to diagnose anemia and its subtypes from synthetic yet realistic patient data. By using advanced prompting techniques, the models generate diagnostic pathways that can potentially replace traditional clinical guidelines. The study finds that LLMs have huge potential in clinical pathway discovery, with GPT-4 exhibiting the best performance across all experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to help doctors diagnose patients using artificial intelligence. They used special computer programs called Large Language Models (LLMs) to make decisions just like doctors do. The LLMs looked at fake but realistic patient data and figured out how to tell if someone had anemia or one of its subtypes. This could be a new way to help doctors get accurate diagnoses faster. |
Keywords
» Artificial intelligence » Gpt » Large language model » Llama » Prompting » Transformer
