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1,140 claims from 718 papers are on the record. 39 have been checked so far; the other 1,101 have no check with a result yet.

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Keyword: medical question answering ×Clear all

7 claims from 3 papers

1. Computer Science › Machine Learning in Healthcare

   A large language model for electronic health records

   Yang, Chen, PourNejatian et al. · npj Digital Medicine · 2022

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   1. Unchecked“GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthca…

2. Medicine › Artificial Intelligence in Healthcare and Education

   Large Language Models Encode Clinical Knowledge

   Singhal, Azizi, Tao et al. · arXiv (Cornell University) · 2022

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   1. Unchecked“Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), sur…
   2. Unchecked“The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians.”
   3. Unchecked“We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine.”

3. Medicine › Artificial Intelligence in Healthcare and Education

   Can large language models reason about medical questions?

   Liévin, Hother, Geert and Ole · arXiv (Cornell University) · 2022

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   1. Unchecked“Based on an expert annotation of the generated CoTs, we found that InstructGPT can often read, reason and recall expert knowledge.”
   2. Unchecked“Last, by leveraging advances in prompt engineering (few-shot and ensemble methods), we demonstrated that GPT-3.5 not only yields calibrated predictive distributions, but also reaches the passing score on three datasets: MedQA-USMLE 60.2%, MedMCQA 62.7% and P…
   3. Unchecked“Open-source models are closing the gap: Llama-2 70B also passed the MedQA-USMLE with 62.5% accuracy.”