# Dynamic Q&A of Clinical Documents with Large Language Models

arXiv: https://arxiv.org/abs/2401.10733 · HTML: https://mlsanity.com/paper/2401.10733/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data](https://mlsanity.com/paper/2304.08247/) (2023)
- [RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture](https://mlsanity.com/paper/2401.08406/) (2024)
- [ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation](https://mlsanity.com/paper/2303.08302/) (2023)
- [CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering](https://mlsanity.com/paper/2010.16021/) (2020)

## Built on by (most cited first, 4 of 4)

- [Evaluating Search Engines and Large Language Models for Answering Health Questions](https://mlsanity.com/paper/2407.12468/) (2024)
- [The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare](https://mlsanity.com/paper/2411.03287/) (2024)
- [EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records](https://mlsanity.com/paper/2601.10020/) (2026)
- [HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering](https://mlsanity.com/paper/2604.21027/) (2026)

## Abstract (arXiv)

Electronic health records (EHRs) house crucial patient data in clinical notes. As these notes grow in volume and complexity, manual extraction becomes challenging. This work introduces a natural language interface using large language models (LLMs) for dynamic question-answering on clinical notes. Our chatbot, powered by Langchain and transformer-based LLMs, allows users to query in natural language, receiving relevant answers from clinical notes. Experiments, utilizing various embedding models and advanced LLMs, show Wizard Vicuna's superior accuracy, albeit with high compute demands. Model optimization, including weight quantization, improves latency by approximately 48 times. Promising results indicate potential, yet challenges such as model hallucinations and limited diverse medical case evaluations remain. Addressing these gaps is crucial for unlocking the value in clinical notes and advancing AI-driven clinical decision-making.