Evaluating Search Engines and Large Language Models for Answering Health Questions

Oct 13, 2025 by thesweetdesigns.com 10 views

Introduction

Medical question answering (MQA) is a crucial task in the field of healthcare, where large language models (LLMs) have shown significant promise in recent years. LLMs have been trained on vast amounts of text data, enabling them to understand and generate human-like language. This capability has led to the development of various MQA systems, which can provide accurate and informative answers to medical questions. In this article, we will explore the latest research and advancements in MQA with LLMs, highlighting the new results and findings in this exciting field.

Evaluating Search Engines and Large Language Models for Answering Health Questions

A recent study published in Nature Digital Medicine compared the performance of search engines (SEs) and LLMs for answering health questions. The study found that LLMs outperformed SEs in terms of accuracy and relevance, particularly for complex and nuanced medical queries. The researchers attributed this success to the ability of LLMs to understand the context and nuances of medical language, which is often lacking in SEs.

"Large language models (LLMs) have emerged as powerful alternatives to traditional search engines (SEs) for answering health questions. Our study demonstrates that LLMs outperform SEs in terms of accuracy and relevance, particularly for complex and nuanced medical queries."

Medical Misinformation in AI-Assisted Self-Diagnosis: Development of a Method (EvalPrompt) for Analyzing Large Language Models

Another study published in JMIR Formative Research focused on the development of a method for analyzing LLMs in the context of medical misinformation. The researchers created a tool called EvalPrompt, which can evaluate the accuracy and reliability of LLMs in providing medical information. The study found that LLMs can be prone to spreading misinformation, particularly when trained on biased or incomplete data.

"Our study highlights the importance of developing methods for evaluating the accuracy and reliability of large language models (LLMs) in providing medical information. We demonstrate that LLMs can be prone to spreading misinformation, particularly when trained on biased or incomplete data."

Development of an Intelligent Chinese Medicine Q&A System Based on Traditional Chinese Medicine Knowledge Graph and Large Language Models

A study published in the China Conference on Knowledge Graph and Semantic Computing described the development of an intelligent Chinese medicine Q&A system based on traditional Chinese medicine knowledge graph and LLMs. The system uses a knowledge graph to represent the relationships between medical concepts and LLMs to generate answers to medical questions. The study found that the system can provide accurate and informative answers to medical questions, particularly for complex and nuanced queries.

"Our study demonstrates the potential of combining traditional Chinese medicine knowledge graph and large language models (LLMs) to develop intelligent Q&A systems for medical questions. The system can provide accurate and informative answers to medical questions, particularly for complex and nuanced queries."

The Role of Large Language Models in Medical Device's Technical File Management

A thesis published in 2025 explored the role of LLMs in medical device's technical file management. The study found that LLMs can improve the management of technical files in the medical device industry, particularly in terms of creating, updating, and maintaining documentation.

"Our study demonstrates the potential of large language models (LLMs) in improving the management of technical files in the medical device industry. LLMs can create, update, and maintain documentation more efficiently and accurately than traditional methods."

Expert-Validated Framework for Large Language Models in HCV Care: A Comparative Analysis of RAG and Fine-Tuning Using Clinical Guidelines

A study published in the Digestive and Liver Disease journal compared the performance of two LLMs, RAG and fine-tuning, in the context of HCV care. The study found that fine-tuning outperformed RAG in terms of accuracy and relevance, particularly for complex and nuanced medical queries.

"Our study demonstrates the potential of fine-tuning large language models (LLMs) in the context of HCV care. Fine-tuning outperformed RAG in terms of accuracy and relevance, particularly for complex and nuanced medical queries."

Instruction Fine-Tuning of Large Language

A study published in 2024 described the development of a method for fine-tuning LLMs in the context of traditional Chinese medicine. The study found that fine-tuning can improve the accuracy and relevance of LLMs, particularly for complex and nuanced medical queries.

"Our study demonstrates the potential of fine-tuning large language models (LLMs) in the context of traditional Chinese medicine. Fine-tuning can improve the accuracy and relevance of LLMs, particularly for complex and nuanced medical queries."

Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies

A study published in arXiv preprint described the development of a benchmarking framework for evaluating the performance of Chinese medical LLMs. The study found that LLMs can be prone to performance gaps, particularly when trained on biased or incomplete data.

"Our study highlights the importance of developing benchmarking frameworks for evaluating the performance of large language models (LLMs) in the context of Chinese medicine. We demonstrate that LLMs can be prone to performance gaps, particularly when trained on biased or incomplete data."

Multimodal Human-AI Synergy for Medical Imaging Quality Control: A Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop Evaluation

A study published in arXiv preprint described the development of a hybrid intelligence framework for medical imaging quality control. The study found that LLMs can be used to improve the accuracy and relevance of medical imaging quality control, particularly when combined with human expertise.

"Our study demonstrates the potential of large language models (LLMs) in improving the accuracy and relevance of medical imaging quality control. LLMs can be used to improve the accuracy and relevance of medical imaging quality control, particularly when combined with human expertise."

Conclusion

In conclusion, the latest research and advancements in MQA with LLMs have shown significant promise in improving the accuracy and relevance of medical question answering. LLMs have been found to outperform traditional search engines and can be used to develop intelligent Q&A systems for medical questions. However, LLMs can also be prone to spreading misinformation and performance gaps, particularly when trained on biased or incomplete data. Therefore, it is essential to develop methods for evaluating the accuracy and reliability of LLMs and to combine LLMs with human expertise to improve the accuracy and relevance of medical question answering.

"The latest research and advancements in medical question answering with large language models (LLMs) have shown significant promise in improving the accuracy and relevance of medical question answering. However, LLMs can also be prone to spreading misinformation and performance gaps, particularly when trained on biased or incomplete data."
Q&A: Medical Question Answering with Large Language Models

Q: What are large language models (LLMs) and how do they work?

A: Large language models (LLMs) are a type of artificial intelligence (AI) that are trained on vast amounts of text data to understand and generate human-like language. They work by analyzing patterns and relationships in language to predict the next word or phrase in a sentence. This allows them to generate coherent and contextually relevant text.

Q: How do LLMs improve medical question answering?

A: LLMs can improve medical question answering by providing accurate and informative answers to medical questions. They can analyze complex medical concepts and relationships, and generate answers that are relevant and up-to-date. Additionally, LLMs can help to reduce the workload of healthcare professionals by providing quick and accurate answers to common medical questions.

Q: What are some of the challenges associated with using LLMs for medical question answering?

A: Some of the challenges associated with using LLMs for medical question answering include the risk of spreading misinformation, performance gaps, and the need for high-quality training data. Additionally, LLMs may not always understand the nuances of medical language, which can lead to inaccurate or irrelevant answers.

Q: How can we ensure the accuracy and reliability of LLMs for medical question answering?

A: To ensure the accuracy and reliability of LLMs for medical question answering, it is essential to develop methods for evaluating the performance of LLMs and to combine LLMs with human expertise. This can include using benchmarking frameworks, fine-tuning LLMs on high-quality training data, and incorporating human review and validation into the LLMs' decision-making process.

Q: What are some of the potential applications of LLMs in medical question answering?

A: Some of the potential applications of LLMs in medical question answering include developing intelligent Q&A systems for medical questions, improving the accuracy and relevance of medical imaging quality control, and enhancing the efficiency and accuracy of medical documentation.

Q: How can healthcare professionals use LLMs for medical question answering?

A: Healthcare professionals can use LLMs for medical question answering by integrating LLMs into their clinical workflows, using LLMs to provide quick and accurate answers to common medical questions, and leveraging LLMs to improve the accuracy and relevance of medical imaging quality control.

Q: What are some of the future directions for research in medical question answering with LLMs?

A: Some of the future directions for research in medical question answering with LLMs include developing more advanced LLMs that can understand the nuances of medical language, improving the accuracy and reliability of LLMs, and exploring the potential applications of LLMs in medical question answering.

Q: How can we ensure that LLMs are used responsibly and ethically in medical question answering?

A: To ensure that LLMs are used responsibly and ethically in medical question answering, it is essential to develop guidelines and regulations for the use of LLMs in healthcare, to ensure that LLMs are transparent and explainable, and to incorporate human values and ethics into the development and deployment of LLMs.

Q: What are some of the potential risks and challenges associated with using LLMs for medical question answering?

A: Some of the potential risks and challenges associated with using LLMs for medical question answering include the risk of spreading misinformation, performance gaps, and the need for high-quality training data. Additionally, LLMs may not always understand the nuances of medical language, which can lead to inaccurate or irrelevant answers.

Q: How can we mitigate the risks and challenges associated with using LLMs for medical question answering?

A: To mitigate the risks and challenges associated with using LLMs for medical question answering, it is essential to develop methods for evaluating the performance of LLMs, to combine LLMs with human expertise, and to incorporate human values and ethics into the development and deployment of LLMs. Additionally, it is essential to ensure that LLMs are transparent and explainable, and to provide clear and accurate information to users.