NurseLLM: The First Specialized Language Model for Nursing
- URL: http://arxiv.org/abs/2510.07173v1
- Date: Wed, 08 Oct 2025 16:15:06 GMT
- Title: NurseLLM: The First Specialized Language Model for Nursing
- Authors: Md Tawkat Islam Khondaker, Julia Harrington, Shady Shehata,
- Abstract summary: We introduce NurseLLM, the first nursing-specialized LLM tailored for multiple choice question-answering (MCQ) tasks.<n>We build the first large scale nursing MCQ dataset to train LLMs on a broad spectrum of nursing topics.<n>Our experiments demonstrate that NurseLLM outperforms SoTA general-purpose and medical-specialized LLMs of comparable size.
- Score: 3.2696866605604185
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advancements in large language models (LLMs) have significantly transformed medical systems. However, their potential within specialized domains such as nursing remains largely underexplored. In this work, we introduce NurseLLM, the first nursing-specialized LLM tailored for multiple choice question-answering (MCQ) tasks. We develop a multi-stage data generation pipeline to build the first large scale nursing MCQ dataset to train LLMs on a broad spectrum of nursing topics. We further introduce multiple nursing benchmarks to enable rigorous evaluation. Our extensive experiments demonstrate that NurseLLM outperforms SoTA general-purpose and medical-specialized LLMs of comparable size on different benchmarks, underscoring the importance of a specialized LLM for the nursing domain. Finally, we explore the role of reasoning and multi-agent collaboration systems in nursing, highlighting their promise for future research and applications.
Related papers
- MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration [57.98393950821579]
We introduce the Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis (MAM)<n>Inspired by our empirical findings, MAM decomposes the medical diagnostic process into specialized roles: a General Practitioner, Specialist Team, Radiologist, Medical Assistant, and Director.<n>This modular and collaborative framework enables efficient knowledge updates and leverages existing medical LLMs and knowledge bases.
arXiv Detail & Related papers (2025-06-24T17:52:43Z) - Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning [57.873833577058]
We build a multimodal dataset enriched with extensive medical knowledge.<n>We then introduce our medical-specialized MLLM: Lingshu.<n>Lingshu undergoes multi-stage training to embed medical expertise and enhance its task-solving capabilities.
arXiv Detail & Related papers (2025-06-08T08:47:30Z) - A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations [5.265452667976959]
This survey systematically summarizes how to train medical LLMs based on open-source general LLMs.
It covers (a) how to acquire training corpus and construct customized medical training sets, (b) how to choose an appropriate training paradigm, and (d) existing challenges and promising research directions.
arXiv Detail & Related papers (2024-06-14T02:42:20Z) - Polaris: A Safety-focused LLM Constellation Architecture for Healthcare [17.074456639617996]
Polaris is the first safety-focused LLM constellation for real-time patient-AI healthcare conversations.
We train our models on proprietary data, clinical care plans, healthcare regulatory documents, medical manuals, and other medical reasoning documents.
We recruit over 1100 U.S. licensed nurses and over 130 U.S. licensed physicians to perform end-to-end conversational evaluations of our system.
arXiv Detail & Related papers (2024-03-20T05:34:03Z) - LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery [57.358568111574314]
Patient data privacy often restricts the availability of old data when updating the model.
Prior CL studies overlooked two vital problems in the surgical domain.
This paper proposes addressing these problems with a multimodal large language model (LLM) and an adaptive weight assignment methodology.
arXiv Detail & Related papers (2024-02-26T15:35:24Z) - BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains [8.448541067852]
Large Language Models (LLMs) have demonstrated remarkable versatility in recent years.
Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges.
We introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model.
arXiv Detail & Related papers (2024-02-15T23:39:04Z) - Large Language Model Distilling Medication Recommendation Model [58.94186280631342]
We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs)<n>Our research aims to transform existing medication recommendation methodologies using LLMs.<n>To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
arXiv Detail & Related papers (2024-02-05T08:25:22Z) - MedLM: Exploring Language Models for Medical Question Answering Systems [2.84801080855027]
Large Language Models (LLMs) with their advanced generative capabilities have shown promise in various NLP tasks.
This study aims to compare the performance of general and medical-specific distilled LMs for medical Q&A.
The findings will provide valuable insights into the suitability of different LMs for specific applications in the medical domain.
arXiv Detail & Related papers (2024-01-21T03:37:47Z) - Large language models in healthcare and medical domain: A review [4.456243157307507]
Large language models (LLMs) provide proficient responses to free-text queries.
This review explores the potential of LLMs to amplify the efficiency and effectiveness of diverse healthcare applications.
arXiv Detail & Related papers (2023-12-12T20:54:51Z) - ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences [51.66185471742271]
We propose ChiMed-GPT, a benchmark LLM designed explicitly for Chinese medical domain.
ChiMed-GPT undergoes a comprehensive training regime with pre-training, SFT, and RLHF.
We analyze possible biases through prompting ChiMed-GPT to perform attitude scales regarding discrimination of patients.
arXiv Detail & Related papers (2023-11-10T12:25:32Z) - A Survey of Large Language Models in Medicine: Progress, Application, and Challenge [85.09998659355038]
Large language models (LLMs) have received substantial attention due to their capabilities for understanding and generating human language.
This review aims to provide a detailed overview of the development and deployment of LLMs in medicine.
arXiv Detail & Related papers (2023-11-09T02:55:58Z) - LLMs-Healthcare : Current Applications and Challenges of Large Language
Models in various Medical Specialties [0.7673339435080445]
We aim to present a comprehensive overview of the latest advancements in utilizing Large Language Models (LLMs) within the healthcare sector.
LLMs have become pivotal in supporting healthcare, including physicians, healthcare providers, and patients.
We shed light on how LLMs are applied in cancer care, dermatology, dental care, neurodegenerative disorders, and mental health.
arXiv Detail & Related papers (2023-10-28T01:01:30Z) - MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data [37.60056509129154]
Large language models (LLMs) hold considerable promise for improving medical, diagnostics, patient care, and education.<n>Yet, there is an urgent need for open-source models that can be deployed on-premises to safeguard patient privacy.<n>We present an innovative dataset consisting of over 160,000 entries, specifically crafted to fine-tune LLMs for effective medical applications.
arXiv Detail & Related papers (2023-04-14T11:28:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.