# Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis

arXiv: https://arxiv.org/abs/2403.16303 · HTML: https://mlsanity.com/paper/2403.16303/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 125 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 25 of 25)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://mlsanity.com/paper/1907.12412/) (2019)
- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health](https://mlsanity.com/paper/2306.10070/) (2023)
- [Improving Chest X-Ray Report Generation by Leveraging Warm Starting](https://mlsanity.com/paper/2201.09405/) (2022)
- [Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine](https://mlsanity.com/paper/2308.06834/) (2023)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)
- [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023)
- [Towards Interpretable Mental Health Analysis with Large Language Models](https://mlsanity.com/paper/2304.03347/) (2023)
- [ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model](https://mlsanity.com/paper/2304.03086/) (2023)
- [Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting](https://mlsanity.com/paper/2310.07146/) (2023)
- [Biomedical named entity recognition using BERT in the machine reading comprehension framework](https://mlsanity.com/paper/2009.01560/) (2020)
- [Medical Image Captioning via Generative Pretrained Transformers](https://mlsanity.com/paper/2209.13983/) (2022)
- [MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation](https://mlsanity.com/paper/2310.14088/) (2023)
- [Identifying and Extracting Rare Disease Phenotypes with Large Language Models](https://mlsanity.com/paper/2306.12656/) (2023)
- [Zero-shot information extraction from radiological reports using ChatGPT](https://mlsanity.com/paper/2309.01398/) (2023)
- [Artificial Intelligence for Health Message Generation: Theory, Method, and an Empirical Study Using Prompt Engineering](https://mlsanity.com/paper/2212.07507/) (2022)
- [ChatGPT in education: A discourse analysis of worries and concerns on social media](https://mlsanity.com/paper/2305.02201/) (2023)
- [Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework](https://mlsanity.com/paper/2311.11055/) (2023)
- [Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures](https://mlsanity.com/paper/2303.08259/) (2023)
- [Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning](https://mlsanity.com/paper/2306.07170/) (2023)
- [How many preprints have actually been printed and why: a case study of computer science preprints on arXiv](https://mlsanity.com/paper/2308.01899/) (2023)

## Built on by (most cited first, 10 of 10)

- [MoralBench: Moral Evaluation of LLMs](https://mlsanity.com/paper/2406.04428/) (2024)
- [Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education](https://mlsanity.com/paper/2409.18924/) (2024)
- [Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge](https://mlsanity.com/paper/2502.13010/) (2025)
- [Small Agent Group is the Future of Digital Health](https://mlsanity.com/paper/2602.08013/) (2026)
- [DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services](https://mlsanity.com/paper/2510.21228/) (2025)
- [Cooking Up Risks: Benchmarking and Reducing Food Safety Risks in Large Language Models](https://mlsanity.com/paper/2604.01444/) (2026)
- [Large Language Models' Accuracy in Emulating Human Experts' Evaluation of Public Sentiments about Heated Tobacco Products on Social Media](https://mlsanity.com/paper/2502.01658/) (2025)
- [Robust or Suggestible? Exploring Non-Clinical Induction in LLM Drug-Safety Decisions](https://mlsanity.com/paper/2510.13931/) (2025)
- [Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking](https://mlsanity.com/paper/2606.26205/) (2026)
- [Model Retirement Creates Reproducibility Risk in Biomedical AI Publications](https://mlsanity.com/paper/2609.04699/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research. This study aims to provide a comprehensive overview of LLM applications in BHI, highlighting their transformative potential and addressing the associated ethical and practical challenges. We reviewed 1,698 research articles from January 2022 to December 2023, categorizing them by research themes and diagnostic categories. Additionally, we conducted network analysis to map scholarly collaborations and research dynamics. Our findings reveal a substantial increase in the potential applications of LLMs to a variety of BHI tasks, including clinical decision support, patient interaction, and medical document analysis. Notably, LLMs are expected to be instrumental in enhancing the accuracy of diagnostic tools and patient care protocols. The network analysis highlights dense and dynamically evolving collaborations across institutions, underscoring the interdisciplinary nature of LLM research in BHI. A significant trend was the application of LLMs in managing specific disease categories such as mental health and neurological disorders, demonstrating their potential to influence personalized medicine and public health strategies. LLMs hold promising potential to further transform biomedical research and healthcare delivery. While promising, the ethical implications and challenges of model validation call for rigorous scrutiny to optimize their benefits in clinical settings. This survey serves as a resource for stakeholders in healthcare, including researchers, clinicians, and policymakers, to understand the current state and future potential of LLMs in BHI.