Large language models in medicine: the potentials and pitfalls

Abstract

Large language models (LLMs) have been applied to tasks in healthcare, ranging from medical exam questions to responding to patient questions. With increasing institutional partnerships between companies producing LLMs and healthcare systems, real world clinical application is coming closer to reality. As these models gain traction, it is essential for healthcare practitioners to understand what LLMs are, their development, their current and potential applications, and the associated pitfalls when utilized in medicine. This review and accompanying tutorial aim to give an overview of these topics to aid healthcare practitioners in understanding the rapidly changing landscape of LLMs as applied to medicine.

References in corpus (28)

• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• LLaMA: Open and Efficient Foundation Language Models
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• LoRA: Low-Rank Adaptation of Large Language Models
• On the Opportunities and Risks of Foundation Models
• Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
• A Survey of Large Language Models
• Scaling Instruction-Finetuned Language Models
• BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
• ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
• MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
• Solving Quantitative Reasoning Problems with Language Models
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
• Universal and Transferable Adversarial Attacks on Aligned Language Models
• Challenges and Applications of Large Language Models
• Holistic Evaluation of Language Models
• ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports
• Deep Bidirectional Language-Knowledge Graph Pretraining
• Can large language models reason about medical questions?
• PMC-LLaMA: Towards Building Open-source Language Models for Medicine
• Radiology-Llama2: Best-in-Class Large Language Model for Radiology
• Towards Generalist Biomedical AI
• Analyzing Leakage of Personally Identifiable Information in Language Models
• LinkBERT: Pretraining Language Models with Document Links
• shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned LLMs for Radiology Report Impression Generation
• Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?

Cited by in corpus (5)

• Benchmarking large language models for biomedical natural language processing applications and recommendations
• A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
• A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine
• Optimizing Large Language Models for ESG Activity Detection in Financial Texts
• Improving Transformer Performance for French Clinical Notes Classification Using Mixture of Experts on a Limited Dataset