Clinical Insights: A Comprehensive Review of Language Models in Medicine

2024-08-21

arXiv.org

AI Summary

This paper systematically reviews the current state, challenges, and deployment pathways of large language models (LLMs) in healthcare. It addresses four core barriers to clinical adoption: poor deployability, high privacy risks, weak task-specific adaptation, and absence of standardized evaluation frameworks. To tackle these, the study proposes: (1) a hierarchical ethical framework tailored to healthcare, integrating data security, algorithmic fairness, and clinical accountability; (2) a structured, multidimensional taxonomy of clinical LLM tasks—encompassing text generation, information extraction, multimodal understanding, and conversational interaction; and (3) an integrated technical pathway combining localized inference, in-context learning, and multimodal modeling. The resulting comprehensive guide bridges theoretical rigor and practical implementation, offering a methodological foundation and actionable roadmap for developing trustworthy, evaluable, and deployable clinical AI systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

This paper explores the advancements and applications of language models in healthcare, focusing on their clinical use cases. It examines the evolution from early encoder-based systems requiring extensive fine-tuning to state-of-the-art large language and multimodal models capable of integrating text and visual data through in-context learning. The analysis emphasizes locally deployable models, which enhance data privacy and operational autonomy, and their applications in tasks such as text generation, classification, information extraction, and conversational systems. The paper also highlights a structured organization of tasks and a tiered ethical approach, providing a valuable resource for researchers and practitioners, while discussing key challenges related to ethics, evaluation, and implementation.

Problem

• Medical Language Models
• Ethical Challenges
• Privacy Protection

Innovation

• Medical Language Models
• Privacy-preserving Modeling
• Multi-modal Data Processing