用医学中的大型语言模型进行推理:对技术,挑战和临床整合的系统审查
Isra Mansoor1, Muhammad Abdullah1, Muhammad Dawood Rizwan1
1National University of Sciences and Technology (NUST), Sector H-12, Islamabad, 44000 Pakistan.
Health information science and systems
|December 1, 2025
概括
大型语言模型 (LLM) 在医疗保健中显示出复杂的医学推理的希望,但在准确性和偏见方面面临挑战. 未来的整合需要仔细评估和人类监督,以便在医学中安全,辅助的人工智能.
科学领域:
- 人工智能在医学中的应用
- 计算语言学 计算语言学
- 临床决策支持系统 临床决策支持系统
背景情况:
- 大型语言模型 (LLM) 展示了医学推理方面的高级能力,包括推理和模式识别.
- 应用范围包括诊断,临床决策支持,医学成像,药物发现和患者管理.
- 现有的研究审查了医疗保健中的LLM应用和评估方法.
研究的目的:
- 综合审查医学推理LLM应用程序的当前状态.
- 分析在临床环境中适应和评估LLMs的方法方法.
- 批判性地比较LLM的表现与传统系统和人类临床医生的表现.
主要方法:
- 对LLM适应和评估方法的系统分析.
- 对建筑适应,微调技术和评估协议进行比较分析.
- 审查特定的LLM (例如,GPT-4,Med-PaLM) 和适应方法 (例如,即时工程,少量学习).
主要成果:
- 在处理多式联运数据,产生假设和基于证据的建议方面,LLM显示出潜力.
- 性能因模型设计,培训和适应方法而异.
- 关键的挑战包括幻觉,偏见,问责制,道德和部署障碍.
结论:
- 在提高诊断准确性和决策方面,LLM提供了前景,但面临着重大障碍.
- 未来的方向包括混合神经符号模型,严格的评估和人-in-the-loop系统.
- 最好将LLM视为协作AI代理,而不是替代临床医生.
相关概念视频
01:15Methods of Documentation VI: Case Management Model
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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
For example, a patient with a chronic...
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01:27Language and Cognition
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
264
01:26Pharmacokinetic Models: Comparison and Selection Criterion
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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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01:21Mechanistic Models: Overview of Compartment Models
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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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