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Introduction to Language of Pathophysiology l

Introduction to Language of Pathophysiology l

Pathophysiology investigates how biological mechanisms—typically starting at the cellular level—disrupt normal bodily functions. It bridges anatomy and physiology to explain the progression of disease. With this foundation, it is important to understand the following key terms used to describe disease processes: Diagnosis:The process of identifying a disease using clinical evaluation, including signs (objective evidence like rashes), symptoms (subjective experiences like pain), laboratory test...

Introduction to Language of Pathophysiology ll

Introduction to Language of Pathophysiology ll

This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...

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Updated: Jul 9, 2026

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Published on: December 1, 2023

整形外科における大型言語モデルに関する体系的レビュー

Kevin Mo¹, Rowen Lin², Evan Dunn¹

¹Orthopaedic Surgery, Valley Hospital Medical Center, 620 Shadow Ln, Las Vegas, NV 89106, USA.

Journal of clinical medicine

|August 28, 2025

まとめ

大型言語モデル (LLM) は整形外科での可能性を示していますが,現在のAIの評価の精度は整形外科のリジデントに遅れています. 臨床応用にはさらなる開発が必要である.

キーワード:

チャットGPT 大型言語モデル整形外科体系的なレビュー

さらに関連する動画

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

関連する実験動画

Last Updated: Jul 9, 2026

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Published on: December 1, 2023

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

科学分野:

整形外科
人工知能
医学教育

背景:

2022年以降の大型言語モデル (LLM) の急速な発展は,整形外科における新たな機会を提示しています.
この体系的レビューは,この分野におけるLLMの現在の研究状況を初めて検討したものです.

研究の目的:

骨科で研究されたLLMを特定するために.
その機能を評価し,結果の質を評価する.
整形外科のレジデントと比較するために

主な方法:

PubMed,Embase,Cochrane Libraryを使って行った体系的なレビュー.
体系的なレビューとメタ分析 (PRISMA) のガイドラインの優先報告項目の遵守
ChatGPT,Bard,Perplexity AI,BingなどのLLMを評価した60件の研究が含まれています.

主要な成果:

ChatGPT4. 0は,ChatGPT3. 5 (29. 4から55. 8%) よりも高い精度を示しました.
Bardは49.8%から58%の精度を達成し,画像ベースの評価はすべてのLLMの性能を低下させた.
整形外科のレジデントは 74.2%から75.3%の得点で LLM を一貫して上回りました

結論:

骨科評価の精度において,ChatGPT 4.0はChatGPT 3.5に比べて有意に改善されました.
整形外科のレジデントは現在のLLMよりも高い得点でした
骨科評価,画像分析,臨床ドキュメントにおけるLLMのパフォーマンスを向上させるための実質的な機会が存在します.