人工智慧應用於重症肌無力

Applications of Artificial Intelligence in Myasthenia Gravis

重症肌無力（myasthenia gravis，MG）是一種神經肌肉交接處的自體免疫疾病，其特徵為骨骼肌無力或疲勞，涵蓋從眼睛至全身多處肌肉無力。由於臨床表現高度異質性，疾病的診斷與預後評估，需仰賴專科醫師經驗與臨床判斷。隨著人工智慧技術的發展，尤其是機器學習與深度學習方法，對於重症肌無力照護迎來新的契機。人工智慧在重症肌無力的應用已涵蓋多個面向，包括診斷輔助、預後風險分層、急性惡化預測、住院天數與醫療資源耗用分析、生物標記物挖掘，以及遠距健康監測與病人自主管理。診斷方面，研究已利用影像辨識臉部分析與眼部結構分割技術、光譜分析、聲音辨識與腸道微生物組建構分類模型，協助提升早期診斷的準確性。在預後風險評估上，監督式機器學習模型可整合臨床指標、實驗室數據與治療歷程，有效預測病程轉變、呼吸衰竭風險與醫療成本。近年來，可解釋性人工智慧模型更被導入重症肌無力領域，提升模型透明度與臨床可接受性。重症肌無力作為低盛行率疾病，人工智慧應用仍面臨若干挑戰。資料來源的限制、多中心資料整合困難、標準化不足與資料隱私議題，皆限制模型的泛化能力與臨床推廣潛力。人工智慧在重症肌無力領域的發展，應朝向多中心資料共享、前瞻性試驗設計、可解釋性演算法開發與跨領域平台整合。透過結合醫療專業、資料科學與使用者導向設計，可望推動人工智慧工具，在重症肌無力病人個人化照護與智慧決策支援中的實際落地，實現以病人為中心之精準醫療。

Myasthenia Gravis (MG) is a chronic autoimmune disorder affecting the neuromuscular junction, characterized by fluctuating skeletal muscle weakness and encompassing a broad spectrum of subtypes ranging from ocular to generalized forms. Due to its pronounced clinical heterogeneity, the diagnosis and prognosis of MG heavily rely on the expertise and clinical judgment of specialists. The advent of artificial intelligence (AI)—particularly machine learning (ML) and deep learning (DL) techniques—has introduced novel opportunities in the management of MG. Recent AI applications in MG span various domains, including diagnostic support, prognostic risk stratification, prediction of acute exacerbations, estimation of hospital stay and healthcare resource utilization, biomarker discovery, as well as remote monitoring and patient self-management. In the realm of diagnosis, studies have employed image recognition technologies (e.g., facial analysis and ocular structure segmentation), spectroscopic analysis, voice recognition, and gut microbiome-based classification models to enhance early diagnostic accuracy. For prognostic evaluation, supervised ML models can integrate clinical indicators, laboratory data, and therapeutic histories to effectively predict disease progression, respiratory failure risk, and economic burden. More recently, interpretable AI models have been introduced to improve model transparency and clinical acceptability. Nevertheless, MG presents specific challenges for AI applications. Limitations in data availability (e.g., small sample sizes, feature heterogeneity), difficulties in multi-center data harmonization, lack of standardization, and data privacy concerns all hinder model generalizability and clinical translation. Furthermore, real-world implementation of remote monitoring faces obstacles such as variable image quality, inconsistent audio input, and poor patient adherence. Looking ahead, the future development of AI in MG should prioritize multi-center data sharing, prospective study design, interpretable algorithm development, and cross-disciplinary platform integration. Through the synergy of medical expertise, data science, and user-centered design, AI tools hold promises to advance personalized care and intelligent decision support for MG patients, realizing the vision of patient-centered precision medicine.