A literature review of artificial intelligence (AI) for medical image segmentation: from AI and explainable AI to trustworthy AI

作者：Zixuan Teng, Lan Li, Ziqing Xin, Dehui Xiang, Jiang Huang, Hailing Zhou, Fei Shi, Weifang Zhu, Jing Cai, Tao Peng, Xinjian Chen · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2024 · DOI：10.21037/qims-24-723 · 被引用次数：61 · 研究领域：Artificial Intelligence in Healthcare and Education、Radiomics and Machine Learning in Medical Imaging、Medical Imaging and Analysis

Background and Objective: Medical image segmentation is a vital aspect of medical image processing, allowing healthcare professionals to conduct precise and comprehensive lesion analyses. Traditional segmentation methods are often labor intensive and influenced by the subjectivity of individual physicians. The advent of artificial intelligence (AI) has transformed this field by reducing the workload of physicians, and improving the accuracy and efficiency of disease diagnosis. However, conventional AI techniques are not without challenges. Issues such as inexplicability, uncontrollable decision-making processes, and unpredictability can lead to confusion and uncertainty in clinical decision-making. This review explores the evolution of AI in medical image segmentation, focusing on the development and impact of explainable AI (XAI) and trustworthy AI (TAI). Methods: This review synthesizes existing literature on traditional segmentation methods, AI-based approaches, and the transition from conventional AI to XAI and TAI. The review highlights the key principles and advancements in XAI that aim to address the shortcomings of conventional AI by enhancing transparency and interpretability. It further examines how TAI builds on XAI to improve the reliability, safety, and accountability of AI systems in medical image segmentation. Key Content and Findings: XAI has emerged as a solution to the limitations of conventional AI by providing greater transparency and interpretability, all...