Research and Analysis of Explainable Artificial Intelligence in the Medical Field

With the rapid penetration of Artificial Intelligence (AI) in the medical field, the opaque decision-making caused by the “black box” nature of algorithms has become a core bottleneck restricting the clinical implementation of AI. Explainable Artificial Intelligence (XAI), by providing decision-making bases that conform to clinical logic, has emerged as a key technical direction for building doctor-patient trust and meeting regulatory compliance requirements. This paper systematically reviews the research achievements of XAI in medical scenarios over the past five years, conducting in-depth analysis from three core dimensions: technical framework, clinical application, and existing challenges. It focuses on summarizing the technical characteristics and clinical scenario adaptability of mainstream XAI solutions such as white-box models and post-processing explanations, and verifies the core value of XAI in improving diagnostic efficiency, reducing medical errors, and ensuring the safety of diagnosis and treatment through real clinical cases. Meanwhile, it discusses issues encountered during the implementation of XAI, such as the balance between interpretability and model performance, and adaptation to clinical standardization, and proposes a practical path of “doctor-XAI collaborative decision-making”. This study aims to provide theoretical references and practical guidance for the standardized implementation of XAI technology in the medical field.