Acute myeloid leukemia (AML) represents a genetically heterogeneous malignancy, with mutations in the nucleophosmin-1 (NPM1) gene identified as the most prevalent and clinically significant molecular biomarkers. These mutations play a crucial pivotal role in the realms of diagnosis, prognosis, and therapeutic decision-making. Although an ideal measurable residual disease (MRD) test has yet to be developed, there is increasing acknowledgment of the significance of advanced molecular methodologies for monitoring MRD in NPM1-mutated (NPM1) AML. This underscores the necessity to customize strategies according to individual mutation profiles and clinical scenarios. Techniques such as quantitative PCR (qPCR), next-generation sequencing (NGS), and Droplet Digital PCR (ddPCR) are evaluated for their sensitivity and specificity in the detection of MRD. Concurrently, innovative approaches, including CRISPR-Cas9 and single-cell sequencing, are particularly instrumental in elucidating complex diseases like AML, where conventional methods frequently fall short in identifying clonal diversity and MRD. Furthermore, the incorporation of artificial intelligence (AI) is emphasized for its potential to enhance diagnostic accuracy, enhance prognostic modeling, and streamline personalized treatment planning. Despite its considerable potential, only a limited number of AI and machine learning (ML) tools have been fully integrated into clinical practice. This limited adoption is primarily due to challenges related to data quality, equity, the need for advanced infrastructure, and the establishment of robust evaluation metrics. While AI offers significant promise in the field of MRD in NPM1AML, its widespread use remains constrained by critical issues, including algorithmic bias, data integrity concerns, and the lack of regulatory frameworks and safety standards capable of keeping pace with rapid technological advancements. This review elucidates the dynamic landscape of MRD monitoring and rigorously assesses the challenges inherent in contemporary molecular techniques such as qPCR, in addition to interdisciplinary technologies-including single-cell sequencing, CRISPR-based methodologies, and AI-driven analyses-focusing on the implementation of these technologies and their implications for improving clinical decision-making in NPM1AML. mut mut mut
