Citations

Artificial intelligence (AI) is transforming gastrointestinal (GI) endoscopy into a more standardized, data-driven, and workflow-integrated field. Advances in computer-assisted detection (CADe), diagnosis (CADx), quality assessment (CAQ), natural language processing (NLP), and multimodal deep learning have expanded AI applications across colonoscopy, upper endoscopy, endoscopic ultrasound (EUS), ERCP, cholangioscopy, and capsule endoscopy. These systems have demonstrated improvements in lesion detection, procedural quality assessment, workflow efficiency, and diagnostic support. However, current evidence remains largely focused on surrogate outcomes rather than patient-centered clinical benefits, while challenges related to generalizability, explainability, regulatory oversight, automation bias, and workflow integration continue to limit widespread adoption. Future progress will depend on prospective real-world validation, diverse datasets, explainable AI frameworks, and careful integration of human–AI interaction into clinical practice. Overall, AI is evolving from a supportive adjunct into an increasingly integrated component of gastrointestinal endoscopy with the potential to improve procedural quality, diagnostic consistency, and clinical efficiency.

Acute myeloid leukemia (AML) pathogenesis is deeply rooted in the concept of clonal evolution, in which hematopoietic stem cells experience an initial genetic insult, followed by the sequential acquisition of mutations leading to various disease phenotypes. Through natural selection, a malignant clone can diversify and propagate with time. Aside from clonal evolution, a number of time-honored principles of molecular oncology account for the initiation and maintenance of AML. These principles include loss of heterozygosity, genomic instability, epigenomic disruption, deregulation of signaling pathways, anti-apoptotic mechanisms, and oncogene addiction. Such tenets of cancer biology not only explain AML cellular behavior but also may inform target validation in AML. Herein, we highlight the current state of targeted therapeutics, including the 14 novel agents (beyond 7+3 chemotherapy) approved by the Food and Drug Administration, in the context of these tenets and discuss future prospects by leveraging current knowledge of AML genomics. Such therapeutic concepts, including synthetic lethality, are especially relevant to TP53-mutant AML, as there are no unique therapies that have received regulatory approval for this subset to date. We emphasize the emerging role of single-cell genomics and multi-omics toward better understanding of AML biology and development of future successful AML therapies.