AI-NanoHybrid Resection (ANHR): A Conceptual Framework for Precision Hepatectomy in Hepatocellular Carcinoma

This conceptual paper proposes the AI-NanoHybrid Resection (ANHR) framework as a theoretical approach for precision hepatectomy in hepatocellular carcinoma (HCC), addressing the challenge of balancing tumor excision with liver parenchyma preservation. Drawing from existing literature and computational simulations, ANHR hypothesizes a closed-loop integration of artificial intelligence (AI) for real-time imaging analysis, nanotechnology for targeted tumor visualization and modulation, and robotics for minimally invasive execution under surgeon control. In this framework, nanoparticle-derived intraoperative signals are processed by AI to generate a real-time tumor margin probability map, which conceptually guides robotic resection while preserving human-in-the-loop decision making. Simulation-based and literature-informed models suggest illustrative potential for reduced resection of healthy tissue under idealized conditions, though these estimates are strictly theoretical. ANHR is intended as a hypothesis-generating framework to guide future preclinical and clinical research; no in vivo or human data are available at this stage.