This conceptual systems-architecture study presents a foresight-driven blueprint for an artificial intelligence (AI)–powered, immune-evasive nanobot designed for systemic cancer and viral eradication with integrated regenerative capabilities. The proposed platform combines biomimetic immune camouflage, an embedded convolutional neural network (CNN)–based diagnostic core, multimodal nanosensors, stimuli-responsive therapeutic release, regenerative payload deployment, and a theoretical autonomous energy module within a modular nanoscale framework. Unlike conventional nanocarriers that rely primarily on passive targeting mechanisms such as the enhanced permeability and retention (EPR) effect, this system is designed to perform real-time pathological sensing, AI-guided target verification, and adaptive therapeutic activation directly within the in vivo environment. The nanobot architecture integrates validated advances in immune-mimetic membrane cloaking, AI-assisted medical diagnostics, smart nanocarriers, and regenerative biology into a unified theoretical platform. A structured Technology Readiness Level (TRL) assessment and comparative systems analysis are provided to evaluate subsystem maturity and identify translational gaps. While significant technological barriers remain—particularly in nanoscale AI hardware fabrication, in vivo energy harvesting, and micro-integration stability—the model offers an interdisciplinary roadmap toward autonomous and regenerative precision nanomedicine. This study does not present experimental validation but instead proposes a strategic conceptual framework intended to guide future research, engineering development, and ethical regulatory discussions surrounding intelligent therapeutic nanodevices.
Gazy Ebrahim (Thu,) studied this question.
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