AI-Driven Holographic Counterpart Framework for Early Detection of Hepatocellular and Biliary Cancers in Consumer-Centric IoT-Based Diagnostic Tools

The convergence of holographic counterparts and the Internet of Things (IoT) is transforming consumer healthcare electronics into intelligent and immersive diagnostic systems. This paper presents a secure AI-driven holographic counterpart framework for early detection of hepatocellular and biliary cancers using multimodal consumer IoT data. The proposed system integrates biosignals, biochemical markers, and imaging data from wearable and home sensors within an edge and cloud pipeline that delivers real-time holographic visualizations of organ health. A hybrid deep learning architecture combining convolutional and transformer networks achieves an AUC of 0.97 and 94.2 percent diagnostic accuracy while maintaining end-to-end latency below one second. To ensure trust and privacy, a quantum-safe encryption stack using AES-256 with quantum key distribution and a blockchain integrity ledger protect data transmission and inference results. The system demonstrates scalability, interoperability, and energy efficiency with a 47 percent reduction in power consumption, confirming its suitability for consumer deployment. Beyond diagnostic accuracy, the holographic interface enhances engagement by transforming predictions into intuitive three-dimensional organ projections. By integrating AI analytics, holographic visualization, and quantum-resilient IoT security, this study establishes a foundation for the next generation of consumer-centric and explainable holographic IoT health systems.