Speakers

Artificial intelligence has the capacity to accelerate the interpretation of medical images and support diagnostic decision-making. Delivering these benefits directly within clinical imaging systems, however, requires compact computing hardware capable of executing both generative and discriminative operations with low latency and high energy efficiency. Generative adversarial networks, which combine image synthesis with classification, offer particular promise for improving diagnostic accuracy and augmenting limited datasets, but their implementation in hardware remains challenging owing to competing requirements for rapid weight updates and long-term retention stability. Here we report a reconfigurable neuromorphic platform based on ferroelectric field-effect transistors that enables dual-mode operation via dynamic switching between ferroelectric polarization and charge-trapping mechanisms. This architecture supports both high-speed, energy-efficient synaptic programming and robust non-volatile retention within the same device. Implemented in a 6 × 6 analogue crossbar, the system performs convolution and deconvolution operations representative of generative-adversarial-network workflows, enabling realistic synthesis of mammographic lesions. This approach establishes a compact hardware foundation for embedded diagnostic intelligence in next-generation imaging instruments.