  The new era of artificial intelligence demands large-scale ultrafast hardware
for machine learning. Optical artificial neural networks process classical and
quantum information at the speed of light, and are compatible with silicon
technology, but lack scalability and need expensive manufacturing of many
computational layers. New paradigms, as reservoir computing and the extreme
learning machine, suggest that disordered and biological materials may realize
artificial neural networks with thousands of computational nodes trained only
at the input and at the readout. Here we employ biological complex systems,
i.e., living three-dimensional tumour brain models, and demonstrate a random
neural network (RNN) trained to detect tumour morphodynamics via image
transmission. The RNN, with the tumour spheroid as a three-dimensional deep
computational reservoir, performs programmed optical functions and detects
cancer morphodynamics from laser-induced hyperthermia inaccessible by optical
imaging. Moreover, the RNN quantifies the effect of chemotherapy inhibiting
tumour growth. We realize a non-invasive smart probe for cytotoxicity assay,
which is at least one order of magnitude more sensitive with respect to
conventional imaging. Our random and hybrid photonic/living system is a novel
artificial machine for computing and for the real-time investigation of tumour
dynamics.
