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Abstract Chronic wounds in diabetic patients are challenging because their prolonged inflammation makes healing difficult, thus burdening patients, society, and health care systems. Customized dressing materials are needed to effectively treat such wounds that vary in shape and depth. The continuous development of 3D‐printing technology along with artificial intelligence has increased the precision, versatility, and compatibility of various materials, thus providing the considerable potential to meet the abovementioned needs. Herein, functional 3D‐printing inks comprising DNA from salmon sperm and DNA‐induced biosilica inspired by marine sponges, are developed for the machine learning‐based 3D‐printing of wound dressings. The DNA and biomineralized silica are incorporated into hydrogel inks in a fast, facile manner. The 3D‐printed wound dressing thus generates provided appropriate porosity, characterized by effective exudate and blood absorption at wound sites, and mechanical tunability indicated by good shape fidelity and printability during optimized 3D printing. Moreover, the DNA and biomineralized silica act as nanotherapeutics, enhancing the biological activity of the dressings in terms of reactive oxygen species scavenging, angiogenesis, and anti‐inflammation activity, thereby accelerating acute and diabetic wound healing. These bioinspired 3D‐printed hydrogels produce using a DNA‐induced biomineralization strategy are an excellent functional platform for clinical applications in acute and chronic wound repair.

An artificial synapse is an essential element to construct a hardware‐based artificial neural network (ANN). While various synaptic devices have been proposed along with studies on electrical characteristics and proper applications, a small number of conductance states with nonlinear and asymmetric conductance changes have been problematic and imposed limits on computational performance. Their applications are thus still limited to the classification of simple images or acoustic datasets. Herein, a polymer electrolyte‐gated synaptic transistor (pEGST) is demonstrated for video‐based learning and inference using transfer learning. In particular, abnormal car detection (ACD) is attempted with video‐based learning and inference to avoid traffic accidents. The pEGST showed multiple states of 8,192 (=13 bits) for weight modulation with linear and symmetric conductance changes and helped reduce the error rate to 3% to judge whether a car in a video is abnormal.