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Civil infrastructure inspection -and consequently maintenance- is carried out primarily through visual inspections. AI-enhanced (Artificial Intelligence) digital inspection methods, integrated with risk-based probabilistic approaches, have been promoted to keep existing structures, especially infrastructures, safe and predictable. Drones are used to obtain a significant number of images to cover the surface of a bridge, which are further integrated into a digital 3D (three-dimensional) model. According to the IFC standards (Industry Foundation Class), this 3D model is GPS-positioned (Global Positioning System) and connected to BIM (Building Information Modelling). Post-processing the accumulated data volume of all digital images is very time-consuming. For this reason, appropriate AI-based algorithms streamline this process significantly, enabling partially automated damage detection and assessment. To this end, images of various types of damage on different bridges are used to train and test the AI-enhanced models. In addition, damage identification and classification are developed. Six visually detectable defects can be identified, and theoretical models estimate the associated structural diseases. Finally, a probability-based risk assessment presents the basis for defining the criticality of the structure. With the help of digital images, it is possible to create a high-fidelity digital model and quantitative surface and spatial data records of the structural health condition of bridges and other infrastructures.