Model-assisted designs is an emerging class of novel designs that utilize probability models for efficient decision making, similar to the model-based design, but its decision rules of dose escalation and de-escalation can be determined before the onset of the trial in a fashion similar to the algorithm-based designs. Examples of model-assisted designs include the modified toxicity probability interval (mTPI) design, Bayesian optimal interval (BOIN) design, and Keyboard design. Because of their simplicity and good performance, the model-assisted designs are increasingly used in practice. In this talk, I will present several model-assisted designs for phase I drug-combination trials and discuss their statistical properties, including the optimality of decision rules, coherence in dose transition, and convergence to the target dose. Easy-to-use and freely available software will be demonstrated for implementing these novel designs.