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Interest in artificial intelligence (AI) has grown exponentially in recent years, attracting sensational headlines and speculation. While there is considerable potential for AI to augment clinical practice, there remain numerous practical implications that must be considered when exploring AI solutions. These range from ethical concerns about algorithmic bias to legislative concerns in an uncertain regulatory environment. In the absence of established protocols and examples of best practice, there is a growing need for clear guidance both for innovators and early adopters. Broadly, there are three stages to the innovation process: invention, development and implementation. In this paper, we present key considerations for innovators at each stage and offer suggestions along the AI development pipeline, from bench to bedside.