The integration of artificial intelligence (AI) into medical imaging has shown a revolutionary advancement in healthcare, enhancing diagnostic accuracy, expediting disease identification, and optimizing treatment methodologies. This chapter explores the real-world implementation of AI solutions within clinical environments and in drug development programs, emphasizing practical implications and the journey toward certification and intellectual property (IP) protection. With strides in image acquisition and reporting techniques, AI has demonstrated its relevance in managing the increasing volume of imaging data and streamlining radiologists’ workflows via Clinical Decision Support Systems (CDSS). In addition, the integration of AI into drug development programs holds the promise of halving the duration, concurrently reducing drug discovery costs and increasing precision. When preparing an AI model for deployment in real-world scenarios, meticulous attention to every phase from data preparation to post-deployment model monitoring is essential, ensuring quality, fairness, and reproducibility. For the utilization of an AI solution as a medical device within clinical workflows, adherence to rigorous certification protocols mandated by regulatory bodies such as the CE mark in the European Union or the FDA in the United States is imperative. During the deployment of an AI solution in clinical settings, prioritizing interoperability and seamless integration into daily workflows is essential. Continuous model monitoring is important to ensure reliability and adaptability to evolving data trends and clinical methodologies. Ethical standards must be conscientiously considered, encompassing patient privacy safeguards and navigating the intricacies of intellectual property within the realm of AI innovations. Patents can also be leveraged to safeguard innovations in AI solutions for cancer imaging. Public-private partnerships, open access to AI solutions and resources, and training on AI are key to accelerate the development of robust AI solutions and to increase the trust and adoption of AI in clinical environments.