The medical technology industry welcomes the new European Commission's priority focus on Artificial Intelligence (AI). Our industry sees enormous potential of AI to make healthcare better and safer, improve access and outcomes, empower patients and citizens with information, and make healthcare delivery more efficient.
There are several challenges that impede the deployment of AI in healthcare. These include a fragmented data landscape that makes access to or sharing of data difficult, a lack of interoperability, and a shortage of incentives for data sharing. Legal, technical and social challenges present additional obstacles for the sharing and aggregation of data.
This study presents an overview of the development, adoption and use of Artificial Intelligence (AI) technologies and applications in the healthcare sector across all Member States. The main aim of this study was to support the European Commission in identifying and addressing any issues that might be hindering the wider adoption of AI technologies in the healthcare sector. The study will help the Commission to take action to achieve its long-term goal on the effective implementation of AI in the healthcare sector, based around common legislation and policy framework. 
Artificial Intelligence (AI) refers to the ability of algorithms encoded in technology to learn from data so that they can perform automated tasks without every step in the process having to be programmed explicitly by a human. WHO recognizes that AI holds great promise for the practice of public health and medicine. WHO also recognizes that, to fully reap the benefits of AI, ethical challenges for health care systems, practitioners and beneficiaries of medical and public health services must be addressed. Many of the ethical concerns described in this report predate the advent of AI, although AI itself presents a number of novel concerns. 
The Programme should be a means of promoting actions in areas where there is a Union added value that can be demonstrated. Such actions should include, inter alia, strengthening the exchange of best practices between Member States, supporting networks for the sharing of knowledge or for mutual learning, addressing cross-border threats to health so as to reduce the risks of such threats and to mitigate their consequences, addressing certain issues relating to the internal market in relation to which the Union can achieve Union-wide high-quality solutions, thereby unlocking the potential of innovation in health, and improving efficiency by avoiding the duplication of activities and optimising the use of financial resources. 
Artificial Intelligence (AI) is a fast evolving family of technologies that can bring a wide array of economic and societal benefits across the entire spectrum of industries and social activities. By improving prediction, optimising operations and resource allocation, and personalising service delivery, the use of artificial intelligence can support socially and environmentally beneficial outcomes and provide key competitive advantages to companies and the European economy. Such action is especially needed in high-impact sectors, including climate change, environment and health, the public sector, finance, mobility, home affairs and agriculture. However, the same elements and techniques that power the socio-economic benefits of 
Europe is a Union of and for citizens. What matters to Europeans matters to the EU. It should come as no surprise that regular surveys and debates across the continent consistently rank health among the top priorities for European citizens. They are right to expect a high level of protection and it is up to all of us - in all the European capitals - to deliver.
This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution. Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks.