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Objective:  The aim was to develop a new curriculum for radiology in medical studies, to reach a national consensus and to integrate it into the new national competence-based learning objectives catalog (NKLM 2.0). In this statement of the German Radiological Society (DRG), the process of curriculum development is described and the new curriculum is presented together with suggestions for practical implementation., Materials and Methods:  The DRG has developed a new curriculum for radiology. This was coordinated nationally among faculty via an online survey and the result was incorporated into the NKLM 2.0. Furthermore, possibilities for the practical implementation of the competency-based content are shown and different teaching concepts are presented., Results:  The developed curriculum is competency-based and aims to provide students with important skills and abilities for their future medical practice. The general part of the curriculum is divided into the topics "Radiation Protection", "Radiological Methods" and radiologically-relevant "Digital Skills". Furthermore, there is a special part on the individual organ systems and the specific diseases. In order to implement this in a resource-saving way, new innovative teaching concepts are needed that combine the advantages of face-to-face teaching in small groups for practical and case-based learning with digital teaching offers for resource-saving teaching of theoretical content., Conclusion:  We have created a uniform radiology curriculum for medical studies in Germany, coordinated it nationally and integrated it into the NKLM 2.0. The curriculum forms the basis of a uniform mandatory radiology teaching and should be the basis for the individual curriculum development of each faculty and strengthen the position of radiology in the interdisciplinary context., Key Points:   · A radiology curriculum for undergraduate medical education was developed.. · The curriculum was brought into agreement among the faculties in Germany and integrated into the NKLM 2.0.. · This curriculum is intended to be the basis for curriculum development and to strengthen the position of radiology.. · In order to implement the competence-based teaching, new innovative teaching concepts are necessary.., Citation Format: · Dettmer S, Barkhausen J, Volmer E et al. White Paper: Radiology Curriculum for Undergraduate Medical Education in Germany and Integration into the NKLM 2.0. Fortschr Röntgenstr 2021; 193: 1294 - 1303., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement regarding the use of machine learning (ML) in cardiovascular imaging. The purpose of this statement is to provide guidance on requirements for successful development and implementation of ML applications in cardiovascular imaging. In particular, recommendations on how to adequately design ML studies and how to report and interpret their results are provided. Finally, we identify opportunities and challenges ahead. While the focus of this position statement is ML development in cardiovascular imaging, most considerations are relevant to ML in radiology in general. KEY POINTS: • Development and clinical implementation of machine learning in cardiovascular imaging is a multidisciplinary pursuit. • Based on existing study quality standard frameworks such as SPIRIT and STARD, we propose a list of quality criteria for ML studies in radiology. • The cardiovascular imaging research community should strive for the compilation of multicenter datasets for the development, evaluation, and benchmarking of ML algorithms.

Artificial Intelligence in Radiology - Definition, Potential and Challenges Abstract. Artificial Intelligence (AI) is omnipresent. It has neatly permeated our daily life, even if we are not always fully aware of its ubiquitous presence. The healthcare sector in particular is experiencing a revolution which will change our daily routine considerably in the near future. Due to its advanced digitization and its historical technical affinity radiology is especially prone to these developments. But what exactly is AI and what makes AI so potent that established medical disciplines such as radiology worry about their future job perspectives? What are the assets of AI in radiology today - and what are the major challenges? This review article tries to give some answers to these questions.

Objectives: To assess undergraduate medical students' attitudes towards artificial intelligence (AI) in radiology and medicine., Materials and Methods: A web-based questionnaire was designed using SurveyMonkey, and was sent out to students at three major medical schools. It consisted of various sections aiming to evaluate the students' prior knowledge of AI in radiology and beyond, as well as their attitude towards AI in radiology specifically and in medicine in general. Respondents' anonymity was ensured., Results: A total of 263 students (166 female, 94 male, median age 23 years) responded to the questionnaire. Around 52% were aware of the ongoing discussion about AI in radiology and 68% stated that they were unaware of the technologies involved. Respondents agreed that AI could potentially detect pathologies in radiological examinations (83%) but felt that AI would not be able to establish a definite diagnosis (56%). The majority agreed that AI will revolutionise and improve radiology (77% and 86%), while disagreeing with statements that human radiologists will be replaced (83%). Over two-thirds agreed on the need for AI to be included in medical training (71%). In sub-group analyses male and tech-savvy respondents were more confident on the benefits of AI and less fearful of these technologies., Conclusion: Contrary to anecdotes published in the media, undergraduate medical students do not worry that AI will replace human radiologists, and are aware of the potential applications and implications of AI on radiology and medicine. Radiology should take the lead in educating students about these emerging technologies., Key Points: • Medical students are aware of the potential applications and implications of AI in radiology and medicine in general. • Medical students do not worry that the human radiologist or physician will be replaced. • Artificial intelligence should be included in medical training.