Ethics and standards in the use of artificial intelligence in medicine on behalf of the Royal Australian and New Zealand College of Radiologists.

The Royal Australian and New Zealand College of Radiologists (RANZCR) led the medical community in Australia and New Zealand in considering the impact of machine learning and artificial intelligence (AI) in health care. RANZCR identified that medical leadership was largely absent from these discussions, with a notable absence of activity from governments in the Australasian region up to 2019. The clinical radiology and radiation oncology sectors were considered ripe for the adoption of AI, and this raised a range of concerns about how to ensure the ethical application of AI and to guide its safe and appropriate use in our two specialties. RANZCR's Artificial Intelligence Committee undertook a landscape review in 2019 anddetermined that AI within clinical radiology and radiation oncology had the potential to grow rapidly and significantly impact the professions. In order to address this, RANZCR drafted ethical principles on the use of AI and standards to guide deployment and engaged in extensive stakeholder consultation to ensure a range of perspectives were received and considered. RANZCR published two key bodies of work: The Ethical Principles of Artificial Intelligence in Medicine, and the Standards of Practice for Artificial Intelligence in Clinical Radiology. RANZCR's publications in this area have established a solid foundation to prepare for the application of AI, however more work is needed. We will continue to assess the evolution of AI and ML within our professions, strive to guide the upskilling of clinical radiologists and radiation oncologists, advocate for appropriate regulation and produce guidance to ensure that patient care is delivered safely.

Kenny LM ，Nevin M ，Fitzpatrick K 《-》

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.

Brady AP ，Allen B ，Chong J ，Kotter E ，Kottler N ，Mongan J ，Oakden-Rayner L ，Dos Santos DP ，Tang A ，Wald C ，Slavotinek J ... -  《-》

Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.

Artificial intelligence (AI) has the potential to impact clinical practice and healthcare delivery. AI is of particular significance in radiology due to its use in automatic analysis of image characteristics. This scoping review examines stakeholder perspectives on AI use in radiology, the benefits, risks, and challenges to its integration. A search was conducted from 1960 to November 2019 in EMBASE, PubMed/MEDLINE, Web of Science, Cochrane Library, CINAHL, and grey literature. Publications reflecting stakeholder attitudes toward AI were included with no restrictions. Commentaries (n = 32), surveys (n = 13), presentation abstracts (n = 8), narrative reviews (n = 8), and a social media study (n = 1) were included from 62 eligible publications. These represent the views of radiologists, surgeons, medical students, patients, computer scientists, and the general public. Seven themes were identified (predicted impact, potential replacement, trust in AI, knowledge of AI, education, economic considerations, and medicolegal implications). Stakeholders anticipate a significant impact on radiology, though replacement of radiologists is unlikely in the near future. Knowledge of AI is limited for non-computer scientists and further education is desired. Many expressed the need for collaboration between radiologists and AI specialists to successfully improve patient care. Stakeholder views generally suggest that AI can improve the practice of radiology and consider the replacement of radiologists unlikely. Most stakeholders identified the need for education and training on AI, as well as collaborative efforts to improve AI implementation. Further research is needed to gain perspectives from non-Western countries, non-radiologist stakeholders, on economic considerations, and medicolegal implications. Stakeholders generally expressed that AI alone cannot be used to replace radiologists. The scope of practice is expected to shift with AI use affecting areas from image interpretation to patient care. Patients and the general public do not know how to address potential errors made by AI systems while radiologists believe that they should be "in-the-loop" in terms of responsibility. Ethical accountability strategies must be developed across governance levels. Students, residents, and radiologists believe that there is a lack in AI education during medical school and residency. The radiology community should work with IT specialists to ensure that AI technology benefits their work and centres patients.

Yang L ，Ene IC ，Arabi Belaghi R ，Koff D ，Stein N ，Santaguida PL ... -  《-》

Artificial Intelligence in Radiology: Some Ethical Considerations for Radiologists and Algorithm Developers.

As artificial intelligence (AI) is finding its place in radiology, it is important to consider how to guide the research and clinical implementation in a way that will be most beneficial to patients. Although there are multiple aspects of this issue, I consider a specific one: a potential misalignment of the self-interests of radiologists and AI developers with the best interests of the patients. Radiologists know that supporting research into AI and advocating for its adoption in clinical settings could diminish their employment opportunities and reduce respect for their profession. This provides an incentive to oppose AI in various ways. AI developers have an incentive to hype their discoveries to gain attention. This could provide short-term personal gains, however, it could also create a distrust toward the field if it became apparent that the state of the art was far from where it was promised to be. The future research and clinical implementation of AI in radiology will be partially determined by radiologist and AI researchers. Therefore, it is very important that we recognize our own personal motivations and biases and act responsibly to ensure the highest benefit of the AI transformation to the patients.

Mazurowski MA 《-》

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology.

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.

Tang A ，Tam R ，Cadrin-Chênevert A ，Guest W ，Chong J ，Barfett J ，Chepelev L ，Cairns R ，Mitchell JR ，Cicero MD ，Poudrette MG ，Jaremko JL ，Reinhold C ，Gallix B ，Gray B ，Geis R ，Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group ... -  《-》