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

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever­growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi­society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Leadership in Interventional Radiology - Fostering a Culture of Excellence.

This invited article reviews the current status of Interventional Radiology (IR), in terms of its status as a speciality, and outlines the conditions needed for IR to function optimally within healthcare settings. Guidance is offered in terms of developing an IR department, ensuring high-quality practice, dealing with administrative and political challenges, dealing with industry and creating a legacy.

Radiology in the Era of Value-based Healthcare: A Multi-Society Expert Statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA.

Background The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. Methods, findings and interpretation This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined. Published under a CC BY 4.0 license.

The vanishing radiologist-an unseen danger, and a danger of being unseen.

KEY POINTS: • Radiology has developed into a central and important part of patient care.• A combination of technological developments, increasing workload and radiologists' behaviour run the risk of diminishing the visibility of radiologists to referrers and patientsRadiology has developed into a central and important part of patient care.• It is vital for the successful future of radiology that we remain conscious of the need to maintain visibility of who we are and what we contribute to patient care.

Radiology in the Era of Value-Based Healthcare: A Multi-Society Expert Statement From the ACR, CAR, ESR, IS3R, RANZCR, and RSNA.

BACKGROUND: The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. METHODS, FINDINGS AND INTERPRETATION: This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes. This article is a simultaneous joint publication in Radiology, Journal of the American College of Radiology, Canadian Association of Radiologists Journal, and Insights into Imaging. Published under a CC BY-NC-ND 4.0 license. Online supplemental material is available for this article.

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.

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

Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. KEY POINTS.

Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety.• Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance.• AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated.

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

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

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

The QuADRANT study: Current status and recommendations for improving uptake and implementation of clinical audit of medical radiological procedures in Europe. The radiotherapy perspective.

BACKGROUND: QuADRANT was a research project funded by the European Commission to evaluate clinical audit uptake and implementation across Europe, with an emphasis on clinical audit as mandated within the BSSD (Basic Safety Standards Directive). AIM: Focusing on the QuADRANT objectives - to obtain an overview of European clinical audit activity; identify good practices, resources, barriers and challenges; provide guidance and recommendations going forwards; identify the potential for European Union action on quality and safety focusing on the field of radiotherapy. RESULTS: A pan-European survey, expert interviews and a literature review conducted within the framework of the QuADRANT project indicated that developments in national clinical audit infrastructure are required. While in radiotherapy, there is a strong tradition and high level of experience of dosimetry audits and well-established practice through the IAEA's QUATRO audits, few countries have a well-established comprehensive clinical audit programme or international/national initiatives on tumour specific clinical audits. Even if sparse, the experience from countries with established system of quality audits can be used as role-models for national professional societies to promote clinical audit implementation. However, resource allocation and national prioritisation of clinical audit are needed in many countries. National and international societies should take the initiative to promote and facilitate training and resources (guidelines, experts, courses) for clinical audits. Enablers used to enhance clinical audit participation are not widely employed. Development of hospital accreditation programmes can facilitate clinical audit uptake. An active and formalised role for patients in clinical audit practice and policy development is recommended. Because there is a persisting variation in European awareness of BSSD clinical audit requirements, work is needed to improve dissemination of information on the legislative requirements relating to clinical audit in the BSSD and in relation to inspection processes. The aim is to ensure these include clinical audit and that they encompass all clinics and specialties involved in medical applications using ionising radiation. CONCLUSION: QuADRANT provided an overarching view of clinical audit practice in Europe, with all its related aspects. Unfortunately, it showed that the awareness of the BSSD requirements for clinical audit are highly variable. Therefore, there is an urgent need to dedicate efforts towards ensuring that regulatory inspections also incorporate an assessment of clinical audit program(s), affecting all aspects of clinical work and specialties involved in patient exposure to ionising radiation.

QuADRANT: a study on uptake and implementation of clinical audit of medical radiological procedures in Europe-expert recommendations for improvement, endorsed by the ESR.

BACKGROUND: QuADRANT was a study funded by the European Commission to evaluate clinical audit uptake and implementation across Europe, with an emphasis on clinical audit as mandated within the BSSD (Basic Safety Standards Directive). AIMS: QuADRANT objectives-obtain an overview of European clinical audit activity; identify good practices and resources, barriers and challenges; provide guidance and recommendations going forwards; identify the potential for European Union action on quality and safety in the three core project specialties, radiology, radiotherapy and nuclear medicine. FINDINGS AND RECOMMENDATIONS: QuADRANT identified that developments in national clinical audit infrastructure are required. National professional societies can be pivotal in improving clinical audit implementation, but resource allocation and national prioritisation of clinical audit are needed in many countries. Lack of staff time and expertise are also barriers. Enablers to enhance clinical audit participation are not widely employed. Development of hospital accreditation programmes can facilitate clinical audit uptake. An active and formalised role for patients in clinical audit practice and policy development is recommended. There is persisting variation in European awareness of BSSD clinical audit requirements. Work is needed to improve dissemination of information on the legislative requirements relating to clinical audit in the BSSD and in relation to inspection processes to ensure these include clinical audit and that they encompass all clinics and specialties involved in medical applications using ionising radiation. CONCLUSION: QuADRANT provides an important step towards enhancing clinical audit uptake and implementation across Europe and improving patient safety and outcomes.