Navigating the ethical landscape of artificial intelligence in radiography: a cross-sectional study of radiographers' perspectives.

BACKGROUND: The integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers' perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them. METHODS: A structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives. RESULTS: Three hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions. CONCLUSIONS: This study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare.

Real Decreto 601/2019 sobre justificación y optimización: aspectos prácticos / Royal Decree 601/2019 on justification and optimization: Practical aspects

El Real Decreto 601/2019 de 18 de octubre es fruto de la transposición parcial al ordenamiento jurídico español de la Directiva EURATOM 59/2013. Este Real Decreto recoge los mandatos de la Directiva relacionados con la necesidad de justificar y optimizar la exposición médica, incluida la de personas asintomáticas, la propuesta de requisitos más estrictos en cuanto a la información que debe proporcionarse al paciente, el registro y la notificación de las dosis de los procedimientos médico-radiológicos, el uso de niveles de referencia para diagnóstico y la disponibilidad de dispositivos indicadores de dosis. El artículo revisa los aspectos más relevantes y novedades relacionadas con los principios de justificación, optimización, control de dosis y las obligaciones derivadas del derecho a la información y el consentimiento. El Real Decreto considera fundamental que exista un alto nivel de competencia, y una nueva enumeración de responsabilidades y funciones de los radiólogos, las cuales se detallan y analizan.(AU)

The Royal Decree 601/2019 of 18th october is the result of the partial transposition into the Spanish legal system of the EURATOM Directive 59/2013. This Royal Decree includes the mandates of the Directive related to the need to justify and optimize medical exposure, including that of asymptomatic people, proposal of stricter requirements regarding the information that must be provided to the patient, registration and notification of the doses of medical-radiological procedures, use of reference levels for diagnosis and the availability of dose-indicating devices. The article reviews the most relevant aspects and novelties related to the principles of justification, optimization, dose control and the obligations derived from the right to information and consent. This Royal Decree considers essential for radiologists to develop a high level of competence and a new list of responsibilities and functions, which are detailed and analysed in this article.(AU)

Does radiology require informed consent for radiation risk?

Recent trends in medical decision-making have moved from paternalistic doctor-patient relations to shared decision-making. Informed consent is fundamental to this process and to ensuring patients' ongoing trust in the health-care profession. It cannot be assumed that patients consent to the risk associated with medical exposures, unless they have been provided with the information to make that decision. This position is supported by both the legal and ethical framework around Radiation Protection detailed in this commentary.

Artificial intelligence: Who is responsible for the diagnosis?

The aim of the paper is to find an answer to the question "Who or what is responsible for the benefits and harms of using artificial intelligence in radiology?" When human beings make decisions, the action itself is normally connected with a direct responsibility by the agent who generated the action. You have an effect on others, and therefore, you are responsible for what you do and what you decide to do. But if you do not do this yourself, but an artificial intelligence system, it becomes difficult and important to be able to ascribe responsibility when something goes wrong. The manuscript addresses the following statements: (1) using AI, the radiologist is responsible for the diagnosis; (2) radiologists must be trained on the use of AI since they are responsible for the actions of machines; (3) radiologists involved in R&D have the responsibility to guide the respect of rules for a trustworthy AI; (4) radiologist responsibility is at risk of validating the unknown (black box); (5) radiologist decision may be biased by the AI automation; (6)risk of a paradox: increasing AI tools to compensate the lack of radiologists; (7) need of informed consent and quality measures. Future legislation must outline the contours of the professional's responsibility, with respect to the provision of the service performed autonomously by AI, balancing the professional's ability to influence and therefore correct the machine, limiting the sphere of autonomy that instead technological evolution would like to recognize to robots.

Ethical considerations in artificial intelligence.

With artificial intelligence (AI) precipitously perched at the apex of the hype curve, the promise of transforming the disparate fields of healthcare, finance, journalism, and security and law enforcement, among others, is enormous. For healthcare - particularly radiology - AI is anticipated to facilitate improved diagnostics, workflow, and therapeutic planning and monitoring. And, while it is also causing some trepidation among radiologists regarding its uncertain impact on the demand and training of our current and future workforce, most of us welcome the potential to harness AI for transformative improvements in our ability to diagnose disease more accurately and earlier in the populations we serve.

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.

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.

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.

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.

Overdiagnosis and overimaging: an ethical issue for radiological protection.

AIMS AND OBJECTIVES: This study aimed to analyse the key factors that influence the overimaging using X-ray such as self-referral, defensive medicine and duplicate imaging studies and to emphasize the ethical problem that derives from it. MATERIALS AND METHODS: In this study, we focused on the more frequent sources of overdiagnosis such as the total-body CT, proposed in the form of screening in both public and private sector, the choice of the most sensitive test for each pathology such as pulmonary embolism, ultrasound investigations mostly of the thyroid and of the prostate and MR examinations, especially of the musculoskeletal system. RESULTS: The direct follow of overdiagnosis and overimaging is the increase in the risk of contrast media infusion, radiant damage, and costs in the worldwide healthcare system. The theme of the costs of overdiagnosis is strongly related to inappropriate or poorly appropriate imaging examination. CONCLUSIONS: We underline the ethical imperatives of trust and right conduct, because the major ethical problems in radiology emerge in the justification of medical exposures of patients in the practice. A close cooperation and collaboration across all the physicians responsible for patient care in requiring imaging examination is also important, balancing possible ionizing radiation disadvantages and patient benefits in terms of care.

Artificial intelligence and medical imaging 2018: French Radiology Community white paper.

The rapid development of information technology and data processing capabilities has led to the creation of new tools known as artificial intelligence (AI). Medical applications of AI are emerging, and the French radiology community felt it was therefore timely to issue a position paper on AI as part of its role as a leader in the development of digital projects. Essential information about the application of AI to radiology includes a description of the available algorithms with a glossary; a review of the issues raised by healthcare data, notably those pertaining to imaging (imaging data and co-variables, metadata); a look at research and innovation; an overview of current and future applications; a discussion of AI education; and a scrutiny of ethical issues. In addition to the principles set forth at the Asilomar Conference on Beneficial AI, the French radiology community has developed ten principles aimed at governing the use and development of AI tools in a manner that will create a concerted approach centered on benefits to patients, while also ensuring good integration within clinical workflows. High-quality care in radiology and opportunities for managing large datasets are two avenues relevant to the development of a precision, personalized, and participative radiology practice characterized by improved predictive and preventive capabilities.