Ethics, Artificial Intelligence, and Radiology

doi:10.1016/j.jacr.2018.05.020

Journal of the American College of Radiology

Volume 15, Issue 9, September 2018, Pages 1317-1319

https://doi.org/10.1016/j.jacr.2018.05.020 Get rights and content

Section snippets

The Ethics of Data

Five key areas of data ethics [2] include informed consent, privacy and data protection, ownership, objectivity, and the gap between those who have or lack the resources to manage and analyze large data sets. Other issues include bias against group-level subsets of individuals such as women or specific ethnic or economic groups, the importance of trust in assessing data ethics, and providing meaningful access rights to individual patients.

Release of information, data use agreements, and

The Ethics of Algorithms

With the power and scope of artificial intelligence algorithms evolving at a rate hard to comprehend, the discussion and concepts of algorithm ethics for autonomous and intelligent systems is in flux. This includes the definition and practice of ethical design and audit algorithm requirements. Primary ethical concerns 2, 6 include the following:

•
Safety: Autonomous and intelligent systems should be safe and secure throughout their operational lifetimes, and verifiably so when applicable and

The Ethics of Practices

Practice ethics inform the code of conduct for people and organizations involved with the entire lifecycle of artificial intelligence products, including innovation, research, design, build, implementation, production use, and even finally retiring the product or system. Radiology should define and document ethical artificial intelligence practice that both promotes technical progress and protects the rights of individuals and groups. Central to this are patient consent, user privacy, secondary

Summary

The rapid evolution of autonomous and intelligent systems in radiology calls for us to update radiology’s ethics and code of behavior for artificial intelligence in radiology. This code must be continually reassessed as these systems become more complex and autonomous. Radiologists and developers of autonomous and intelligent systems have a duty to follow rules and principles that result in the best outcomes for patients.

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References (9)

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Protecting your patients’ interests in the era of big data, artificial intelligence, and predictive analytics
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There are more references available in the full text version of this article.

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Update on ethical aspects in clinical research: Addressing concerns in the development of new AI tools in radiology
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El análisis de aspectos éticos en la investigación clínica siempre ha supuesto un reto que ha requerido constantes actualizaciones.
En resumen, la ética en la investigación es el conjunto de principios, reglas y normas específicas del comportamiento que una comunidad investigadora ha decidido como apropiadas y justas bajo la premisa de que la investigación debe caracterizarse por ser válida, fiable, legítima y representativa.
Se presenta una revisión no sistemática que trata algunas de las preocupaciones éticas que la comunidad científica debe tener en cuenta. Numerosos estudios sobre el desarrollo de nuevas herramientas de inteligencia artificial (IA), especialmente aplicadas a la radiología, hacen necesario que la comunidad investigadora en esta área promueva debates y establezca unos principios éticos aplicables a la práctica y al desarrollo de las nuevas herramientas de IA.
The analysis of ethical aspects in clinical research has always been a challenge and has required constant updates.
In short, research ethics is the set of specific principles, rules, and norms of behavior that a research community has decided are appropriate and fair under the premise that research must be valid, reliable, legitimate, and representative.
This non-systematic review brings some ethical concerns that should be considered within the scientific community. Many studies and the development of new artificial intelligence (AI) tools, especially in radiology, make it necessary for the radiology research community to promote debates and establish ethical standards for the practice and development of new AI tools.
Applications of machine learning in healthcare
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El presente trabajo tiene por objetivo mostrar algunas aplicaciones recientes de aprendizaje automático en el área de la salud. El aprendizaje automático o machine learning es una rama de la inteligencia artificial que ha logrado grandes avances en la extracción de patrones y análisis predictivo obteniendo el estado del arte en varias tareas. Por lo mismo, esta tecnología es utilizada en varios sistemas dentro de hospitales y clínicas. Este trabajo introduce a la temática de aprendizaje automático y algunos de sus usos en salud. Posteriormente, se muestran algunas aplicaciones divididas según los tipos de datos que utilizan.
This work aims to show recent applications of machine learning in health. Machine learning is a branch of artificial intelligence that has produced excellent results in pattern recognition and predictive analysis achieving state-of-the-art performance in several tasks. For that reason, this technology is used in multiple hospital systems. This article introduces machine learning, important algorithms, and ethical considerations as well as commenting on various applications in health sorted by the type of data used in those studies.
Barriers to Artificial Intelligence implementation in radiology practice: What the Radiologist needs to know
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La inteligencia artificial (IA) ofrece la posibilidad de cambiar la práctica de la radiología clínica en todo el mundo. Sin embargo, existen dificultades que los radiólogos deben conocer antes de aplicar la inteligencia artificial en la práctica diaria. Estas dificultades incluyen cuestiones de cumplimiento de la legislación, cuestiones éticas, aspectos relacionados con la privacidad de los datos y la ciberseguridad, el sesgo de aprendizaje automático y la integración segura de la IA en la práctica habitual. En este artículo, resumimos estas cuestiones y su repercusión en la radiología clínica.
Artificial Intelligence has the potential to disrupt the way clinical radiology is practiced globally. However, there are barriers that radiologists should be aware of prior to implementing Artificial Intelligence in daily practice. Barriers include regulatory compliance, ethical issues, data privacy, cybersecurity, AI training bias, and safe integration of AI into routine practice. In this article, we summarize the issues and the impact on clinical radiology.
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Citation Excerpt :
Providers priorities is safety, and ML algorithms need to be weighted similarly towards safety [4]. Introducing AI can provide an advantage by making decisions based on subtle data variations that humans may not detect [8]. Implementation of an autonomous AI based system in populations without access to care, automation bias risks may occur more often, as there may not be a radiologist present to oversee AI [8].
Artificial intelligence (AI) has an enorgmoust potential to improve radiologic diagnostics, efficiency, and consistency, through implementation of evidence-based regulations is crucial to prevent ethical violations pertaining to patient privacy, data management, and diagnostic accuracy. Several key terms were searched on PubMed such as “Artificial Intelligence”, "COVID-19", “Ethics” and “Radiology”. Ultimately, 32 articles that were published from June 2016 - November 2020 were included in our review given their presentation of original information and relevance to clinical AI imaging and ethics. Several themes appeared and allowed for the following recommendations: 1. Protocols explaining how patient data will be utilized should be provided to and implemented by providers. 2. Differences in data regulations among industry partners must be considered for successful implementation of AI in radiology, and protections against entities with a financial interest in patient data must be included. 3. Providers should receive education and training on basic AI algorithm methodology to allow them to explain risks to patients and obtain informed consent. 4. Radiologists and AI programmers should work together to allow for feedback, collaboration, and the development of more clinically impactful and ethical AI applications. We conclude by recommending collaboration between providers and programmers with a focus on existing AI platforms over developing new algorithms, as there is a lack of consensus on the proper applications of and regulations needed for already existing technologies. Future research should examine and aim to addressimplicit biases in AI technologies.
Trustworthy Artificial Intelligence in Medical Imaging
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Citation Excerpt :
The conceptualization of what makes AI trustworthy, as of today, remains ambiguous and highly debated in research and practice.40 To address this need, frameworks and guidelines for ethical AI,10,41 beneficial AI,42 and trustworthy AI (TAI)39,40,43 have been set forth to advance AI while minimizing the potential risks associated with it. The technology sector has been a leader in seeking the implementation of TAI.

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: Dr Geis is an adviser to Philips Healthcare, is a shareholder in Montage Healthcare Solutions (purchased by Nuance), has received speaking fees from Intelerad Medical Systems, and is an adviser to Innosphere. Dr Kohli has no conflicts of interest related to the material discussed in this article.

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Data scienceData science in practiceEthics, Artificial Intelligence, and Radiology

Section snippets

The Ethics of Data

The Ethics of Algorithms

The Ethics of Practices

Summary

First page preview

J Am Coll Radiol

What is data ethics?

Philos Trans R Soc A

The ethics of big data: current and foreseeable issues in biomedical contexts

Sci Eng Ethics

Consent and the ethical duty to participate in health data research

J Med Ethics

Data science
Data science in practice
Ethics, Artificial Intelligence, and Radiology