Artificial Intelligence in Radiology Will Require Ethics, Standards

Ethical use of artificial intelligence in radiology will require stakeholders to carefully consider how they use data, as well as how they develop and operate decision-making tools, according to a statement from some of the world’s leading radiology, medical physics, and imaging informatics groups. 

The contributing organizations include the American College of Radiology (ACR), European Society of Radiology (ESR), Radiological Society of North America (RSNA), Society for Imaging Informatics in Medicine (SIIM), European Society of Medical Imaging Informatics (EuSoMII), Canadian Association of Radiologists (CAR) and American Association of Physicists in Medicine (AAPM). 

"Because of the international nature of AI research, rapid pace of technology development and cross-border deployment of AI software, an ethical framework for AI in radiology was much needed," said An Tang, MD, MSc, FRCPC, chair of CAR's AI Working Group and co-author. 

"This multi-society statement highlights ethical issues and discusses how to detect and manage them in a manner that is beneficial to patients."

The authors noted that AI has the potential to radically improve care delivery, patient engagement, and clinical workflows. However, the technology also comes with potential risks, such as the possibility of grave medical errors, the amplification of inherent biases, and the perpetuation of ethical and societal issues. 

"Developments in artificial intelligence represent one of the most exciting, and most challenging, changes in how radiology services will be delivered to patients in the near future,” said Dr. Adrian Brady, Chairperson of the ESR Quality, Safety and Standards Committee and co-author. 

“The potential for patient benefit from AI implementation is great, but there are also significant risks of unexpected or unplanned harmful effects of these changes. It's incumbent on professionals working in this area to ensure that patient and public benefit and safety are paramount.”

The ethics of data collection, management, and analytics are essential to the use of AI in radiology, the authors noted. When an AI model is implemented, radiologists should know how they will document and notify patients about how they will use patient data and recognize what biases may exist in the data used to train algorithms. 

Radiologists should also reflect on the steps they have taken to mitigate these biases, and how users should consider any remaining biases, the statement said. 

“In order for AI technology to positively impact human health, it is crucial that robust and reproducible data, methods, guidelines, and tools are developed and made available,” said Cynthia McCollough, PhD, FACR, FAAPM, FAIMBE and president of the AAPM. 

“As quantitative and interdisciplinary scientists, medical physicists are playing an essential role in the development of these essential resources — we need to ensure that variability and bias are minimized in the data used to answer compelling medical questions.”

In addition to the ethics of data, radiologists should also consider the ethics of AI algorithms and trained models, the authors stated. There should be as much transparency as possible as to how these tools make decisions. If an algorithm contributes to an adverse event, providers should be able to understand why it produced the result that it did. 

“Radiologists remain ultimately responsible for patient care and will need to acquire new skills to do their best for patients in the new AI ecosystem,” said J. Raymond Geis, MD, FACR, FSIIM, ACR Data Science Institute senior scientist and one of the paper’s leading contributors. 

“The radiology community needs an ethical framework to help steer technological development, influence how different stakeholders respond to and use AI, and implement these tools to make the best decisions for — and increasingly with — patients.”

Finally, the statement discussed the ethics of using AI tools in radiological practice. Radiologists should consider the potential risks associated with AI implementation, and the ways they can mitigate these risks. Organizations should also think about what education and skills are necessary to effectively use AI and to use it safely. 

With this statement, leading radiology organizations have outlined critical considerations for the ethical use of AI in radiology. 

“The application of AI tools in radiological practice lies in the hand of the radiologists, which also means that they have to be well-informed not only about the advantages they can offer to improve their services to patients, but also about the potential risks and pitfalls that might occur when implementing them,” said Erik R. Ranschaert, MD, PhD, president of EuSoMII. 

“This paper is therefore an excellent basis to improve their awareness about the potential issues that might arise, and should stimulate them in thinking proactively on how to answer the existing questions.”