Adoption of ‘Responsible AI’ in Medical Imaging
Updated: Aug 24, 2022
Ilina Navani In recent years, AI has gained the great potential to increase the efficiency and accuracy of many
technologies across various fields. The widespread use of AI-based systems has heightened the
need to adopt frameworks on responsible and ethical practices. AI is often complex and can
increase the risk of errors, as well as unintended biases and discrimination in results. This can
have serious consequences regarding the transparency and fairness of AI models, especially in
the healthcare industries where people’s lives are at stake. Governments and organizations,
therefore, deemed it important to develop a universal set of principles to deal with the ethical and
societal issues that may arise from emerging AI technologies. Extensive research is being
pursued, and guidelines are being developed, to understand how to responsibly deploy AI in
clinical practice. Key Features of Responsible AI
When looking at the responsible use of AI, one of the main concerns is the transparency and explainability of the adopted technologies. AI is often perceived as a ‘black box’ wherein the workings and decision-making processes of a model remain hidden, and, therefore, untrustworthy. However, this does not need to be the case. Transparency in AI allows us to describe, analyze, and communicate the ins and outs of a model in a way that is comprehensible to the public. AI systems should strive to be meaningfully transparent to bridge the conceptual gap that may exist between AI developers and users. As a result, transparency is tightly linked to explainability to ensure that the functions and predictions made by AI models can be understood and trusted. In medical imaging, explainability often stems from being transparent about the architecture of a particular AI model and its underlying technology. Explainability includes an adequate comprehension of both the technical details of an algorithm and of how outputs are presented to the user. Patients must understand how a model interacts with their data to achieve the desired outcome, such as predicting the presence of a disease. Hence, documentation of model training and selection processes, the criteria used to make decisions, and the measures taken to identify potential risks, is necessary. Finally, repeatability of the desired outcome is important to adjudge that a model can reliably make decisions to perform consistent actions given the same scenario.
Lastly, some of the leading features of responsible AI rest in the hands of us humans who design, operate, and utilize the technology. AI systems are often criticized for harboring biases that may arise during the training process. These biases may be intentional or unintentional, and both can have serious consequences on the outcome. Companies need to ensure that models base their decisions on honesty, fairness, and integrity, and do not favor a particular group of users over others. Thus, fairness testing should be carried out, in which a model’s decisions are recorded and compared between the real world and a counterfactual world where sensitive attributes (such as gender, age, or race) are adjusted. This is supported by data governance; wherein legitimately sourced and good-quality images are captured by AI systems in radiology. Determining thresholds for image features, such as exposure or lighting, allows for quality images that are clearly recognizable by a model. Such data governance practices must be emphasized in the training phase so that all images meet the same required standards, thus reducing the probability of unintentional discrimination in results.
The need to regulate biases and any unfairness in AI results is eventually in the control of the stakeholders involved in developing these emerging technologies. This makes accountability and responsibility principles highly important to the companies that develop AI, and healthcare professionals that deploy the AI. Determining who is responsible for the actions taken by AI systems is tricky, but it is something that needs to be properly discerned within society. Responsible AI in healthcare is not just about simply ticking some ethical ‘boxes’, but is about making honest and meaningful change in the radiology community, as well as the larger world. To accomplish this, we need to hold ourselves accountable by establishing ethical frameworks and guidelines that allow us to make responsible decisions and actions regarding AI in healthcare. Bibliography
"Model Artificial Intelligence Governance Framework Second Edition." Personal Data Protection Commission Singapore | PDPC, 2020, www.pdpc.gov.sg/-/media/Files/PDPC/PDF-Files/Resource-for-Organisation/AI/SGModelAIGovFramework2.pdf.