Artificial Intelligence in Radiology—Ethical Considerations

Brady, Adrian P.; Neri, Emanuele

doi:10.3390/diagnostics10040231

Cited by 66 publications

(38 citation statements)

References 18 publications

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“…Automation bias leads humans to prefer machine-generated decisions over those of humans. The question is to what degree physicians can delegate the task of diagnosing to autonomous systems without exposing themselves to increased liability for malpractice in case the system makes an error [ 75 ]. Thus, there is a need for different liability models for different use cases and for a risk liability system.…”

Section: Discussionmentioning

confidence: 99%

Ethical Implications of Alzheimer’s Disease Prediction in Asymptomatic Individuals through Artificial Intelligence

2021

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Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put forward for AI aided AD prediction in subjectively asymptomatic individuals and their ethical implications. The ethical assessment is based on a systematic literature search. Thematic analysis was conducted inductively of 18 included publications. The ethical framework includes the principles of autonomy, beneficence, non-maleficence, and justice. Reasons for offering predictive tests to asymptomatic individuals are the right to know, a positive balance of the risk-benefit assessment, and the opportunity for future planning. Reasons against are the lack of disease modifying treatment, the accuracy and explicability of AI aided prediction, the right not to know, and threats to social rights. We conclude that there are serious ethical concerns in offering early diagnosis to asymptomatic individuals and the issues raised by the application of AI add to the already known issues. Nevertheless, pre-symptomatic testing should only be offered on request to avoid inflicted harm. We recommend developing training for physicians in communicating AI aided prediction.

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Section: Discussionmentioning

confidence: 99%

Ethical Implications of Alzheimer’s Disease Prediction in Asymptomatic Individuals through Artificial Intelligence

2021

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“…In many cases, these data biases are often introduced inadvertently by AI algorithm developers but unscrupulous individuals can take advantage of this to exacerbate bias from cultural prejudices and increase disparities in delivering healthcare services. Moreover, misuse of AI models can also result when the datasets used for model training do not take into account future use-case conditions; for example, radiologists can easily adapt to change in MRI field strength and breathing motion artifacts but these changes will affect the performance of AI models unless they have been specifically allowed for during the training of the models (Brady and Neri, 2020).…”

Section: Potential Misuse Of Ai Applicationsmentioning

confidence: 99%

The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype

Abdulkareem

Petersen

2021

Front. Artif. Intell.

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COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.

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“…Similarly, is a machine-made critical decision socially acceptable? For sure, the society must pretend to understand how a neural network works and how the results have been produced [15].…”

Section: Transparency Amentioning

confidence: 99%

Measurement of Ethical Issues in Software Products

F¹

2020

Preprint

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Ethics is a research field that is obtaining more and more attention in Computer Science due to the proliferation of artificial intelligence software, machine learning algorithms, robot agents (like chatbot), and so on. Indeed, ethics research has produced till now a set of guidelines, such as ethical codes, to be followed by people involved in Computer Science. However, a little effort has been spent for producing formal requirements to be included in the design process of software able to act ethically with users. In the paper, we investigate those issues that make a software product ethical and propose a set of metrics devoted to quantitatively evaluate if a software product can be considered ethical or not.

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Artificial Intelligence in Radiology—Ethical Considerations

Cited by 66 publications

References 18 publications

Ethical Implications of Alzheimer’s Disease Prediction in Asymptomatic Individuals through Artificial Intelligence

Ethical Implications of Alzheimer’s Disease Prediction in Asymptomatic Individuals through Artificial Intelligence

The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype

Measurement of Ethical Issues in Software Products

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