Teasing out Artificial Intelligence in Medicine: An Ethical Critique of Artificial Intelligence and Machine Learning in Medicine

Arnold, Mark

doi:10.1007/s11673-020-10080-1

Cited by 62 publications

(40 citation statements)

References 178 publications

(146 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Putting this issue in the context of radiological practice, radiologists would be asked to monitor AI system outputs and validate AI interpretations, so they would risk carrying the ultimate responsibility of validating something they cannot understand ( Neri et al, 2020 ). There is a clear difference between statistical and clinical validation, and hence achieving adequate informed consent is problematic when the algorithmic decision-making process is opaque to clinicians, patients, or courts ( Martinez-Martin et al, 2018 ; Arnold, 2021 ). Actually, while the number of published articles on the applications of AI in medical imaging and other medical specialties is steadily increasing, so far only few AI applications have been validated for clinical use, partly due to the difficulty of using AI projects on a large scale in real-life clinical practice, poor adherence to scientific quality standards ( Nagendran et al, 2020 ; Park et al, 2020 ), and clinical validation issues.…”

Section: Artificial Intelligence and Humansmentioning

confidence: 99%

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

et al. 2021

View full text Add to dashboard Cite

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

show abstract

Section: Artificial Intelligence and Humansmentioning

confidence: 99%

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

et al. 2021

View full text Add to dashboard Cite

show abstract

“…While a human would find it near-impossible to search through tens of thousands of medical records, to discover novel patterns and insights, an AI algorithm can be designed to perform such a task very quickly. As beneficial as this can be in many contexts (e.g., assisting patient care or preventing disease (Arnold 2021 )), informed consent may need to be secured again, if the original consent is no longer applicable. For example, someone who consents to sharing their postal code may wish to withdraw consent when they learn such data can be used to determine insurance premiums (see Floridi 2019 , p. 110).…”

Section: Big Data and Informed Consentmentioning

confidence: 99%

AI, big data, and the future of consent

2021

View full text Add to dashboard Cite

In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a ‘soft governance’ ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts.

show abstract

“…Bio/medical ethics can be turned to for inspiration when approaching and developing AI ethics due to the fact that it is well established, robust, has accountability mechanisms and is an example of the ethics of an applied science with significant social impact 60 , 61 , 62 , 63 , 64 ; this is not to be confused with applications of new technologies in medicine) 65 or the ethics of using AI in medicine. 66 However, there are important disanalogies between bio/medical ethics and any AI ethics scheme that may emerge.…”

Section: Three Approaches: Principles Processes and Ethical Consciousnessmentioning

confidence: 99%

“…Finally, and more specifically, in the context of AI there is the question regarding the legal status of an algorithm (e.g., will algorithms follow how companies have rights and obligations, and will AI systems have artificial personhood status), 58 and how the questions of legal culpability, which rely on judgments of intent, can be formulated in the context of AI systems. 59 Bio/medical ethics Bio/medical ethics can be turned to for inspiration when approaching and developing AI ethics due to the fact that it is well established, robust, has accountability mechanisms and is an example of the ethics of an applied science with significant social impact [60][61][62][63][64] ; this is not to be confused with applications of new technologies in medicine) 65 or the ethics of using AI in medicine. 66 However, there are important disanalogies between bio/medical ethics and any AI ethics scheme that may emerge.…”

Section: Abstract First-principlesmentioning

confidence: 99%

A high-level overview of AI ethics

Kazim

Koshiyama

2021

Patterns

View full text Add to dashboard Cite

Summary Artificial intelligence (AI) ethics is a field that has emerged as a response to the growing concern regarding the impact of AI. It can be read as a nascent field and as a subset of the wider field of digital ethics, which addresses concerns raised by the development and deployment of new digital technologies, such as AI, big data analytics, and blockchain technologies. The principle aim of this article is to provide a high-level conceptual discussion of the field by way of introducing basic concepts and sketching approaches and central themes in AI ethics. The first part introduces concepts by noting what is being referred to by “AI” and “ethics”, etc.; the second part explores some predecessors to AI ethics, namely engineering ethics, philosophy of technology, and science and technology studies; the third part discusses three current approaches to AI ethics namely, principles, processes, and ethical consciousness; and finally, the fourth part discusses central themes in translating ethics in to engineering practice. We conclude by summarizing and noting the inherent interdisciplinary future directions and debates in AI ethics.

show abstract

Teasing out Artificial Intelligence in Medicine: An Ethical Critique of Artificial Intelligence and Machine Learning in Medicine

Cited by 62 publications

References 178 publications

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

AI, big data, and the future of consent

A high-level overview of AI ethics

Contact Info

Product

Resources

About