Emerging ethical issues regarding digital health data. On the World Medical Association Draft Declaration on Ethical Considerations Regarding Health Databases and Biobanks

Aicardi, Christine; Savio, Lorenzo Del; Lucivero, Federica; Tempini, Niccolò; Prainsack, Barbara

doi:10.3325/cmj.2016.57.207

Cited by 33 publications

(20 citation statements)

References 11 publications

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“…Organisation has a number of projects under way to develop guidance for member states (Aicardi et al, 2016) (World Health Organisation, 2019. In China, several norms7 provide specific and detailed instructions to ensure health data security and confidentiality (Wang, 2019) to ensure that health and medical big data sets can be used as a national resource to develop algorithms (Zhang et al, 2018) for the improvement of public health (Li, Li, Jiang, & Lan, 2019).…”

Section: Considerationmentioning

confidence: 99%

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Morley

Machado²,

Burr

et al. 2019

SSRN Journal

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Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be 'Artificial Intelligence' (AI)-particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by "robot doctors." Instead, it is an argument that rests on the classic counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients' health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted.

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Section: Considerationmentioning

confidence: 99%

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Morley

Machado²,

Burr

et al. 2019

SSRN Journal

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“…Data which may be non-identifiable in one research setting may turn out to be identifiable in another. Reporting on the World Medical Association (WMA) draft declarations regarding health and biobank data bases, Aicard et al (2016) noted the following:…”

Section: Research Involving Non-identifiable Data In Private Domain Wmentioning

confidence: 99%

Ethics in academic research and scholarship: An elucidation of the principles and applications

Agunloye¹

2019

JGER

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Responsible, respectable, and successful engagement in research and scholarship in academia requires adherence to certain basic professional ethical principles to sustain the fidelity of academic work and the integrity of the researcher-scholar. This is more so for works which are intended for dissemination, information, attention, and consumption of audiences beyond the researcher-scholar's proximal academic habitat. Ethical principles in research and scholarship apply whether the intended work, to be made public, is a conceptual scholarly narrative of ideas and thoughts or a narration of actual scientific-process-based research. This paper explores some of the key ethical principles in research and scholarship, their applications, and implications. Emerging ethical requirements, based on the 2018 updates to the Common Rule for Institutional Review Board, are explored. Criteria for meeting international ethical requirements and processes in research and scholarship are outlined with guidelines for authorship. Implications for current and future need for fidelity, integrity and trustworthiness in the academic enterprise are stressed.

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“…It clearly encompasses information that is not medical information in the narrow sense of information "gathered by physicians or other members of the medical team" (WMA -The World Medical Association 2016). The health-related digital phenotype also encompasses information produced by "individuals themselves via social media, fitness trackers, remote sensors, and the Internet of things", which, it has been argued, will be very relevant for health-care (Aicardi, Del Savio, Dove, Lucivero, Mittelstadt, et al 2016, see also Aicardi, Del Savio, Dove, Lucivero, Tempini, et al 2016). A broader concept than that of medical information is that of "data concerning health", as defined by Article 4(15) of the GDPR, which refers (according to recital 35) to information able to "reveal information about […] health status" sensors, whenever it is processed for health purposes (Mittelstadt 2017a, 2).…”

Section: The Limits Of the Libertarian Approachmentioning

confidence: 99%

The Digital Phenotype: a Philosophical and Ethical Exploration

Loi

2018

Philos. Technol.

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The concept of the digital phenotype has been used to refer to digital data prognostic or diagnostic of disease conditions. Medical conditions may be inferred from the time pattern in an insomniac's tweets, the Facebook posts of a depressed individual, or the web searches of a hypochondriac. This paper conceptualizes digital data as an extended phenotype of humans, that is as digital information produced by humans and affecting human behavior and culture. It argues that there are ethical obligations to persons affected by generalizable knowledge of a digital phenotype, not only those who are personally identifiable or involved in data generation. This claim is illustrated by considering the health-related digital phenotypes of precision medicine and digital epidemiology. Keywords Information technologies Innovation Policy making Risk, biomedical data Big data

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Emerging ethical issues regarding digital health data. On the World Medical Association Draft Declaration on Ethical Considerations Regarding Health Databases and Biobanks

Cited by 33 publications

References 11 publications

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Ethics in academic research and scholarship: An elucidation of the principles and applications

The Digital Phenotype: a Philosophical and Ethical Exploration

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