Special Section on Ethics in Health Informatics

Petersen, Carolyn; Subbian, Vignesh

doi:10.1055/s-0040-1702014

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…There are also other factors to consider. According to a white paper by Stanford Medicine [ 105 ], the sheer volume of healthcare data is growing at an astronomical rate: an estimated 2314 exabytes (one exabyte = one billion gigabytes) would have been produced in 2020. With such vast amounts of data available it may become confusing to cardiologists and patients.…”

Section: Limitations and Challengesmentioning

confidence: 99%

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

et al. 2023

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Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.

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Section: Limitations and Challengesmentioning

confidence: 99%

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

et al. 2023

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“…While RECs traditionally focus on protecting participants and ensuring societal benefits 10 , modern healthcare’s evolution and the advent of digital health technology introduced new ethical complexities, such as juggling privacy with the need for public health surveillance, data ownership concerns, consent validity, and disparities from algorithmic biases 18, 19 . Balancing stakeholders’ interests at the crossroads of participants, researchers, and society 20 , RECs are often vulnerable to being received as disrespectful, bureaucratic or obstructive 21 .…”

Section: Introductionmentioning

confidence: 99%

Practical Challenges for Commercial Enterprises in the Ethics Review Process for Digital Health Research: Document Analysis and Interview Study

Yang,

Potts

2024

Preprint

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Introduction: The rapid evolution of digital health interventions has created challenges in navigating the ethics approval process for commercial enterprises. Recognising the need for processes that balance ethical considerations with the specifics of digital health research, this study aimed to describe what happens when enterprises seek ethical review in the UK and propose strategies for a smoother process. Methods: Inductive thematic analysis was conducted on thirty-two ethics review documents (29 to an NHS Research Ethics Committee, 3 to an ethics committee at a higher education institution) submitted by digital health developers with commercial sponsors and ten semi-structured interviews with digital health enterprise representatives. Results: Ethics committees raised an average of 4.3 action points per submission. We identified five broad themes around committees' concerns: ethical commitments in care; study design; digital health research peculiarities; data governance; document quality and completeness. Interviewees reported a range of experiences. Here, we identified six broad themes: submission and protocol revisions; the dynamic between parties; application time and procedures; acumen and practicality in digital health; support and guidance from RECs; enterprise expertise and resources. Conclusion: We suggest strategies for applicants to achieve a favourable decision, such as evidence-based study designs and participant support for better inclusion and equity, and identified specific pitfalls to avoid, such as lack of justification for data governance procedures. We recommend that UK research ethics committees provide adapted guidance and foster collaboration through open communication and mutual understanding, to facilitate a smoother approval process in digital health research.

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“…However, data sharing and re-use of people's data entail risks and challenges for people's privacy, fair use of data, and justice (4)(5)(6). It becomes increasingly di cult to apply old safeguards and oversight in dataintensive contexts where the very notion of consent and anonymization change (7,8).…”

Section: Introductionmentioning

confidence: 99%

What are the Ethical Approaches Used by Experts When Sharing Health Data? - An Interview Study

Johansson

Bentzen

Mascalzoni

2021

Preprint

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Background: Health data driven activities have become central in diverse areas (research, AI development, wearables, etc.), and new ethical challenges have arisen with regard to privacy, integrity, and appropriateness of use. To improve data subjects’ privacy and security, we aim to identify ethically relevant issues experienced by experts in the data intensive exploitation area while collecting, using, or sharing peoples’ health data. Methods: Twelve experts, who were collecting, using, or sharing health data in different contexts in Sweden were interviewed. We used systematic expert interviews to access experts’ specialist knowledge. Thereafter, thematic analysis was used to identify categories and subcategories. The codes targeted ethical issues and approaches reported by the interviewed experts. Results: The main conceptual categories were ‘Consideration of the consequences,’ ‘Respect for rights,’ ‘Procedural compliance,’ and ‘Professional conduct.’ The respondents discussed and balanced different ethical approaches through several examples. They were morally sensitive to the problems involved in sharing health data. Conclusions: These empirical findings suggest a need for practical procedures that make it easier for data collectors and sharers to follow the ethical principles and laws relating to data sharing. We suggest that the time is now ripe to move on from policy discussions to practical technological solutions of the principles.

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Special Section on Ethics in Health Informatics

Cited by 6 publications

References 21 publications

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

Practical Challenges for Commercial Enterprises in the Ethics Review Process for Digital Health Research: Document Analysis and Interview Study

What are the Ethical Approaches Used by Experts When Sharing Health Data? - An Interview Study

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