Cho and Martinez-Martin provide a wide-ranging analysis of what they label “digital simulacra”—which are in essence data-driven AI-based simulation models such as digital twins or models used for i...

Cho and Martinez-Martin (2023) discuss the ethical challenges associated with the use of digital simulacra (also known as digital twins) in biomedicine, specifically focusing on the issue of episte...

Cho and Martinez-Martin (2023) provide a compelling critique of the profound influence that data sourcing for artificial intelligence (AI) has on the healthcare sector. They emphasize the need for...

Artificial Intelligence and Machine Learning (AI/ML) models introduce unique considerations when determining their epistemic value. Fortunately, existing work on the epistemic features of AI/ML can...

Cho and Martinez-Martin’s (2023) “Epistemic rights and responsibilities of digital simulacra for biomedicine” presents a comprehensive overview of big data and AI in clinical medicine and research....

Mildred Cho and Nicole Martinez-Martin (2023) distinguish between two of the ways in which humans can be represented in medical contexts. One is technical: a digital model of aspects of a person’s...

This perspective article explores the use of digital twins (DTs) in medicine, highlighting its capacity to simulate risks and personalize treatments while examining the emerging bioethical concerns. Central concerns include power dynamics, exclusion, and misrepresentation. We propose adopting a relational bioethical approach that advocates for a comprehensive assessment of DTs in medicine, extending beyond individual interactions to consider broader structural relations and varying levels of access to power. This can be achieved through two key relational recommendations: acknowledging the impact of (...) uneven relational structures on access to medical care and promoting social justice by evaluating resource allocation. While DTs in medicine offer promising advancements, a relational bioethical lens may provide a nuanced understanding, fostering equitable, inclusive and responsible integration of DTs into medical practice. (shrink)

Digital twins for health care have the potential to significantly impact the provision of medical services. In addition to possible use in care, this technology could serve as a conduit by which no...

Through hypothetical scenarios, this paper analyses whether machine learning (ML) could resolve one of the main shortcomings present in Christopher Boorse’s Biostatistical Theory of health (BST). In doing so, it foregrounds the boundaries and challenges of employing ML in formulating a naturalist (i.e., prima facie value-free) definition of health. The paper argues that a sweeping dataist approach cannot fully make the BST truly naturalistic, as prior theories and values persist. It also points out that supervised learning introduces circularity, rendering it (...) incompatible with a naturalistic perspective. Additionally, it underscores the need for pre-existing auxiliary theories to assess results from unsupervised learning. It emphasizes the importance of understanding the epistemological entanglements between data and data processing methods to manage expectations about what data patterns can predict. In conclusion, the paper argues against delegating the final authority for defining complex concepts like health to AI systems, as it necessitates ethical judgment and capacities for deliberation that AI currently lacks. It also warns against granting creators and deployers of AI systems the discretionary authority to determine these definitions outside the wider social discussion, advocating for ongoing public engagement on normative notions. Failure to do so risks limiting individuals and collectives’ ability to shape a just digital future and diminishes their fundamental epistemic agency. (shrink)

Bioconvergence is a growing area within the evolving bioeconomy that seeks out synergistic opportunities at the intersection of engineering and the life sciences (Greenbaum 2023). One example is th...

The construction of digital simulacra in healthcare and medical research purportedly strives to virtually recreate some aspect of reality, whether that be a piece of human tissue, an entire organ,...

If you’ve ever had a time where your smartwatch buzzed to say you’ve completed your daily steps while you were actually just brushing your hair, you’ll know our data aren’t perfect. Every day, we c...

Digital simulacra present an entrancing vision of a research-rich future shorn of the messiness that comes from dealing with real live patients as part of the research enterprise—and in that sheari...

Cho and Martinez-Martin (2023) provide a thoughtful analysis of the epistemological and ethical implications of digital twin (DT) technology in biomedical research. However, they overlook the profo...

In their article, Cho and Martinez-Martin (2023) argue that developers and users of digital simulacra for modelling health and disease should involve a continued focus on causality of health states...