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TRUVACE RECORD VERSION
record: TRV-2026-0691
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-08T06:28:02.636876Z
status: published
lens: p_space
sector: health
headline: Data science and AI in medicine and global health: The need for inter-philosophies dialogue, cross-cultural ethics and ecocentricity
dek: Advances in data science and medical artificial intelligence (AI) raise complex philosophical and ethical quandaries about what it means to know a person or a community through data and what kinds of people and societies we are becoming in this era of predictive data science. Drawing on four lightly fictional but reality-informed case studies in mental health, radiology, genomics and environmental public health, we reflect on how AI technologies, largely built on Western biomedical traditions, may conflict with…
gain_title: (none)
problem_title: Medical AI systems built on Western biomedical traditions may inflict ontological harm and diminish trust in patient-clinician relationships by conflicting with relational and Indigenous understandings of health.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Medical AI systems built on Western biomedical traditions may inflict ontological harm and diminish trust in patient-clinician relationships by conflicting with relational and Indigenous understandings of health.
problem_evidence: AI systems may inflict significant ontological harm by reconfiguring identity, moral agency and imagined futures | diminished trust in patient-clinician relationships
quick_read: Published 6 August 2026 in the South African Medical Journal, this peer-reviewed essay examines data science and medical AI through four lightly fictional but reality-informed case studies from Bamenda to Mthatha to Toronto. It argues that AI tools largely built on Western biomedical traditions may conflict with relational, spiritual and Indigenous understandings of health, manifesting as epistemic friction and diminished trust.

The significance lies in moving the ethics debate from how AI knows and predicts to how it reconfigures being, including identity, moral agency and imagined futures. The paper does not report measured clinical outcomes by its publication date, instead advancing a normative call for cross-cultural ethics and ecocentric care, leaving open how such philosophical integration would be operationalized and evaluated in real deployments.
limitation: Analysis relies on lightly fictional but reality-informed case studies rather than measured deployment outcomes, and frames harms as potential rather than observed.
tag: Evidence-backed problem
key_points: Paper uses four lightly fictional but reality-informed case studies in mental health, radiology, genomics and environmental public health to examine conflicts. | Authors argue Western biomedical AI traditions can clash with relational, spiritual and Indigenous understandings of health and wellbeing. | Analysis shifts from epistemology of how AI knows and predicts to ontology of how data and AI reconfigure being, identity and moral agency.
rundown: The authors draw on cases spanning mental health, radiology, genomics and environmental public health to illustrate how predictive data science knows persons and communities through data, raising questions about what kinds of people and societies emerge.

They propose a renewed medical humanism grounded in inter-philosophies dialogue, cross-cultural ethics and ecocentric approaches, arguing African, Indigenous, Islamic, Buddhist, Confucian and marginalised Western worldviews should be constitutive resources for inclusive health technologies, and that bioethics should be core infrastructure alongside data science and medicine.
sources:
- peer_reviewed | South African Medical Journal | https://doi.org/10.7196/samj.2026.v116i6b.3662 | 2026-08-06
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