Ethical AI in Digital Health: Patient Knowledge, Pulmonary Hypertension, and the Problem of Misrecognition
Clicks: 1
ID: 317441
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #14 of 55 articles by views in European Heart Journal - Digital Health
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Artificial intelligence (AI) is increasingly being introduced into healthcare as a way to make care more efficient, accurate, and personalised. Most discussion has focused on familiar concerns such as bias, safety, and transparency. We argue that another problem deserves much more attention: misrecognition. AI systems may come to know patients mainly through what is easiest to measure and record, while missing what is hardest to code but most central to living with illness. Drawing on the concept of epistemic injustice, we suggest that patients are often treated as unreliable knowers, or required to fit complex, embodied, and relational experiences into clinical categories that do not quite work. AI can deepen this problem by relying on proxies such as cost, utilisation, and adherence, and in doing so harden narrow understandings of illness into technical systems. Using pulmonary hypertension (PH) as a case, we reflect on patient-led outcome measures such as emPHasis-10 to show that measurement is never neutral: it shapes what counts as legitimate knowledge, meaningful change, and good care. The real question, we argue, is not only whether AI is accurate or fair, but what kinds of patient experience become visible within it.
| Reference Key |
openalex_W7164848242
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Iain Armstrong, Peter Winter |
| Journal | European Heart Journal - Digital Health |
| Year | 2026 |
| DOI |
10.1093/ehjdh/ztag087
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.