Ethical AI in Digital Health: Patient Knowledge, Pulmonary Hypertension, and the Problem of Misrecognition

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.