The ethical application of artificial intelligence in digital health: patients' knowledge, pulmonary hypertension,...

Artificial intelligence (AI) is increasingly being introduced into healthcare to improve efficiency, accuracy, and personalisation. Debate has centred on concerns such as bias, safety, and transparency. We argue that another problem deserves much more attention: misrecognition. By this we mean the risk that digital systems know patients mainly through what is easiest to measure and record, while overlooking what is hardest to code but most central to living with illness. Drawing on the concept of epistemic injustice, we suggest that patients may be disbelieved, misunderstood or required to translate complex, embodied, and relational experiences into clinical categories that fit poorly. Our point is not that patients' accounts fall outside data, but that all data require interpretation, and patient and caregiver inputs are often treated as lower-status knowledge in decisions about burden, benefit, and value. These risks do not arise uniformly across computational tools: they take different forms in task-specific machine learning systems used for classification or prediction, and in generative AI systems, including large language models, used to process or generate text. AI may deepen the problem by relying on proxies such as cost, utilization, and adherence thereby hardening 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 key question is not only whether AI is accurate or fair, but what patient experiences become visible within it.