ABSTRACT NUMBER: ESOC2026A291 PATIENT PERSPECTIVES ON MACHINE LEARNING TOOLS FOR OUTCOME PREDICTION AND DECISION-MAKING IN INTRACEREBRAL HAEMORRHAGE: A MIXED-METHODS STUDY
Alexandra Hurden, Menglu Ouyang, Leibo Liu, Xiaoying Chen, Craig Anderson · 2026
Read ABSTRACT NUMBER: ESOC2026A291 PATIENT PERSPECTIVES ON MACHINE LEARNING TOOLS FOR OUTCOME PREDICTION AND DECISION-MAKING IN INTRACEREBRAL HAEMORRHAGE: A MIXED-METHODS STUDY by Alexandra Hurden, Menglu Ouyang, Leibo Liu, Xiaoying Chen, Craig Anderson (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13145929.txt.
Description
Alexandra Hurden and co-authors asked stroke survivors how they feel about machine learning tools that predict outcomes after a brain bleed. Such tools could help doctors decide care for intracerebral hemorrhage, but patient trust is untested ground. The team surveyed 186 people who had lived through the condition and ran focus groups with nine more.
Most patients, about four in five, were comfortable with software supporting their clinicians' predictions. Their worries centered on accuracy, on whether the training data stay current and relevant, and on how much they themselves understand about AI. About one in five objected to their medical data feeding model training. Patients suggested fixes: publish proof of performance, involve clinicians in design, and explain the tool plainly to families. The authors say acceptance is there, but it must be earned case by case.
Catalog record
| CREATOR | Alexandra Hurden, Menglu Ouyang, Leibo Liu, Xiaoying Chen, Craig Anderson |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 2 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |