When machines misread science: creating guardrails for human and AI interpretation of biomedical research
Nicholas Peoples, Ken Milne, Bing Luo, Lijing L Yan · 2026
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Description
Nicholas Peoples and co-authors warn that the first reader of much biomedical research is now a machine, and machines can misread. AI summaries shape how evidence reaches clinicians, while plain-language versions reach the wider public. Errors and over-broad claims at that step can skew care and endanger health.
Rather than wait for smarter AI, the authors argue that science reporting itself should change. Clearer standards for stating samples, limits, and effect sizes would guard both human and machine readers. Their piece sketches guardrails journals could adopt, so findings travel with their caveats attached instead of losing them in summary.
Catalog record
| CREATOR | Nicholas Peoples, Ken Milne, Bing Luo, Lijing L Yan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 27 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |