Beyond accuracy metrics: Toward responsible integration of large language models in pediatric diagnostic reasoning
Masab Mansoor · 2026
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Description
Masab Mansoor writes a 2026 editorial on large language models in children's diagnostic reasoning. His starting point is a study that pitted four models against seventy-eight clinicians on fifty real cases from a Barcelona children's hospital. Half the cases were common conditions and half were rare diseases. Two models, o1-preview and Claude 3.5 Sonnet, beat the clinician group by a wide margin. The edge grew largest on the rare diseases, where diagnosis often takes years.
For rare cases, one model's top-five odds reached six times the clinicians' level. Clinician experience, strikingly, did not predict accuracy across seniority levels. Mansoor argues that such results demand a place for these tools in the diagnostic workflow. He also weighs where the evidence falls short, including the limits of testing on already gathered case data. The piece calls for responsible integration rather than adoption on accuracy scores alone.
Catalog record
| CREATOR | Masab Mansoor |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 9 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |