Performance of large language models for extracting clinical data from breast cancer pathology reports: a systematic review
Ravi Shankar, Vahul Sundar, Ziyu Goh, Pei Yi Sin, Ern Yu Tan · 2026
Read Performance of large language models for extracting clinical data from breast cancer pathology reports: a systematic review by Ravi Shankar, Vahul Sundar, Ziyu Goh, Pei Yi Sin, Ern Yu Tan (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-13473680.txt.
Description
Ravi Shankar and co-authors review language models reading breast cancer reports in a 2026 paper. Pathology reports hold vital data in free text. Manual extraction is slow and error-prone at hospital scale.
Seven databases were searched under standard review guidance. Two reviewers screened studies and pulled data independently. Nine studies covering about fourteen thousand reports qualified. Over thirty model designs were evaluated across those studies. Best results and reporting quality are summarized for each. Quality tools built for prediction models graded the work. Readers see how close hospitals are to trusting machines with report data.
Catalog record
| CREATOR | Ravi Shankar, Vahul Sundar, Ziyu Goh, Pei Yi Sin, Ern Yu Tan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 59 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |