Large language models for breast cancer treatment planning: a blinded real-world evaluation of DeepSeek, ChatGPT, and oncologist recommendations
Ming Li, Yiran Yu, Gang Li, Xiaoli Zhang, Yuting Shi, Rila Su · 2026
Read Large language models for breast cancer treatment planning: a blinded real-world evaluation of DeepSeek, ChatGPT, and oncologist recommendations by Ming Li, Yiran Yu, Gang Li, Xiaoli Zhang, Yuting Shi, Rila Su (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/PMC13366216.txt.
Description
Ming Li and co-workers run in 2026 a blind test of two big chat tools on breast cancer care plans, next to past plans by top cancer docs. They use charts of 213 patients, from stage one to four, all stripped of names. Three blind pros grade each plan for right fit and stead.
Both tools beat the past doc plans on grades and vary less. One tool tops the grades with a near full score. Still, tool and doc plans drift far part as stage climbs, most at stage four. The tools may aid care talks, but late stage calls stay a pro job.
Catalog record
| CREATOR | Ming Li, Yiran Yu, Gang Li, Xiaoli Zhang, Yuting Shi, Rila Su |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 36512 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |