A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence
Zichen Ye, Yue Chen, Xuefeng Huang, Manman Chen, Wanzhou Wang, He Zhu, Keyu Han, Jiahui Wang, Qu Lu, Yuankai Zhao, Yimin Qu, Guanghan Gao, Yu Jiang · 2026
Read A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence by Zichen Ye, Yue Chen, Xuefeng Huang, Manman Chen, Wanzhou Wang, He Zhu, Keyu Han, Jiahui Wang, Qu Lu, Yuankai Zhao, Yimin Qu, Guanghan Gao, Yu Jiang (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-13545083.txt.
Description
Zichen Ye and twelve co-authors present a 2026 paper proposing a five phase framework for judging medical AI systems. AI tools for diagnosis and prediction have raced ahead, yet the path from lab scores to proven patient benefit is patchy. Most checks still rest on past data and algorithm centered measures.
Their framework walks a model through five gates: technical soundness, toughness in operation, safe interaction with users, clinical proof, and fit into real world care. Gates come with pass criteria, and the design allows loops back when a system changes. The aim is a shared yardstick for hospitals, makers, and regulators.
Catalog record
| CREATOR | Zichen Ye, Yue Chen, Xuefeng Huang, Manman Chen, Wanzhou Wang, He Zhu, Keyu Han, Jiahui Wang, Qu Lu, Yuankai Zhao, Yimin Qu, Guanghan Gao, Yu Jiang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 140 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |