Evidence map›Paper›PMID 42413501›Full record

ArticleCell reports. Medicine2026

From generalization to precision: A large domain-specific pretrained model for specialized medical tasks.

Zhongwen Li, Yangyang Wang, Lei Wang, Yiyang Li, Zhouqian Wang, Yibing Li, Pengyao Zhang, Donghua Qi, Liheng Xiu, Daoyuan Li and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Zhongwen LiNingbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China. Electronic address: li.zhw@qq.com.
Yangyang WangNingbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China.
Lei WangNational Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
Yiyang LiNingbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China.
Zhouqian WangNingbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China.
Yibing LiNingbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China.
Pengyao ZhangNingbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China.
Donghua QiDaqing Eye Hospital, Daqing 163711, China.
Liheng XiuDepartment of Ophthalmology, West China Second University Hospital, Sichuan University, Chengdu 610041, China.
Daoyuan LiDepartment of Ophthalmology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China.
Yahui XiAnkang Central Hospital, Xi'an Medical University, Xi'an 725000, China.
Yingyan PengHunan Province Directly Affiliated TCM Hospital, Zhuzhou 412000, China.
Shiqi YinNingbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China.
Jiewei JiangSchool of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China. Electronic address: jiangjiewei@xupt.edu.cn.
Kai JinNingbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; Eye Center of Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China; Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310058, China. Electronic address: jinkai@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Foundation models are recognized for their broad generalizability across diverse tasks, which has driven their widespread adoption. In medicine, however, precision is often paramount, particularly in the era of precision medicine, where diagnostic and therapeutic decisions demand exceptional accuracy. In this study, we investigate whether a large domain-specific pretrained model can outperform general-purpose foundation models in specialized medical tasks based on ultra-widefield fundus (UWF) imaging. We develop USPEC, a large-scale domain-specific model pretrained on 875,947 unlabeled UWF images using self-supervised learning, and subsequently adapt it to downstream tasks with explicit labels. USPEC consistently outperforms the foundation models (RETFound, RETFound-DE, VisionFM, RetiZero, and EyeFound) across multiple UWF-based diagnostic and few-shot learning tasks, while also demonstrating superior label and training efficiency. Its robustness is further confirmed on independent datasets from different countries. These findings support a large domain-specific pretrained model as a powerful paradigm for advancing artificial intelligence (AI) performance in specialized medical fields.

Indexed as

Precision MedicineAlgorithmsArtificial IntelligenceFundus OculiHumansartificial intelligencedomain-specific modelfoundation modelfundus diseasesultra-widefield fundus image

Identifiers

PMID42413501
PMCPMC13522753

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.