Evidence map›Paper›PMID 42556343›Full record

ArticleCell reports. Medicine2026

An autonomous multimodal AI agent for evidence-grounded ophthalmic diagnosis.

Kaikai Zhao, Qixuan Sun, Daohuan Kang, Tao Yu, Wenzheng Han, Rui Yao, Rupesh Agrawal, Gui-Shuang Ying, Andrzej Grzybowski, Kai Jin

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

10 authors.

Kaikai ZhaoEye Center of Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China; School of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology, Xuzhou, China.
Qixuan SunDepartment of Biomedical Engineering, Zhejiang University, Hangzhou, China.
Daohuan KangDepartment of Ophthalmology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents' Health and Diseases, Hangzhou, Zhejiang, China.
Tao YuEye Center of Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China.
Wenzheng HanThe First Affiliated Hospital, Wannan Medical College, Wuhu, Anhui, China.
Rui YaoSchool of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology, Xuzhou, China.
Rupesh AgrawalNational Healthcare Group Eye Institute, Tan Tock Seng Hospital, Tan Tock Seng, Singapore, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore; Singapore Eye Research Institute, Singapore, Singapore; Duke-NUS Medical School, Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Gui-Shuang YingCenter for Preventive Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Andrzej GrzybowskiInstitute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland; Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.
Kai JinEye Center of Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China. Electronic address: jinkai@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multimodal ophthalmic diagnosis requires integrating fundus photography, B-scan ultrasonography, and medical evidence, yet most artificial intelligence (AI) systems remain single-task or weakly grounded. AgentEYE is an auditable multimodal agent that routes ocular images to specialized fundus and B-scan tools, retrieves guideline/web evidence, and synthesizes evidence-grounded reports. In a 302-case internal benchmark, AgentEYE shows higher diagnostic correctness and completeness than large language model (LLM)-only baselines and an ablation without specialized imaging tools; performance remains similar to the no-retrieval ablation, indicating that retrieval mainly supports evidence grounding and citation auditability. Blinded evaluation of 200 cases by three ophthalmologists confirms improved diagnostic correctness, completeness, safety, and citation grounding versus an LLM-only self-citation baseline. External analyses show distribution-dependent performance. These findings support AgentEYE as a traceable decision-support prototype requiring prospective multicenter validation.

Indexed as

Artificial IntelligenceEye DiseasesHumansLarge Language ModelsUltrasonographyagentic AIautonomous AI agentsclinical decision supportevidence-grounded diagnosisexplainable medical AIfundus photographyguideline-based retrievallarge language modelsmultimodal ophthalmic imagingophthalmic ultrasonography

Identifiers

PMID42556343
PMCPMC13522791

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.