Evidence mapPaperPMID 40712809Full record

ReviewJournal of biomedical informatics2025

A review on knowledge graphs for healthcare: Resources, applications, and promises.

Hejie Cui, Jiaying Lu, Ran Xu, Shiyu Wang, Wenjing Ma, Yue Yu, Shaojun Yu, Xuan Kan, Chen Ling, Liang Zhao and 7 more

Abstract readReview
In one paragraph

Review in Journal of biomedical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Large Language Models in Bio-Ontology Research: A Review.Bioengineering (Basel, Switzerland) · 2025
    Review
  11. Article
  12. Article
  13. Article
  14. Article
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

17 authors.

Hejie CuiDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Jiaying LuCenter for Data Science, School of Nursing, Emory University, Atlanta, GA, USA.
Ran XuDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Shiyu WangDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Wenjing MaDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Yue YuSchool of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Shaojun YuDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Xuan KanDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Chen LingDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Liang ZhaoDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Zhaohui S QinDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Joyce C HoDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Tianfan FuDepartment of Computational Science, Rensselaer Polytechnic Institute, Troy, NY, USA.
Jing MaDepartment of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
Mengdi HuaiDepartment of Computer Science, Iowa State University, Ames, IA, USA.
Fei WangDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, Ithaca, NY, USA.
Carl YangDepartment of Computer Science, Emory University, Atlanta, GA, USA. Electronic address: j.carlyang@emory.edu.

Funding

SCH: Visual explanation-guided learning for human-AI collaborative abdominal cancer diagnostic imagingR01CA297856 · EMORY UNIVERSITY · 2025 to 2025
$291k
Understanding Diabetes Heterogeneity via Mining Multimodality Interconnected DataK25DK135913 · EMORY UNIVERSITY · 2025 to 2025
$171k
NCI NIH HHS R01 CA297856NIDDK NIH HHS K25 DK135913
6 · The paper itself

Abstract

objectiveThis comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains.

methodsWe thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications in basic science research, pharmaceutical research and development, clinical decision support, and public health. The review encompasses both model-free and model-based utilization approaches and the integration of HKGs with large language models (LLMs).

resultsWe searched Google Scholar for relevant papers on HKGs and classified them into the following topics: HKG construction, HKG utilization, and their downstream applications in various domains. We also discussed their special challenges and the promise for future work. DISCUSSION: The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice by integrating vast amounts of biomedical knowledge from multiple domains. The synergy between HKGs and LLMs offers promising opportunities for constructing more comprehensive knowledge graphs and improving the accuracy of healthcare applications.

conclusionsHKGs have emerged as a powerful tool for structuring medical knowledge, with broad applications across biomedical research, clinical decision-making, and public health. This survey serves as a roadmap for future research and development in the field of HKGs, highlighting the potential of combining knowledge graphs with advanced machine learning models for healthcare transformation.

Indexed as

Computer GraphicsDelivery of Health CareMedical InformaticsBiomedical ResearchHumansMachine LearningHealthcareInterpretable AIKnowledge graphlanguage modelsMultimodality

Identifiers

PMID40712809
PMCPMC12995551

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

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 Socratic graph.