Evidence map›Paper›PMID 40176862›Full record

ReviewBioMedicine2025

Mini-review of clinical data service platforms in the era of artificial intelligence: A case study of the iHi data platform.

Yu-Ting Lin, Ya-Chi Lin, Hung-Lin Chen, Che-Chen Lin, Min-Yen Wu, Sheng-Hsuan Chen, Zi-Han Lin, Yi-Ching Chang, Chuan-Hu Sun, Sheng-Ya Lu and 7 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. 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.

Yu-Ting Lin *Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Ya-Chi Lin *Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Hung-Lin ChenBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Che-Chen LinBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Min-Yen WuBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Sheng-Hsuan ChenBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Zi-Han LinBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Yi-Ching ChangBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Chuan-Hu SunBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Sheng-Ya LuBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Min-Yu ChiangBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Hui-Chao TsaiBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Mei-Ju ShihBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
David Ray ChangDivision of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.
Fuu-Jen TsaiDepartment of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Hsiu-Yin ChiangBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Chin-Chi KuoBig Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the past two decades, healthcare organizations have transitioned from the early stages of digitization and digitalization to a more comprehensive process of digital transformation, a shift significantly accelerated by the advent of artificial intelligence (AI). Consequently, the development of high-quality clinical data warehouses, derived from electronic health records (EHRs) and enriched with multidomain data, such as genomics, proteomics, and Internet of Things (IoT) information, has become essential for the creation of the modern patient digital twin (PDT). This approach is critical for leveraging AI in the evolving landscape of clinical practice. Leading medical centers and healthcare institutions have adopted this model, as summarized in this review. Since 2020, China Medical University Hospital (CMUH) has been constructing its data ecosystem by integrating EHRs with extensive genomic databases. This initiative has led to the development of a data service platform, the ignite Hyper-intelligence (iHi®) platform. The iHi platform serves as a case study exemplifying the workflow of the smart data chip, which facilitates the deep cleaning and reliable de-identification of clinical data while incorporating analytical platforms related to genomics and the microbiome to enhance insight extraction processes. The ability to predict complex interactions and disease trajectories among PDTs, digital counterparts of healthcare professionals, and virtual socioeconomic environments will be pivotal in advancing personalized healthcare and optimizing patient outcomes. Future challenges will involve the unification of cross-institutional data platforms and ensuring the interoperability of AI inferences-key factors that will define the next era of AI-driven healthcare.

Indexed as

Artificial intelligenceData ecosystemData platformsDigital twiniHi platform

Identifiers

PMID40176862
PMCPMC11959964

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.