Evidence map›Paper›PMID 38953115›Full record

ArticleAnnals of laboratory medicine2024

Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence.

Shinae Yu, Byung Ryul Jeon, Changseung Liu, Dokyun Kim, Hae-Il Park, Hyung Doo Park, Jeong Hwan Shin, Jun Hyung Lee, Qute Choi, Sollip Kim and 3 more

Abstract read
In one paragraph

Article in Annals of laboratory medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Guideline
  2. Article
  3. Article
  4. Review
  5. Advances in laboratory medicine · 2025
    Article
  6. 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

13 authors.

Shinae YuDepartment of Laboratory Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea.
Byung Ryul JeonDepartment of Laboratory Medicine & Genetics, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea.
Changseung LiuDepartments of Laboratory Medicine, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea.
Dokyun KimDepartment of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul, Korea.
Hae-Il ParkDepartment of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Hyung Doo ParkDepartment of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Jeong Hwan ShinDepartment of Laboratory Medicine, Inje University College of Medicine, Busan, Korea.
Jun Hyung LeeDepartment of Laboratory Medicine, GC Labs, Yongin, Korea.
Qute ChoiDepartment of Laboratory Medicine, Chungnam National University Sejong Hospital, Chungnam National University School of Medicine, Daejeon, Korea.
Sollip KimDepartment of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Yeo Min YunDepartment of Laboratory Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.
Eun-Jung ChoDepartment of Laboratory Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea.
Evidence-Based Laboratory Medicine Committee of the Korean Society for Laboratory Medicine

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM). Methods: A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions). Results: In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles. Conclusions: This survey highlighted KSLM members' awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies.

Indexed as

Artificial IntelligenceBig DataAdultDelivery of Health CareFemaleHumansMaleMiddle AgedRepublic of KoreaSurveys and QuestionnairesArtificial intelligenceBig dataDigital medicineHealthcare 4.0Laboratory medicine

Identifiers

PMID38953115
PMCPMC11375187

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.