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

被引:2
作者
Yu, Shinae [1 ]
Jeon, Byung Ryul [2 ]
Liu, Changseung [3 ]
Kim, Dokyun [4 ,5 ]
Park, Hae-Il [6 ]
Park, Hyung Doo [7 ]
Shin, Jeong Hwan [8 ]
Lee, Jun Hyung [9 ]
Choi, Qute [10 ]
Kim, Sollip [11 ]
Yun, Yeo Min [12 ]
Cho, Eun-Jung [13 ]
机构
[1] Inje Univ, Haeundae Paik Hosp, Coll Med, Dept Lab Med, Busan, South Korea
[2] Soonchunhyang Univ, Bucheon Hosp, Coll Med, Dept Lab Med & Genet, Bucheon, South Korea
[3] Univ Ulsan, Gangneung Asan Hosp, Coll Med, Dept Lab Med, Kangnung, South Korea
[4] Yonsei Univ, Coll Med, Dept Lab Med, Seoul, South Korea
[5] Yonsei Univ, Res Inst Bacterial Resistance, Coll Med, Seoul, South Korea
[6] Catholic Univ Korea, Coll Med, Dept Lab Med, Seoul, South Korea
[7] Sungkyunkwan Univ, Samsung Med Ctr, Sch Med, Dept Lab Med & Genet, Seoul, South Korea
[8] Inje Univ, Coll Med, Dept Lab Med, Busan, South Korea
[9] GC Labs, Dept Lab Med, Yongin, South Korea
[10] Chungnam Natl Univ, Sejong Hosp, Sch Med, Dept Lab Med, Daejeon, South Korea
[11] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Lab Med, Seoul, South Korea
[12] Konkuk Univ, Med Ctr, Sch Med, Dept Lab Med, Seoul, South Korea
[13] Hallym Univ, Dongtan Sacred Heart Hosp, Coll Med, Dept Lab Med, 7 Keunjaebong Gil, Hwaseong 18450, South Korea
关键词
Artificial intelligence; Big data; Digital medicine; Healthcare; 4.0; Laboratory medicine; QUALITY; PATHOLOGY;
D O I
10.3343/alm.2024.0111
中图分类号
R446 [实验室诊断]; R-33 [实验医学、医学实验];
学科分类号
1001 ;
摘要
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. Thirtytwo 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.
引用
收藏
页码:562 / 571
页数:10
相关论文
共 40 条
[1]   Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process [J].
Abramoff, Michael D. ;
Tobey, Danny ;
Char, Danton S. .
AMERICAN JOURNAL OF OPHTHALMOLOGY, 2020, 214 :134-142
[2]   Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts [J].
Albahra, Samer ;
Gorbett, Tom ;
Robertson, Scott ;
D'Aleo, Giana ;
Ockunzzi, Samuel ;
Lallo, Daniel ;
Hu, Bo ;
Rashidi, Hooman H. .
SEMINARS IN DIAGNOSTIC PATHOLOGY, 2023, 40 (02) :71-87
[3]  
Ansari M, 2023, Journal of Pharmaceutical Negative Results, V14, P1686
[4]   The inclusion of augmented intelligence in medicine: A framework for successful implementation [J].
Bazoukis, George ;
Hall, Jennifer ;
Loscalzo, Joseph ;
Antman, Elliott Marshall ;
Fuster, Valentin ;
Armoundas, Antonis A. .
CELL REPORTS MEDICINE, 2022, 3 (01)
[5]   The Challenges of Implementing Comprehensive Clinical Data Warehouses in Hospitals [J].
Bocquet, Francois ;
Campone, Mario ;
Cuggia, Marc .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (12)
[6]   The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology [J].
Burns, Bethany L. ;
Rhoads, Daniel D. ;
Misra, Anisha .
JOURNAL OF CLINICAL MICROBIOLOGY, 2023, 61 (09)
[7]   Developing a Data-Driven Approach in Order to Improve the Safety and Quality of Patient Care [J].
Cascini, Fidelia ;
Santaroni, Federico ;
Lanzetti, Riccardo ;
Failla, Giovanna ;
Gentili, Andrea ;
Ricciardi, Walter .
FRONTIERS IN PUBLIC HEALTH, 2021, 9
[8]   A New Strategy for Evaluating the Quality of Laboratory Results for Big Data Research: Using External Quality Assessment Survey Data (2010-2020) [J].
Cho, Eun-Jung ;
Jeong, Tae-Dong ;
Kim, Sollip ;
Park, Hyung-Doo ;
Yun, Yeo-Min ;
Chun, Sail ;
Min, Won-Ki .
ANNALS OF LABORATORY MEDICINE, 2023, 43 (05) :425-433
[9]   Present and Future of Utilizing Healthcare Data [J].
Choi, In Young .
HEALTHCARE INFORMATICS RESEARCH, 2023, 29 (01) :1-3
[10]   Bias in Laboratory Medicine: The Dark Side of the Moon [J].
Coskun, Abdurrahman .
ANNALS OF LABORATORY MEDICINE, 2024, 44 (01) :6-20