ArticleDiagnostics (Basel, Switzerland)2021
Deep into Laboratory: An Artificial Intelligence Approach to Recommend Laboratory Tests.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed.
- Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography.Echo research and practice · 2026Review
- Why health recommender systems struggle to reach clinical practice: A lifecycle-oriented systematic review.iScience · 2026Article
- AI-enabled proactive health education: perceptions of public health students - a qualitative study.BMC medical education · 2026Article
- Revolutionizing Laboratory Practices: Pioneering Trends in Total Laboratory Automation.Annals of laboratory medicine · 2025Review
- Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2025Review
- Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine.Frontiers in digital health · 2024Review
- Artificial Intelligence in Dementia: A Bibliometric Study.Diagnostics (Basel, Switzerland) · 2023Article
- The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update.Cancers · 2023Review
- Artificial intelligence and thyroid disease management: considerations for thyroid function tests.Biochemia medica · 2022Review
- Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence-A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- Laboratory Demand Management Strategies-An Overview.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Laboratory tests are performed to make effective clinical decisions. However, inappropriate laboratory test ordering hampers patient care and increases financial burden for healthcare. An automated laboratory test recommendation system can provide rapid and appropriate test selection, potentially improving the workflow to help physicians spend more time treating patients. The main objective of this study was to develop a deep learning-based automated system to recommend appropriate laboratory tests. A retrospective data collection was performed at the National Health Insurance database between 1 January 2013, and 31 December 2013. We included all prescriptions that had at least one laboratory test. A total of 1,463,837 prescriptions from 530,050 unique patients was included in our study. Of these patients, 296,541 were women (55.95%), the range of age was between 1 and 107 years. The deep learning (DL) model achieved a higher area under the receiver operating characteristics curve (AUROC micro = 0.98, and AUROC macro = 0.94). The findings of this study show that the DL model can accurately and efficiently identify laboratory tests. This model can be integrated into existing workflows to reduce under- and over-utilization problems.
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Registered trials
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.