ArticleAmerican journal of clinical pathology2021
The Value of Artificial Intelligence in Laboratory Medicine.
Article in American journal of clinical pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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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.
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.
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Who cites it
28 citing papers in PubMed.
- Article
- Artificial intelligence in experimental and clinical in vitro analysis: applications, limitations, and future directions.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
- Rigorous validation of machine learning in laboratory medicine: guidance toward quality improvement.Critical reviews in clinical laboratory sciences · 2025Review
- Psychiatrists' and trainees' knowledge, perception, and readiness for integration of artificial intelligence in mental health care in Nigeria.BMC psychiatry · 2025Article
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Artificial Intelligence and Medical Education (2013-2024): A Scopus-Based Bibliometric Analysis.The East African health research journal · 2025Article
- Artificial Intelligence in Biomedical Sciences: A Scoping Review.British journal of biomedical science · 2025Article
- Lessons for local oversight of AI in medicine from the regulation of clinical laboratory testing.NPJ digital medicine · 2024Article
- Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence.Annals of laboratory medicine · 2024Article
- Primary care physicians' perceptions of artificial intelligence systems in the care of adolescents' mental health.BMC primary care · 2024Article
- Intelligent Liver Function Testing (iLFT): An Intelligent Laboratory Approach to Identifying Chronic Liver Disease.Diagnostics (Basel, Switzerland) · 2024Review
- Article
- Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review.Annals of intensive care · 2024Review
- Impacts of the advancement in artificial intelligence on laboratory medicine in low- and middle-income countries: Challenges and recommendations-A literature review.Health science reports · 2024Article
- Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study.Journal of medical Internet research · 2023Article
- Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response.Diagnostic pathology · 2023Article
- A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare.Cureus · 2023Review
- Opinion research among Russian Physicians on the application of technologies using artificial intelligence in the field of medicine and health care.BMC health services research · 2023Article
- Building the Model.Archives of pathology & laboratory medicine · 2023Review
- Artificial Intelligence for Kidney Stone Spectra Analysis: Using Artificial Intelligence Algorithms for Quality Assurance in the Clinical Laboratory.Mayo Clinic proceedings. Digital health · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAs laboratory medicine continues to undergo digitalization and automation, clinical laboratorians will likely be confronted with the challenges associated with artificial intelligence (AI). Understanding what AI is good for, how to evaluate it, what are its limitations, and how it can be implemented are not well understood. With a survey, we aimed to evaluate the thoughts of stakeholders in laboratory medicine on the value of AI in the diagnostics space and identify anticipated challenges and solutions to introducing AI.
methodsWe conducted a web-based survey on the use of AI with participants from Roche's Strategic Advisory Network that included key stakeholders in laboratory medicine.
resultsIn total, 128 of 302 stakeholders responded to the survey. Most of the participants were medical practitioners (26%) or laboratory managers (22%). AI is currently used in the organizations of 15.6%, while 66.4% felt they might use it in the future. Most had an unsure attitude on what they would need to adopt AI in the diagnostics space. High investment costs, lack of proven clinical benefits, number of decision makers, and privacy concerns were identified as barriers to adoption. Education in the value of AI, streamlined implementation and integration into existing workflows, and research to prove clinical utility were identified as solutions needed to mainstream AI in laboratory medicine.
conclusionsThis survey demonstrates that specific knowledge of AI in the medical community is poor and that AI education is much needed. One strategy could be to implement new AI tools alongside existing tools.
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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.