History of disruptions in laboratory medicine: what have we learned from predictions?

@article{Kricka2019HistoryOD,
  title={History of disruptions in laboratory medicine: what have we learned from predictions?},
  author={Larry Jan Kricka},
  journal={Clinical Chemistry and Laboratory Medicine (CCLM)},
  year={2019},
  volume={57},
  pages={308 - 311}
}
  • L. Kricka
  • Published 21 June 2018
  • Medicine
  • Clinical Chemistry and Laboratory Medicine (CCLM)
Abstract Predictions about the future of laboratory medicine have had a mixed success, and in some instances they have been overambitious and incorrectly assessed the future impact of emerging technologies. Current predictions suggest a more highly automated and connected future for diagnostic testing. The central laboratory of the future may be dominated by more robotics and more connectivity in order to take advantage of the benefits of the Internet of Things and artificial intelligence (AI… 
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References

SHOWING 1-10 OF 40 REFERENCES
The role of technology in the clinical laboratory of the future.
  • D. Wilkinson
  • Medicine
    Clinical laboratory management review : official publication of the Clinical Laboratory Management Association
  • 1997
TLDR
Five technologies which will have the greatest impact on the practice of laboratory medicine during the next decade include molecular diagnostics, near patient testing, image analysis, robotics, and information management.
The role of technology in the clinical laboratory of the future.
TLDR
Five technologies which will have the greatest impact on the practice of laboratory medicine during the next decade include molecular diagnostics, near patient testing, image analysis, robotics, and information management.
The future of laboratory medicine - a 2014 perspective.
18 Pathology 2026: The Future of Laboratory Medicine and Academic Pathology
TLDR
A SWOT analysis of laboratory medicine and academic pathology in the year 2006 is carried out to analyse where the authors as a discipline are now and how they are perceived by their medical/scientifi c colleagues, the public at large and their political and governmental masters.
Medicine unplugged: the future of laboratory medicine.
TLDR
This work examines recent advancements in mobile diagnostics enabled by microfluidics and LOC technologies for POC clinical testing to create artificial-intelligence and decision support for patients and the medical community.
Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm
TLDR
This work uses Digital Twins as the hypothesis that one would be in the possession of very detailed bio-physical and lifestyle information of a person over time, and redefines the concept of ‘normality’ or ‘health’ as a set of patterns that are regular for a particular individual, against the backdrop of patterns observed in the population.
Technology that will initiate future revolutionary changes in healthcare and the clinical laboratory
  • R. Nakamura
  • Medicine
    Journal of clinical laboratory analysis
  • 1999
TLDR
The clinical laboratory will soon be able to provide powerful new molecular diagnostic tools along with multianalytic assays for expression of genes and proteins in different patterns of diseases, disease progression, and predisposition to diseases.
Precision Medicine Research in the Million-Genome Era
TLDR
The core articles include: Precision Medicine Research in the Million-Genome Era; Utilizing Machine-Learning Capabilities; NGS Big Data Issues for Biomanufacturing; and Visualization for Advanced Big Data Analysis.
Dermatologist-level classification of skin cancer with deep neural networks
TLDR
This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
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