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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1808026PMC
http://dx.doi.org/10.1371/journal.pcbi.0030012DOI Listing

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Article Synopsis
  • This study looks at how AI (artificial intelligence) can help doctors take care of very sick babies and kids, but it's not used much in real hospitals yet.
  • They reviewed a lot of research and found that most studies are still just testing ideas and have problems with bias, meaning they might not be very reliable.
  • The researchers say we need better plans to connect AI technology from labs to hospitals so it can really help improve patient care.
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From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches.

Hum Vaccin Immunother

December 2023

Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales.

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Objective: Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians' understanding and to promote quality of medical AI research.

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Purpose: Due to the increasing demand for intensive care unit (ICU) treatment, and to improve quality and efficiency of care, there is a need for adequate and efficient clinical decision-making. The advancement of artificial intelligence (AI) technologies has resulted in the development of prediction models, which might aid clinical decision-making. This systematic review seeks to give a contemporary overview of the current maturity of AI in the ICU, the research methods behind these studies, and the risk of bias in these studies.

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