Literature DB >> 31492414

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers.

Bernardo C Bizzo1, Renata R Almeida1, Mark H Michalski1, Tarik K Alkasab2.   

Abstract

Recent advances in artificial intelligence (AI) are providing an opportunity to enhance existing clinical decision support (CDS) tools to improve patient safety and drive value-based imaging. We discuss the advantages and potential applications that may be realized with the synergy between AI and CDS systems. From the perspective of both radiologist and ordering provider, CDS could be significantly empowered using AI. CDS enhanced by AI could reduce friction in radiology workflows and can aid AI developers to identify relevant imaging features their tools should be seeking to extract from images. Furthermore, these systems can generate structured data to be used as input to develop machine learning algorithms, which can drive downstream care pathways. For referring providers, an AI-enabled CDS solution could enable an evolution from existing imaging-centric CDS toward decision support that takes into account a holistic patient perspective. More intelligent CDS could suggest imaging examinations in highly complex clinical scenarios, assist on the identification of appropriate imaging opportunities at the health system level, suggest appropriate individualized screening, or aid health care providers to ensure continuity of care. AI has the potential to enable the next generation of CDS, improving patient care and enhancing providers' and radiologists' experience.
Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; clinical decision support; machine learning; radiology; structured reporting

Mesh:

Year:  2019        PMID: 31492414     DOI: 10.1016/j.jacr.2019.06.010

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  4 in total

1.  Clinical decision support system, using expert consensus-derived logic and natural language processing, decreased sedation-type order errors for patients undergoing endoscopy.

Authors:  Lin Shen; Adam Wright; Linda S Lee; Kunal Jajoo; Jennifer Nayor; Adam Landman
Journal:  J Am Med Inform Assoc       Date:  2021-01-15       Impact factor: 4.497

Review 2.  Artificial intelligence in oncology: Path to implementation.

Authors:  Isaac S Chua; Michal Gaziel-Yablowitz; Zfania T Korach; Kenneth L Kehl; Nathan A Levitan; Yull E Arriaga; Gretchen P Jackson; David W Bates; Michael Hassett
Journal:  Cancer Med       Date:  2021-05-07       Impact factor: 4.452

3.  Ankle MRI and preceding radiographs: an evaluation of physician ordering practices.

Authors:  Kristopher de Ga; Dylan Noblett; Cyrus Bateni
Journal:  Skeletal Radiol       Date:  2022-06-06       Impact factor: 2.128

4.  Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept.

Authors:  Gabriella Macchia; Gabriella Ferrandina; Stefano Patarnello; Rosa Autorino; Carlotta Masciocchi; Vincenzo Pisapia; Cristina Calvani; Chiara Iacomini; Alfredo Cesario; Luca Boldrini; Benedetta Gui; Vittoria Rufini; Maria Antonietta Gambacorta; Giovanni Scambia; Vincenzo Valentini
Journal:  Front Oncol       Date:  2022-01-03       Impact factor: 6.244

  4 in total

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