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Hendrix, N., Scholten, E., Vernhout, B., Bruijnen, S., Maresch, B., de Jong, M., Diepstraten, S., Bollen, S., Schalekamp, S., de Rooij, M. and Scholtens, A. (2021) Development and Validation of a Convolutional Neural Network for Automated Detection of Scaphoid Fractures on Conventional Radiographs. Radiology: Artificial Intelligence, 3, e200260.
https://doi.org/10.1148/ryai.2021200260

has been cited by the following article:

• TITLE: Artificial Intelligence; a Pragmatic Approach to Implementation in Medicine, a Review of the literature and a Survey of Local Practice in Midlands in UK

  AUTHORS: Neil Capes, Hiran Patel, Islam Sarhan, Neil Ashwood, Andrew Dekker, Ramy Shehata

  KEYWORDS: Artificial Intelligence, Local Practice in Midlands, AI in Medicine

  JOURNAL NAME: International Journal of Intelligence Science, Vol.13 No.3, September 28, 2023

  ABSTRACT: The use of Artificial Intelligence (AI) for clinical pathway management and decision making is believed to improve clinical care and has been used to improve pathways for treatment in most medical disciplines. Methods: A literature review was undertaken to identify the hurdles and steps required to introduce supported clinical decision-making using AI within hospitals. This was supported by a survey of local hospital practice within the Midlands of the United Kingdom to see what systems had been introduced and were functioning effectively. Results: It is unclear how to practically implement systems using AI within medicine easily. Algorithmic medicine based on a set of rules calculated from data only takes a clinician so far to deliver patient centred optimal treatment. AI facilitates a clinician’s ability to assimilate data from disparate sources and can help with some of the analysis and decision making. However, learning remains organic and the subtleties of difference between patients, care providers who exhibit non-verbal communication for instance make it difficult for an AI to capture all the pertinent information required to make the correct clinical decision for any given individual. Hence it assists rather than controls any process in clinical practice. It also must continually renew and adapt considering changes in practise and trends as the goalposts change to meet fluctuations in resources and workload. Precision surgery is benefiting from robotic-assisted surgery in parts driven by AI and being used in 80% of trusts locally. Conclusion: The use of AI in clinical practice remains patchy with it being adopted where research groups have studied a more effective method of monitoring or treatment. The use of robotic-assisted surgery on the other hand has been more rapid as the precision of treatment that this provides appears attractive in improving clinical care.