Rehan Babar
Ubiquitous computational power. Faster Data processing.
Rapid progress of analytic techniques. We are amid
major changes all around us and they are happening at an
exponential pace. Artificial Intelligence (AI) – which aims
to mimic human cognitive functions – is bringing a paradigm
shift to the field of radiology. In the last decade, AI
techniques known as deep learning have delivered rapidly
improving performance in image recognition, caption
generation, and speech recognition. Further implementation
of AI in radiology will significantly improve the
quality, value, and depth of radiology’s contribution to
patient care and revolutionize radiologists’ workflows.
However, recent reports of health information technology
(IT) show that the acceptance between purchased
technologies and clinical work systems is critical in determining
intended end users to accept or reject the technology,
to use or to misuse it, or to incorporate it into their
clinical workflows or work around it. This paper assesses
technology implementation frameworks in the context of
AI in radiology and employs a widely accepted and validated
technology acceptance framework - the Technology
Acceptance Model (TAM). The model is built on the
premise that when an end-user is introduced to a technology,
there are constructs and relationships that influence
when and how a user will interact with the technology.
In addition, the findings can further inform and provide
guidance for policymakers, AI application developers,
and business management on the educational needs of
radiologists, research and development, and the role of
radiologists in moving forward with AI in radiology.
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