Artificial intelligence (AI) has been positioned as being the most important recent advancement in radiology, if not the most potentially disruptive. Singapore radiologists have been quick to embrace this technology as part of the natural progression of the discipline toward a vision of how clinical medicine, empowered by technology, can achieve our national healthcare objectives of delivering value-based and patient-centric care. In this article, we consider 3 core questions relating to AI in radiology, and review the barriers to the widespread adoption of AI in radiology. We propose solutions and describe a “Centaur” model as a promising avenue for enabling the interfacing between AI and radiologists. Finally, we introduce The Radiological AI, Data Science and Imaging Informatics (RADII) subsection of the Singapore Radiological Society. RADII is an enabling body, which together with key technological and institutional stakeholders, will champion research, development and evaluation of AI for radiology applications.
When a patient’s diagnosis is uncertain, diagnostic
radiologists study images created using X-rays, computed
tomography (CT), ultrasound, and magnetic resonance
(MR), to infer disease patterns and identify the most likely
cause of the patient’s signs and symptoms. The medical
specialty of diagnostic radiology has always been greatly
affected by advances in the fields of physics, medicine,
biology and engineering, but is now also increasingly
disrupted by innovations in computer and data sciences.
Over the past few years, there have been abundant and
frequent scholarly publications, news articles, and opinion
pieces published on this subject. Some authors have gone
so far as to predict the demise of diagnostic radiology as
a specialty, if human image interpretation can be replaced
by advanced machine learning (ML) techniques and big
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