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Points of view on artificial intelligence in medical imaging—one good, one bad, one fuzzy

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Abstract

Studies have shown that the radiology community is still undecided on welcoming artificial intelligence (AI) as part of their workflow. The great features introduced by AI are creating fears that it will outright replace the radiologist. Others are pointing out existing obstacles in AI development to conclude that the radiologist will always have a place within the medical act. This hot debate is generating a large number of publications, where some are abusing the term AI. In this context, the present paper tackles a number of aspects regarding artificial intelligence in radiology trying to illustrate its current role. The current attitude towards this technology within the radiology community is discussed, highlighting some limitations of AI systems implementation and underlining advantages of using AI to assist the work of the radiologist. For instance, while the impact of AI in error reduction due to perceptual and cognitive differences between individual specialists is generally acknowledged, the racial and regional variations in anatomy and pathology as revealed by various imaging studies are not currently considered by AI systems. Certainly, artificial intelligence has several important advantages and research is undergoing to correct drawbacks. There is no doubt that AI will coexist with the radiologist for the foreseeable future.

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Authors and Affiliations

  1. Cancer Research Institute, University of South Australia, Adelaide, SA, 5001, Australia

    Loredana G. Marcu

  2. Faculty of Informatics & Science, University of Oradea, 410087, Oradea, Romania

    Loredana G. Marcu

  3. Faculty of Electrical Engineering & Information Technology, University of Oradea, 410087, Oradea, Romania

    David Marcu

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  1. Loredana G. Marcu
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  2. David Marcu
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Correspondence to Loredana G. Marcu.

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Marcu, L.G., Marcu, D. Points of view on artificial intelligence in medical imaging—one good, one bad, one fuzzy. Health Technol. 11, 17–22 (2021). https://doi.org/10.1007/s12553-020-00515-5

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