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Artif Intell Med. 2015 Sep;65(1):61-73. doi: 10.1016/j.artmed.2015.07.003. Epub 2015 Jul 29.

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes.

Author information

1
Health e-Research Centre, Institute of Population Health, University of Manchester, Vaughan House, Portsmouth Street, Manchester M13 9GB, UK. Electronic address: niels.peek@manchester.ac.uk.
2
Department of Computer Science, University of Verona, Ca'Vignal 2, strada le Grazie 15, 37134 Verona, Italy. Electronic address: carlo.combi@univr.it.
3
Department of Information Engineering and Communications, University of Murcia, Campus de Espinardo, 30100 Espinardo (Murcia), Spain. Electronic address: roquemm@um.es.
4
Dipartimento di Ingegneria Industriale e dell'Informazione, University of Pavia, Via Ferrata 1, 27100 Pavia, Italy. Electronic address: riccardo.bellazzi@unipv.it.

Abstract

BACKGROUND:

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998.

OBJECTIVES:

To review the history of AIME conferences, investigate its impact on the wider research field, and identify challenges for its future.

METHODS:

We analyzed a total of 122 session titles to create a taxonomy of research themes and topics. We classified all 734 AIME conference papers published between 1985 and 2013 with this taxonomy. We also analyzed the citations to these conference papers and to 55 special issue papers.

RESULTS:

We identified 30 research topics across 12 themes. AIME was dominated by knowledge engineering research in its first decade, while machine learning and data mining prevailed thereafter. Together these two themes have contributed about 51% of all papers. There have been eight AIME papers that were cited at least 10 times per year since their publication.

CONCLUSIONS:

There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. AIME papers relating to guidelines and protocols are among the most highly cited.

KEYWORDS:

Artificial Intelligence in Medicine; History of science; Literature review

PMID:
26265491
DOI:
10.1016/j.artmed.2015.07.003
[Indexed for MEDLINE]

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