CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 027-034
DOI: 10.1055/s-0039-1677899
Special Section: Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications
Working Group Contributions
Georg Thieme Verlag KG Stuttgart

The Interplay of Knowledge Representation with Various Fields of Artificial Intelligence in Medicine

A Contribution from the IMIA Working Group on Language and Meaning in BioMedicine
Laszlo Balkanyi
1  Knowledge Manager, European Centre of Disease Prevention and Control (retired)
,
Ronald Cornet
2  Associate Professor, Department of Medical Informatics, Academic Medical Center - University of Amsterdam, Amsterdam Public Health research institute, Amsterdam, The Netherlands
› Author Affiliations
Further Information

Publication History

Publication Date:
25 April 2019 (online)

  

Summary

Introduction: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR).

Methods: We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts.

Results: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated.

Conclusions: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.