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Review
. 2019 Aug;28(1):120-127.
doi: 10.1055/s-0039-1677911. Epub 2019 Aug 16.

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Stefania Montani  1 , Manuel Striani  1
Affiliations
Review

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Stefania Montani et al. Yearb Med Inform. 2019 Aug.

Abstract

Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to "classical" knowledge-based ones, and to consider the issues raised and their possible solutions.

Methods: We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies.

Results: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data.

Conclusions: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

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Conflict of interest statement

Disclosure The authors report no conflicts of interest in this work.

Figures

Fig. 1
Fig. 1
Flow of the review process.
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References

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