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Artificial Intelligence Algorithms for Expert Identification in Medical Domains: A Scoping Review.

Authors :
Borna S
Barry BA
Makarova S
Parte Y
Haider CR
Sehgal A
Leibovich BC
Forte AJ
Source :
European journal of investigation in health, psychology and education [Eur J Investig Health Psychol Educ] 2024 Apr 28; Vol. 14 (5), pp. 1182-1196. Date of Electronic Publication: 2024 Apr 28.
Publication Year :
2024

Abstract

With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval.

Details

Language :
English
ISSN :
2254-9625
Volume :
14
Issue :
5
Database :
MEDLINE
Journal :
European journal of investigation in health, psychology and education
Publication Type :
Academic Journal
Accession number :
38785576
Full Text :
https://doi.org/10.3390/ejihpe14050078