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

Author:

Borna Sahar1ORCID,Barry Barbara A.2,Makarova Svetlana3,Parte Yogesh3,Haider Clifton R.4,Sehgal Ajai3ORCID,Leibovich Bradley C.35,Forte Antonio Jorge13ORCID

Affiliation:

1. Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA

2. Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 55905, USA

3. Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA

4. Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA

5. Department of Urology, Mayo Clinic, Rochester, MN 55905, USA

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.

Funder

Center for Digital Health at Mayo Clinic, Noaber Foundation

Publisher

MDPI AG

Reference65 articles.

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5. Mookiah, L., and Eberle, W. (2016, January 15–17). Co-Ranking Authors in Heterogeneous News Networks. Proceedings of the 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016, Las Vegas, NV, USA.

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