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

• Published in: European Journal of Investigation in Health, Psychology and Education (EJIHPE) Vol. 14; no. 5; pp. 1182 - 1196
• Main Authors: Borna, Sahar, Barry, Barbara A, Makarova, Svetlana, Parte, Yogesh, Haider, Clifton R, Sehgal, Ajai, Leibovich, Bradley C, Forte, Antonio Jorge
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.05.2024
• Subjects: Algorithms; Artificial intelligence; Computational linguistics; Data mining; Electronic health records; Eligibility; expert finding; expert identification; Expertise; Identification; Information Seeking; knowledge management; Labeling; language model; Language Processing; Machine learning; Medical research; Medical Services; Meta Analysis; Natural language interfaces; Natural language processing; Referral; Search Strategies; Semantics; Semiotics; Social Networks; Word Frequency; expert identification; language model; expert finding; knowledge management; machine learning; artificial intelligence
• ISSN: 2254-9625; 2174-8144; 2254-9625
• DOI: 10.3390/ejihpe14050078

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.