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. 2024 Apr 28;14(5):1182-1196.
doi: 10.3390/ejihpe14050078.

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

Sahar Borna  1 , Barbara A Barry  2 , Svetlana Makarova  3 , Yogesh Parte  3 , Clifton R Haider  4 , Ajai Sehgal  3 , Bradley C Leibovich  3   5 , Antonio Jorge Forte  1   3
Affiliations

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

Sahar Borna et al. Eur J Investig Health Psychol Educ. .

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.

Keywords: artificial intelligence; expert finding; expert identification; knowledge management; language model; machine learning.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Advanced capabilities of AI compared to conventional algorithms. AI rapidly processes diverse data, including medical records and research, enabling quick expert identification through their contributions and impact. Advanced NLP in AI interprets complex medical language, and AI’s pattern recognition identifies emerging experts by analyzing data patterns and citation networks.
Figure 2
Figure 2
PRISMA flow diagram. Study selection process. The diagram outlines steps from identifying to including studies, showing record filtration, and reasons for exclusion.
Figure 3
Figure 3
Utilizing artificial intelligence in the healthcare system to find the most expert specialist for patient referrals. The AI system streamlines the process by identifying and categorizing top specialists, ensuring patients are quickly matched with the most appropriate physician. This approach enhances efficiency, reduces wait times, and minimizes misallocations, leading to a more effective and less confusing healthcare experience.
Figure 4
Figure 4
Medical expert finding using artificial intelligence.
See this image and copyright information in PMC

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