Development of Fuzzy Expert System for Peer Review Biomedical Research Proposals

Priya Shantusha  GopalRai; Azam Orooji; Banafshe Maghsoudi

doi:10.22034/AIMI.2026.20.12

Authors

Priya Shantusha GopalRai Clinical Informatician, Representative Quan Techno Support, The Netherlands
Azam Orooji Assistant Professor of Medical Informatics, Department of Medical Biotechnology, School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran Corresponding Author
https://orcid.org/0000-0002-2773-6179
a.orooji.98@gmail.com
Banafshe Maghsoudi MSc in Medical Informatics, Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

DOI:

https://doi.org/10.22034/AIMI.2026.20.12

Keywords:

Fuzzy Logic, Expert system, peer-review

Abstract

Introduction: The quality of review processes used to evaluate research proposals directly reflects on the soundness of the research data and the new knowledge that are disseminated to the scientific community. In today's rapidly evolving academic landscape, the peer-review process remains a cornerstone for evaluating the merit and integrity of research proposals. However, the traditional peer-review system often grapples with challenges such as reviewer bias, inconsistency, and the growing volume of submissions. To address these issues, this paper presents the development of a fuzzy expert system designed to enhance the peer-review process for biomedical research proposals.

Material and Methods: This cross-sectional study aimed to design a fuzzy evaluation system for research proposals in four steps. Initially, a questionnaire was developed based on literature review, existing review forms, and interviews with experts. Then, this questionnaire was distributed among research council members of several universities to assign weights and rank the parameters. Each parameter was rated on a scale from 1 to 5. In the third phase, the fuzzy rule base was designed based on expert knowledge and the system was implemented in MatLab. Finally, the system was evaluated using collected review forms from research proposals.

Results: The questionnaire, containing 35 items, was distributed to the research council members of Iran, Tehran and Shahid Beheshti University of Medical Sciences. A total of 27 members (response rate: 77.1%) completed and returned the questionnaire. Six factors, including cost, objectives, study type, literature review, statistical methods, and details of findings, were considered as input variables for the system, with acceptance, revision, and rejection as output results. The system had 90% accuracy and an AUC of 0.901, showing good performance.

Conclusion: The fuzzy expert system enhances peer-review by reducing subjectivity, ensuring logical, transparent, consistent, accurate, and defendable biomedical proposal assessments.

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