ReviewInternational journal of food science2026
Artificial Intelligence in Religious Certification: A Comprehensive Review of Halal Digitalization, Compliance Integrity, and Future Directions.
Review in International journal of food science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Religious Certification: A Comprehensive Review of Halal Digitalization, Compliance Integrity, and Future Directions.International journal of food science · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Global food supply chains continue to expand in scale and complexity, heightening risks of fraud, mislabeling, and cross-contamination across certification systems. These vulnerabilities are particularly consequential for Halal certification, where ingredient permissibility, compliant slaughter practices, hygienic processing, and full traceability are religiously mandated. Artificial intelligence (AI), including machine learning, natural language processing, computer vision (CV), predictive modeling, and sensor-enabled monitoring, offers transformative potential for strengthening the precision, transparency, and efficiency of Halal assurance. Current certification workflows often rely on manual document review, heterogeneous national standards, limited real-time oversight, and susceptibility to fraudulent or unregulated Halal claims. AI-enabled tools address these limitations by facilitating automated ingredient authentication through DNA-based analysis, alcohol threshold detection, enzyme origin classification, and advanced label interpretation. In slaughter and processing environments, CV systems and IoT sensors enable continuous monitoring of animal handling, cut accuracy, bleeding efficiency, and postslaughter segregation, generating objective evidence of compliance. Pilot implementations indicate that integrating AI with blockchain-based traceability and automated data pipelines can reduce certification timelines from weeks to days, improve anomaly detection to near-perfect accuracy, and support dynamic, continuously verifiable compliance rather than periodic audits. Within this context, AI operates effectively under a hybrid governance model in which religious authorities retain interpretive and theological decision-making while AI provides high-resolution, tamper-resistant documentation. Future advancements will depend on harmonized data standards, religious principle-aligned AI governance, strong ethical and cybersecurity safeguards, and wider regulatory acceptance across Islamic jurisdictions. Together, these efforts will enable next-generation Halal certification while preserving religious integrity and sustaining consumer trust.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.