ReviewArchives of Razi Institute2025
The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review.
Review in Archives of Razi Institute, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Nutritional Strategies to Mitigate Heat Stress in Cattle: A Narrative Review.Microorganisms · 2026Review
- The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review.Archives of Razi Institute · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of Machine Learning (ML) and Artificial Intelligence (AI) into animal biotechnology is revolutionizing the field, particularly in developing countries where agriculture and livestock play significant roles in the economy. AI and ML enable more efficient data analysis in areas such as genetic optimization, disease prediction, and livestock management, improving both productivity and sustainability. With the growing availability of data, AI-driven models can process large volumes of information from diverse sources such as environmental conditions, genetic markers, and health records, offering more precise insights than traditional methods. Recent advancements include AI-powered diagnostic systems for detecting and managing disease outbreaks, which allow for faster response times and more targeted interventions, ultimately reducing economic losses. Enhanced breeding techniques, now, leverage machine learning algorithms to predict desirable genetic traits, enabling farmers to make data-driven breeding choices. Feed efficiency improvements, another critical area, benefit from AI's ability to analyze nutrient requirements and optimize feeding schedules based on individual animal needs, reducing waste and costs. Additionally, AI is increasingly applied to animal health monitoring, using tools such as sound-based systems and piezoelectric sensors embedded in smart collars to track behaviors indicative of health issues. In the dairy sector, AI models assess health risks like nitrate contamination in milk, contributing to safer food production and improving public health. In genetic studies, AI enhances selective breeding, improving traits such as growth and disease resistance. This manuscript reviews the transformative role of AI and ML in animal biotechnology, focusing on developing regions where resource optimization is crucial. By simplifying complex techniques and providing step-by-step tutorials, this work aims to equip researchers and practitioners with practical tools to harness AI in animal biotechnology.
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.