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Purpose of Review <p>Food allergies are a significant public health issue, impacting millions worldwide and posing substantial healthcare challenges. The goal of this review is to explore how neural networks can improve food allergy diagnosis, management, and research. Specifically, this paper examines their potential applications in allergen detection, risk prediction, personalized treatment strategies, and epidemiological analysis, while also addressing key challenges and future research directions.</p> Recent Findings <p>Recent advancements in artificial intelligence have demonstrated the potential of neural networks in food allergy science. Neural networks have been applied in diagnosing food allergies through genetic and clinical data analysis, improving allergen detection through imaging and spectroscopy, and personalizing immunotherapy for better treatment outcomes. Additionally, artificial intelligence models are enhancing epidemiological studies by analyzing large-scale datasets to identify trends and risk factors associated with food allergies. However, challenges such as data variability, model interpretability, and ethical concerns remain barriers to widespread clinical adoption.</p> Summary <p>Neural networks are revolutionizing food allergy research by enabling precise diagnostics, advancing personalized treatment approaches, and improving allergen detection techniques. Their ability to process complex datasets and identify subtle patterns holds promise for enhancing patient outcomes and public health interventions. Despite their potential, further research is needed to address data standardization, ethical considerations, and transparency in AI-driven decision-making. By fostering interdisciplinary collaboration, neural networks can play a pivotal role in the future of food allergy science, ultimately improving the quality of life for affected individuals.</p>

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Harnessing Neural Networks for Food Allergy Diagnosis, Management, and Research: A Review of Current Applications and Future Prospects

  • 期刊论文

  • 发表日期：

    2025年6月25日

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  • Henry Sutanto,
  • Deasy Fetarayani

摘要

Purpose of Review

Food allergies are a significant public health issue, impacting millions worldwide and posing substantial healthcare challenges. The goal of this review is to explore how neural networks can improve food allergy diagnosis, management, and research. Specifically, this paper examines their potential applications in allergen detection, risk prediction, personalized treatment strategies, and epidemiological analysis, while also addressing key challenges and future research directions.

Recent Findings

Recent advancements in artificial intelligence have demonstrated the potential of neural networks in food allergy science. Neural networks have been applied in diagnosing food allergies through genetic and clinical data analysis, improving allergen detection through imaging and spectroscopy, and personalizing immunotherapy for better treatment outcomes. Additionally, artificial intelligence models are enhancing epidemiological studies by analyzing large-scale datasets to identify trends and risk factors associated with food allergies. However, challenges such as data variability, model interpretability, and ethical concerns remain barriers to widespread clinical adoption.

Summary

Neural networks are revolutionizing food allergy research by enabling precise diagnostics, advancing personalized treatment approaches, and improving allergen detection techniques. Their ability to process complex datasets and identify subtle patterns holds promise for enhancing patient outcomes and public health interventions. Despite their potential, further research is needed to address data standardization, ethical considerations, and transparency in AI-driven decision-making. By fostering interdisciplinary collaboration, neural networks can play a pivotal role in the future of food allergy science, ultimately improving the quality of life for affected individuals.