Volume 12, Issue 3 (12-2025)
jhbmi 2025, 12(3): 277-293 |
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Falahatipour S K, Soltani M, Karami-Mohajeri S, Ghasemloo A, Khanamani Falahati-pour S, Khanamani Falahatipour S et al . A Narrative Critical Review of AI Innovations in Toxicity Prediction and Personalized Healthcare. jhbmi 2025; 12 (3) :277-293
URL: http://jhbmi.ir/article-1-935-en.html
URL: http://jhbmi.ir/article-1-935-en.html
Sakineh Khanamani Falahatipour 
, Motahareh Soltani , Somayyeh Karami-Mohajeri , Amirreza Ghasemloo , Soudeh Khanamani Falahati-pour , Salimeh Khanamani Falahatipour , Zohreh Oghabian *
, Motahareh Soltani , Somayyeh Karami-Mohajeri , Amirreza Ghasemloo , Soudeh Khanamani Falahati-pour , Salimeh Khanamani Falahatipour , Zohreh Oghabian *
Assistant Professor, Pharmaceutical Research Center, Institute of Neuropharmacology, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran
Abstract: (20 Views)
Introduction: Predictive toxicology utilizing artificial intelligence (AI) enables rapid, accurate, and cost-effective assessment of the toxicity of chemicals, pharmaceuticals, and environmental pollutants. By reducing reliance on traditional in vitro and animal testing, this field plays a critical role in drug development, personalized medicine, and environmental health. This study reviews recent advances in AI applications in predictive toxicology and examines the associated opportunities and challenges.
Method: This narrative critical review was conducted through a systematic search of scientific databases, including PubMed, Scopus, ScienceDirect, and Google Scholar. Keywords such as "Artificial Intelligence", "Predictive Toxicology," and "Personalized Medicine" were used to identify relevant articles. Articles were selected based on inclusion criteria (focusing on machine learning methods, published from 2000 onward, and full-text availability) and exclusion criteria (irrelevant articles or those lacking experimental data), and were critically evaluated.
Results: Machine learning and deep learning algorithms have enhanced predictive models such as QSAR, toxicogenomics, and molecular modeling. These technologies have improved the accuracy of toxicity predictions, accelerated compound screening, and reduced the need for animal testing. In personalized medicine, AI predicts drug toxicity and optimizes therapeutic dosages by analyzing genetic profiles. In environmental health, wearable sensor data are analyzed to monitor the pollution effects. However, challenges include limited high-quality data, restricted model interpretability, and regulatory barriers.
Conclusion: Artificial intelligence has significant potential to transform predictive toxicology and enhance human and environmental safety. To fully harness this technology, data standardization, the development of explainable AI models, and the establishment of effective regulatory frameworks are essential. Future directions include real-time toxicity monitoring and the integration of AI with emerging technologies such as CRISPR. These approaches can lead to more informed decision-making, reduced uncertainty in risk assessment, and an accelerated transition toward preventive and personalized toxicology.
Method: This narrative critical review was conducted through a systematic search of scientific databases, including PubMed, Scopus, ScienceDirect, and Google Scholar. Keywords such as "Artificial Intelligence", "Predictive Toxicology," and "Personalized Medicine" were used to identify relevant articles. Articles were selected based on inclusion criteria (focusing on machine learning methods, published from 2000 onward, and full-text availability) and exclusion criteria (irrelevant articles or those lacking experimental data), and were critically evaluated.
Results: Machine learning and deep learning algorithms have enhanced predictive models such as QSAR, toxicogenomics, and molecular modeling. These technologies have improved the accuracy of toxicity predictions, accelerated compound screening, and reduced the need for animal testing. In personalized medicine, AI predicts drug toxicity and optimizes therapeutic dosages by analyzing genetic profiles. In environmental health, wearable sensor data are analyzed to monitor the pollution effects. However, challenges include limited high-quality data, restricted model interpretability, and regulatory barriers.
Conclusion: Artificial intelligence has significant potential to transform predictive toxicology and enhance human and environmental safety. To fully harness this technology, data standardization, the development of explainable AI models, and the establishment of effective regulatory frameworks are essential. Future directions include real-time toxicity monitoring and the integration of AI with emerging technologies such as CRISPR. These approaches can lead to more informed decision-making, reduced uncertainty in risk assessment, and an accelerated transition toward preventive and personalized toxicology.
Keywords: Artificial Intelligence, Toxicity Prediction, Personalized Medicine, Machine Learning, Toxicogenomics, Molecular Modeling
Type of Study: Narrative review articles |
Subject:
Artificial Intelligence in Healthcare
Received: 2025/07/13 | Accepted: 2025/11/11
Received: 2025/07/13 | Accepted: 2025/11/11
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