ReviewFrontiers in toxicology2024
AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability.
Review in Frontiers in toxicology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Narrative Review of Nanomaterial Interactions in Plants with a Focus on Multi-Omics and Epigenetic Remodeling.Plants (Basel, Switzerland) · 2026Review
- Analysis of Ni, Cu, and Fe in Loose Cosmetic Products: A Preliminary Dermal Exposure and Health Risk Assessment.Toxics · 2026Article
- Article
- Advancing the Discovery of Emerging Contaminants: A Leap in Technology and Data.Environmental science & technology · 2026Review
- Development andACS omega · 2026Article
- Experimental models of chemical carcinogenesis: bridging toxicology and preclinical pharmacology.Journal of the Egyptian National Cancer Institute · 2026Review
- Review on Predictive Models and Integration Strategies for Holistic Impact Assessment of Chemicals and Materials.Environmental science & technology · 2026Review
- Machine learning prioritization of antibiotic residues in aquatic foods reveals exposure-driven genotoxic risk mediated by BCL2.Food chemistry: X · 2026Article
- Toxicodynamics and Human Health Risks of Metal Nanoparticles: A Review of AI/ML-Based Predictive Nanotoxicology.International journal of nanomedicine · 2026Review
- Interpretable machine learning for predicting major amputation risk in hospitalized diabetic foot ulcer patients: a single-center study with temporal external validation.Frontiers in endocrinology · 2026Article
- Cancer and Environmental Xenobiotics: Mechanisms, Controversies, and Innovations.Journal of xenobiotics · 2025Review
- Multifunctional Nanoscale Pigments: Emerging Risks and Circular Strategies for a Sustainable Future.Small science · 2025Review
- Integrating Bioprinting and Increased Throughput: Next-Generation Models for Cardiac Research.International journal of molecular sciences · 2025Review
- An interpretable machine learning model predicts the interactive and cumulative risks of different environmental chemical exposures on depression.Translational psychiatry · 2025Article
- Gut as a Target of Ochratoxin A: Toxicological Insights and the Role of Microbiota.International journal of molecular sciences · 2025Review
- Transforming beef quality through healthy breeding: a strategy to reduce carcinogenic compounds and enhance human health: a review.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Review
- Advances in pharmacological research on myocardial remodeling agents: A decade in review.Medicine · 2025Review
- Do Gut Microbiomes Shift After Bariatric Surgery? A Literature Review.Medicina (Kaunas, Lithuania) · 2025Review
- Innovative nanoparticle strategies for treating oral cancers.Medical oncology (Northwood, London, England) · 2025Review
- Influence of experimental variables on spheroid attributes.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, highlighting successful applications and case studies across diverse sectors. From predicting genotoxicity and mutagenicity to elucidating molecular pathways underlying carcinogenesis, integrative approaches offer a comprehensive framework for understanding chemical exposures and mitigating associated health risks. Future perspectives for advancing chemical risk assessment and cancer prevention through data integration, advanced machine learning techniques, translational research, and policy implementation are discussed. By implementing the predictive capabilities of AI/ML and omics technologies, researchers and policymakers can enhance public health protection, inform regulatory decisions, and promote sustainable development for a healthier future.
Indexed as
Identifiers
What Socratic 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 Socratic graph.