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ONCOLOGY, NUCLEAR MEDICINE AND TRANSPLANTOLOGY
Review Article
Nov 12, 2026 3 1

Predicting Cancer Before It Begins: An AI-Enabled Framework for Precision Assessment of Individual Susceptibility to Environmental Carcinogens

Cecilia Oyinlade Ogundare 1,
Abdul-Basid Yakubu 2,
Mary Adebimpe Adewoye 3,
Esther Uyoyooghene Olokede 4 *
1 Department of Chemical Sciences, College of Basic Science, Lagos State University of Science and Technology, Ikorodu, Nigeria2 School of Natural Sciences, University of Chester England, United Kingdom3 Department of Applied Health Science, Indiana University Bloomington, IN, USA4 Department of Medical Laboratory Science, Faculty of Basic Medical Sciences, University of Benin, Edo State, Nigeria* Corresponding Author
Oncology, Nuclear Medicine and Transplantology, 2(4), 2026, onmt030, https://doi.org/10.63946/onmt/19943
Publication date: Nov 12, 2026
Full Text (PDF)

ABSTRACT

Environmental carcinogens remain significant contributors to cancer development; however, conventional risk assessment has largely focused on population-level exposure without adequately accounting for variation in individual susceptibility. Individuals exposed to comparable environmental hazards can nevertheless face different cancer risks, as susceptibility is influenced by genetic and epigenetic variation, metabolic capacity, immune function, age, lifestyle, comorbidities, and cumulative exposure histories. The growing availability of environmental, clinical, molecular, and longitudinal health data offers new opportunities to characterize these differences with greater precision. In this context, artificial intelligence (AI), particularly machine learning and other multimodal analytical approaches, can identify complex patterns across heterogeneous datasets that may remain difficult to detect through conventional statistical methods. This narrative review explores the potential of AI to enable more precise assessment of individual susceptibility to environmental carcinogens and proposes a framework that brings together exposure profiles, host susceptibility, molecular responses, lifestyle and contextual factors, and longitudinal information. Within this framework, AI serves as an analytical layer for generating individualized risk profiles and informing prevention strategies, including exposure reduction, risk-informed surveillance, and targeted preventive interventions where clinically appropriate. However, several challenges may limit its translation into practice, including weaknesses in exposure assessment, data quality, algorithmic bias, limited interpretability, privacy concerns, uncertainty surrounding causal inference, insufficient external validation, and implementation barriers. Accordingly, AI should complement, not replace, established epidemiological and mechanistic approaches. With rigorous prospective validation, transparent model development, and appropriate ethical and clinical safeguards, AI-enabled susceptibility assessment may support a transition from predominantly population-based approaches to more individualized and risk-informed environmental cancer prevention.

KEYWORDS

Environmental Carcinogens Cancer Susceptibility Artificial Intelligence Precision Prevention Primary Prevention Exposome Risk Prediction Environmental Health

CITATION (Vancouver)

Ogundare CO, Yakubu AB, Adewoye MA, Olokede EU. Predicting Cancer Before It Begins: An AI-Enabled Framework for Precision Assessment of Individual Susceptibility to Environmental Carcinogens. Oncology, Nuclear Medicine and Transplantology. 2026;2(4):onmt030. https://doi.org/10.63946/onmt/19943
APA
Ogundare, C. O., Yakubu, A.-B., Adewoye, M. A., & Olokede, E. U. (2026). Predicting Cancer Before It Begins: An AI-Enabled Framework for Precision Assessment of Individual Susceptibility to Environmental Carcinogens. Oncology, Nuclear Medicine and Transplantology, 2(4), onmt030. https://doi.org/10.63946/onmt/19943
Harvard
Ogundare, C. O., Yakubu, A.-B., Adewoye, M. A., and Olokede, E. U. (2026). Predicting Cancer Before It Begins: An AI-Enabled Framework for Precision Assessment of Individual Susceptibility to Environmental Carcinogens. Oncology, Nuclear Medicine and Transplantology, 2(4), onmt030. https://doi.org/10.63946/onmt/19943
AMA
Ogundare CO, Yakubu AB, Adewoye MA, Olokede EU. Predicting Cancer Before It Begins: An AI-Enabled Framework for Precision Assessment of Individual Susceptibility to Environmental Carcinogens. Oncology, Nuclear Medicine and Transplantology. 2026;2(4), onmt030. https://doi.org/10.63946/onmt/19943
Chicago
Ogundare, Cecilia Oyinlade, Abdul-Basid Yakubu, Mary Adebimpe Adewoye, and Esther Uyoyooghene Olokede. "Predicting Cancer Before It Begins: An AI-Enabled Framework for Precision Assessment of Individual Susceptibility to Environmental Carcinogens". Oncology, Nuclear Medicine and Transplantology 2026 2 no. 4 (2026): onmt030. https://doi.org/10.63946/onmt/19943
MLA
Ogundare, Cecilia Oyinlade et al. "Predicting Cancer Before It Begins: An AI-Enabled Framework for Precision Assessment of Individual Susceptibility to Environmental Carcinogens". Oncology, Nuclear Medicine and Transplantology, vol. 2, no. 4, 2026, onmt030. https://doi.org/10.63946/onmt/19943

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