An Intelligent Forensic Toxicology Approach for Lung Cancer Diagnosis Using Deep Learning Techniques AI-Based Forensic Toxicology for Lung Cancer Diagnosis
International Journal of Medical Toxicology and Forensic Medicine,
Vol. 16 (2026),
1 Dey 2026
,
Page 1-7
https://doi.org/10.22037/ijmtfm.v16.52131
Abstract
Background: Lung cancer is one of the most life-threatening malignancies worldwide, strongly associated with environmental pollutants, smoking-related toxins, and occupational carcinogenic exposure. The integration of forensic medicine and medical toxicology provides valuable insights for early diagnosis and effective risk prediction of lung cancer.
Methods: This study proposes an integrated framework combining toxicological analysis, forensic evaluation, and artificial intelligence-based predictive techniques for lung cancer diagnosis. Clinical records, toxicological biomarkers, imaging features, and exposure-related parameters were analyzed using machine- and deep-learning models to identify high-risk individuals and improve diagnostic accuracy.
Results: The proposed framework demonstrated improved performance in lung cancer risk prediction and classification compared with conventional diagnostic approaches. Toxicological biomarkers and environmental exposure factors showed a significant correlation with lung cancer progression. The AI-assisted analytical model enhanced early-stage detection, reduced misclassification rates, and supported accurate prognostic evaluation.
Conclusion: The findings underscore the importance of integrating forensic toxicology, medical analysis, and intelligent computational techniques in the diagnosis and risk assessment of lung cancer. The proposed approach can support clinicians and forensic experts in early intervention, treatment planning, and public health monitoring, thereby contributing to improved patient outcomes and the development of preventive healthcare strategies.
- Forensic medicine, Medical toxicology, Artificial intelligence. Deep learning, Risk prediction, Toxicological biomarkers
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References
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