Conference Abstracts - 5th Binaytara Precision Oncology Summit: Redefining Cancer Treatment with Molecular Targeted Strategies
Vol. 5, Issue Supplement 1, 2025 · S1-1
Artificial Intelligence in Oncology: Advancing Precision Cancer Treatment through Data-Driven Insights
Afef Khanfir, MD,Wala Ben Kridis, MD, PhD
Submission received: 2025-06-13 / Accepted: 2025-08-27 / Published: 2025-09-26
Abstract
Background
Artificial intelligence (AI) is revolutionizing oncology by enabling enhanced analysis of complex molecular and clinical data, critical for precision medicine. With cancer treatment increasingly guided by molecular targeted strategies, AI's role in diagnosis, prognosis, and therapeutic decision-making is expanding rapidly.
Methods
A comprehensive review of recent clinical studies and trials (2019–2024) evaluating AI applications in oncology was performed. Key areas included AI-driven imaging diagnostics, genomics, multi-omics data integration, predictive modeling of treatment response, and clinical workflow optimization.
Results
AI-powered diagnostic algorithms demonstrated an average accuracy of 91.5% (95% CI: 89.0–94.0%), significantly outperforming conventional radiological assessments (accuracy ~83%, p < 0.01). In molecular oncology, machine learning models identified actionable mutations with a sensitivity of 94%, facilitating precise patient stratification for targeted therapies. Predictive AI models forecasted therapeutic response with an area under the curve (AUC) of 0.87, aiding clinicians in optimizing treatment regimens and minimizing adverse effects. For example, in non-small cell lung cancer, AI predicted resistance to EGFR inhibitors with a hazard ratio of 0.62 (95% CI: 0.48–0.80, p = 0.002). Additionally, AI algorithms accelerated clinical trial enrollment by reducing screening time by 40%, improving recruitment efficiency, and trial diversity.
Conclusion
AI is an indispensable tool in the advancement of precision oncology, improving diagnostic accuracy, molecular target identification, and personalized treatment decisions. Despite challenges such as data heterogeneity and ethical considerations, validated AI applications have demonstrated statistically significant benefits in clinical outcomes. Integrating AI into routine oncology practice promises to redefine cancer treatment paradigms, fostering a future of more effective, tailored therapies.