How Does AI Support Precision Oncology Decision Making?

Author: Rasit Dinc

Introduction

Precision oncology, an approach that tailors cancer treatment to the individual characteristics of each patient's tumor, is being revolutionized by artificial intelligence (AI). The integration of AI into clinical workflows is enhancing the ability of oncologists to make more informed and personalized decisions, ultimately improving patient outcomes. AI algorithms can analyze vast and complex datasets, including genomic, proteomic, and imaging data, to identify patterns and biomarkers that are not readily apparent to human observers. This capability is transforming cancer diagnosis, prognosis, and treatment selection, paving the way for a new era of personalized medicine. [1]

The Role of AI in Analyzing Complex Oncological Data

The sheer volume and complexity of data generated in modern oncology present a significant challenge for clinicians. AI, particularly machine learning and deep learning algorithms, excels at processing and interpreting these large datasets. For instance, AI can analyze next-generation sequencing (NGS) data to identify specific mutations, gene expression patterns, and other molecular alterations that drive cancer growth. This information is crucial for selecting targeted therapies that are most likely to be effective for a particular patient. [2]

Moreover, AI is being applied to medical imaging, such as MRI, CT, and PET scans, to improve the accuracy of tumor detection, segmentation, and characterization. AI-powered image analysis can help oncologists to better assess tumor volume, track treatment response, and predict the risk of recurrence. By automating and standardizing these tasks, AI not only improves efficiency but also reduces inter-observer variability, leading to more consistent and reliable diagnoses. [3]

AI-Driven Personalized Treatment Strategies

One of the most promising applications of AI in precision oncology is the development of personalized treatment strategies. AI models can integrate data from multiple sources, including a patient's clinical history, molecular profile, and imaging data, to predict their response to different therapies. This allows oncologists to select the optimal treatment regimen for each patient, maximizing efficacy while minimizing toxicity. [4]

Furthermore, AI is being used to develop adaptive treatment strategies that can be dynamically adjusted based on a patient's response to therapy. For example, AI can monitor changes in tumor size and molecular markers during treatment and recommend modifications to the treatment plan in real-time. This dynamic approach to treatment is particularly valuable in managing aggressive and heterogeneous cancers, where a one-size-fits-all approach is often ineffective. [5]

Challenges and the Future of AI in Precision Oncology

Despite the significant progress, the widespread adoption of AI in precision oncology is not without its challenges. These include the need for large, high-quality datasets for training and validating AI models, the "black box" nature of some algorithms, and the ethical and regulatory considerations associated with the use of AI in clinical decision-making. [6] Ensuring the fairness, transparency, and accountability of AI systems is paramount to their successful integration into clinical practice.

However, the future of AI in precision oncology is bright. Ongoing research is focused on developing more transparent and interpretable AI models, as well as on integrating AI into clinical decision support systems that can be seamlessly integrated into existing workflows. As AI technology continues to mature, it is poised to become an indispensable tool for oncologists, enabling them to deliver more precise, effective, and personalized care to cancer patients.

Conclusion

In conclusion, AI is a powerful tool that is transforming the landscape of precision oncology. By enabling the analysis of complex datasets, the development of personalized treatment strategies, and the real-time adaptation of therapy, AI is empowering oncologists to make more informed decisions and improve patient outcomes. While challenges remain, the continued development and integration of AI into clinical practice hold the promise of a new era of personalized and effective cancer care.

References

[1] Niraula, D., Cuneo, K. C., Dinov, I. D., Gonzalez, B. D., Jamaluddin, J. B., Jin, J. J., ... & El Naqa, I. (2025). Intricacies of human–AI interaction in dynamic decision-making for precision oncology. Nature Communications, 16(1), 1138.

[2] Dlamini, Z., Francies, F. Z., Hull, R., & Marima, R. (2020). Artificial intelligence (AI) and big data in cancer and precision oncology. Computational and structural biotechnology journal, 18, 2404-2418.

[3] Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., & Gevaert, O. (2021). Deep learning in cancer diagnosis, prognosis and treatment selection. Genome medicine, 13(1), 1-20.

[4] Arzikulov, F., & Komiljonov, A. (2025). The role of artificial intelligence in personalized oncology: predictive models and treatment optimization. Academic Journal of Science, Technology and Education, 1(1), 65-71.

[5] Niraula, D., et al. (2025). op. cit.

[6] Zhang, B., Shi, H., & Wang, H. (2023). Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach. Journal of multidisciplinary healthcare, 2595-2608.