How Does AI Support Pediatric Cancer Treatment?

Author: Rasit Dinc

Introduction

The integration of artificial intelligence (AI) into the medical field has marked a new era of innovation, particularly in complex and sensitive areas such as pediatric oncology. The rarity and heterogeneity of childhood cancers present unique challenges for diagnosis, treatment, and research. AI, with its capacity to analyze vast and complex datasets, offers a powerful tool to augment the capabilities of clinicians and researchers, paving the way for more personalized and effective cancer care for children [1]. This article explores the multifaceted role of AI in supporting pediatric cancer treatment, from enhancing diagnostic accuracy to personalizing therapeutic strategies and accelerating drug discovery.

Enhancing Diagnostic Accuracy

One of the most significant contributions of AI in pediatric oncology is its ability to improve diagnostic accuracy. AI algorithms, particularly those based on machine learning (ML) and deep learning (DL), can analyze medical images, such as MRIs and CT scans, with a level of detail and speed that surpasses human capabilities. These algorithms can identify subtle patterns and anomalies that may be indicative of cancer, leading to earlier and more accurate diagnoses. For instance, AI can assist in differentiating between benign and malignant tumors, a critical step in determining the appropriate course of treatment [1].

Furthermore, AI can help overcome the challenges posed by the limited availability of large datasets in pediatric oncology. Techniques such as transfer learning, where models trained on adult cancer data are adapted for pediatric use, and the sharing of algorithms across institutions, are being explored to enhance the robustness and accuracy of AI-driven diagnostic tools [2]. By leveraging these approaches, AI can help to standardize diagnostic processes and reduce the variability that can occur between different clinicians and institutions.

Personalizing Treatment Strategies

Beyond diagnosis, AI is playing an increasingly important role in personalizing cancer treatment for children. By analyzing a patient's genetic information, tumor characteristics, and other clinical data, AI models can predict how a patient is likely to respond to different therapies. This allows clinicians to select the most effective treatment for each individual child, while minimizing the risk of adverse side effects. This approach, known as precision medicine, holds immense promise for improving outcomes and quality of life for children with cancer [1].

AI can also assist in treatment planning by optimizing radiation therapy and other targeted treatments. By creating detailed 3D models of tumors and surrounding tissues, AI can help to ensure that treatment is delivered with maximum precision, sparing healthy tissues from unnecessary damage. This is particularly important in pediatric patients, who are more vulnerable to the long-term side effects of cancer treatment.

Accelerating Drug Discovery and Development

The development of new drugs for pediatric cancer is a long and expensive process. AI has the potential to significantly accelerate this process by identifying new drug targets and predicting the efficacy of potential drug candidates. By analyzing large-scale genomic and proteomic data, AI can identify novel biomarkers and pathways that can be targeted with new therapies. This can help to streamline the drug discovery process and bring new treatments to patients more quickly [1].

Conclusion

The integration of AI into pediatric oncology is transforming the way we diagnose, treat, and manage childhood cancer. From enhancing diagnostic accuracy and personalizing treatment to accelerating drug discovery, AI is providing clinicians and researchers with powerful new tools to improve outcomes for children with cancer. While challenges remain, particularly in the areas of data sharing and algorithm validation, the potential of AI to revolutionize pediatric cancer care is undeniable. As AI technologies continue to evolve, we can expect to see even more innovative applications that will bring us closer to a future where all children with cancer can be cured.

References

[1]: Hassan, M., Shahzadi, S., & Kloczkowski, A. (2025). Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review. Cancers (Basel), 17(11), 1828. https://doi.org/10.3390/cancers17111828

[2]: Yang, Y., Zhang, Y., & Li, Y. (2023). Artificial intelligence applications in pediatric oncology diagnosis. Explor Target Antitumor Ther, 4(1), 157-169. https://doi.org/10.37349/etat.2023.00127