Introduction

Advancements in AI and machine learning are transforming cancer imaging, enhancing algorithm performance, interpretability, and clinical trust. However, challenges persist in integrating AI tools into real-world clinical practice due to technical, infrastructural, and regulatory barriers.

Main Content

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• AI & ML Innovations: Emerging techniques such as self-supervised and representational learning improve algorithm generalisability, while enhanced interpretability fosters trust in AI-driven diagnostics.
• Challenges in Clinical Adoption: Despite AI’s potential, real-world deployment remains difficult due to infrastructure gaps, lack of generalisability, and insufficient post-deployment monitoring.
• Radiomics & Large-Scale AI Research: While radiomics gains attention, its clinical application is still limited. Large-scale AI initiatives like ProCancer-I aim to refine detection and follow-up strategies for prostate cancer.

Conclusion

Addressing technical, regulatory, and workforce challenges is key to realizing AI’s full potential in cancer imaging.