Oncology Data Management with AI: Ensuring Interoperability and Data Security in the Digital Era

In the digital era, oncology data management has undergone a radical transformation thanks to the integration of artificial intelligence (AI). The ability to process large volumes of clinical and imaging data has enabled significant advancements in cancer diagnosis and treatment. However, this progress also presents challenges in terms of interoperability and data security, which are critical aspects for ensuring an efficient and secure information flow in healthcare.

Interoperability and Security: Pillars of Oncology Data Management

Interoperability in healthcare systems is essential for the effective exchange of information between different platforms and devices. An example of this is the use of systems like the Picture Archiving and Communication System (PACS), which has improved diagnostic accuracy and personalized treatment by integrating AI algorithms. These systems allow for faster and more precise analysis of imaging data, which is crucial for early disease detection.

On the other hand, data security is a fundamental aspect that cannot be overlooked. The implementation of techniques such as homomorphic encryption enables federated analysis of real data while complying with data protection requirements. This is especially relevant in the context of oncology, where patient data confidentiality and integrity are paramount.

Moreover, platforms like KETOS demonstrate how clinical decision support models and machine learning can be securely deployed in hospital environments. These platforms utilize standards such as FHIR to access patient data, ensuring that information is handled securely and efficiently.

Conclusions

The integration of AI in oncology data management offers unprecedented opportunities to improve clinical outcomes and healthcare system efficiency. However, to fully leverage these opportunities, it is crucial to address the challenges of interoperability and data security. The adoption of health standards and the development of interoperable and secure platforms are essential steps to ensure a smooth and secure information flow. By doing so, we can move towards a future where technology and medicine work hand in hand to provide more personalized and effective care for oncology patients.

Referencias

• [1] Emerging Applications of Picture Archiving and Communication Systems and Their Impact on Research and Education: A Literature Review
• [2] Clinical Research Informatics
• [3] KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services

Created 20/1/2025