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Related Experiment Video

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Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

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[Artificial Intelligence: Infrastructures and Prerequisites at European Level].

Sebastian Fritsch, Oliver Maassen, Morris Riedel

Anasthesiologie, Intensivmedizin, Notfallmedizin, Schmerztherapie : AINS

|March 23, 2022

Summary

Artificial intelligence (AI) in medicine requires large datasets adhering to FAIR Data principles. This article explores challenges and solutions for secure health data handling in AI applications across Europe.

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Area of Science:

Medical Informatics
Artificial Intelligence
Health Data Management

Background:

Artificial intelligence (AI) applications in medicine rely heavily on extensive datasets.
Health data presents unique challenges for AI model development and data sharing.
The FAIR Data principles (Findable, Accessible, Interoperable, Reusable) are crucial for effective data utilization.

Purpose of the Study:

To highlight the specific challenges of using health data for AI in anaesthesia and intensive care medicine.
To discuss current obstacles hindering the broader application of AI in European medicine.
To propose solutions for secure and effective health data handling for AI at the European level.

Main Methods:

Review of current practices and challenges in health data management for AI.
Analysis of obstacles related to data protection, research data infrastructures, and medical product approval.
Exploration of potential strategies for secure and effective data handling in European AI initiatives.

Main Results:

Significant challenges exist in applying FAIR Data principles to sensitive health data.
Data protection regulations, fragmented research infrastructures, and regulatory hurdles impede AI adoption in European medicine.
Despite difficulties, establishing secure and effective European-level data handling for AI is feasible.

Conclusions:

Addressing data protection, infrastructure, and regulatory issues is essential for advancing AI in European healthcare.
A collaborative European approach is needed to overcome data challenges and unlock AI's potential in medicine.
Secure and effective health data management frameworks can enable widespread AI implementation in healthcare.