Why is this questions listed below relevant and useful to gauge the suitability of AI for medical imaging?

Data Availability and Quality: Is there a sufficient amount of high$-$quality, labeled medical imaging data available for training the AI model?

Required attributes: Volume of data, accuracy of labels, diversity of cases.

Variable attributes: Data augmentation techniques, preprocessing methods.

Clinical Relevance: Does the AI solution address a clinically significant problem that can improve patient outcomes or streamline workflows?

Required attributes: Clinical impact, relevance to current medical practices.

Variable attributes: Potential for integration into existing systems, acceptance by medical professionals.

Regulatory and Ethical Considerations: Are there clear guidelines and regulations for the use of AI in medical imaging, and does the solution comply with these?

Required attributes: Compliance with regulatory standards, ethical considerations.

Variable attributes: Data privacy measures, transparency of AI decision$-$making.

Performance Metrics: How does the AI model perform in terms of accuracy, sensitivity$,$ specificity, and other relevant metrics compared to human experts?

Required attributes: Accuracy, sensitivity$,$ specificity.

Variable attributes: False positive$/$negative rates, robustness across different datasets.

Cost and Resource Implications: What are the costs associated with implementing and maintaining the AI solution, and are these justified by the benefits?

Required attributes: Initial implementation cost, ongoing maintenance cost.

Variable attributes: Return on investment, resource allocation.