The questions to ask yourself and any AI Deep learning supplier

Top 5 considerations when purchasing an RT organ at risk AI contouring solution

Serious about saving time for your Radiation Therapy department? This article gives 5 key topics to explore.

The questions to ask yourself and any AI Deep learning supplier:

Configuration and algorithms: What is the right solution for your department?
Atlas contouring (based on 10/20 datasets) or Deep Learning (based on 100+). Which solution will meet your department’s high standards?:
How many datasets did the provider use to train the data (100+)? What was the quality like?
Can you test before you buy to see your data on their solution? What is the quality of output look like?
Has a product been independently validated and featured in peer-reviewed journals?

Systems adaptability – One size does not fit all.
Can outputs be configured to your needs and preferences?
Can clinical product experts form the supplier refine and configure the solution to your preferences?

Systems setup
Is the product vendor-neutral (vendor agnostic) i.e. work with all your existing systems?
Does the product provide one source of contours to ensure consistency across your department and geographical sites?

Time savings
Is the solution, zero-click, i.e. seamlessly integrate into existing workflows so your expert staff do not have an additional clinical tasks?
Can other autocontouring providers provide customer references that offer quantitative time saving measurements?

Future-proofing
Is this a brand and customer you can trust? How long have they been trading?
What do past customers say about their overall experience with that supplier?
This is more than a transactional purchase; All serious providers should offer your hospital clinically trained customer experts to ensure your clinical, customer and innovation needs are supported, discussed and addressed efficiently and effectively.