Ten factors radiologists should consider when partnering with AI vendors

Ten factors radiologists should consider when partnering with AI vendors

November 19, 2019
Artificial Intelligence Business Affairs
From the November 2019 issue of HealthCare Business News magazine

By Sanjay M. Parekh

With over 150 independent software vendors developing machine learning solutions for medical imaging, sorting through the plethora of options to select vendors is a challenge. Here are 10 factors radiologists should consider (and questions they should ask) before partnering with vendors providing AI solutions for medical imaging.

1. Clinical relevancy


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The foremost consideration for healthcare providers adopting AI into their clinical workflow is relevancy. Does the AI solution truly address the needs of the healthcare provider, regardless of the associated costs and inconveniences to implement such a solution?

There are hundreds of potential use cases for AI in radiology, but the focus should be on those where AI can make the most impact, such as bottlenecks in the radiologist’s workload. These include repetitive tasks (e.g., manually taking measurements of imaging features) that can be automated, and providing additional information and decision support for more complex cases.

Questions the radiologist should ask:
• Is the use case a problem worth solving using AI? Use cases that are quick and easy to diagnose with limited involvement from the radiologist may not warrant AI.
• What is the impact of using AI on the diagnosis and treatment decisions? Will it lead to improved patient care?
• What are the priorities for the radiologist to use AI and how would this be determined?
• Is the AI solution future proof? Will it be relevant in the years to come?

2. Algorithm development
The resurgence of AI can be attributed to the shift in techniques from classic machine learning to the deep learning approach. The challenge remains for AI vendors to reduce the ‘black-box’ phenomenon for the end-user (radiologist) and the methodology used for algorithm development is key.

Questions the radiologist should ask:
• How many images were used to develop the algorithm?
• Were the images obtained from a representative cross-section of population demographics and scanner manufacturers?
• Who annotated the images? Did the developers use experienced radiologists or residents?
• How many radiologists annotated each image (ideally two or more)? If two radiologists annotated the images, did the developers use a third radiologist for any discrepancies?
• Were the annotated images validated using clinical biomarkers or biopsy?
• Was the image data set for training and validation of the algorithm different (and unseen) from the one used for testing the algorithm?

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