The team at maiData has decades of experience in A.I. and has probably already dealt with the challenges you're facing. Now you can tap into our expertise, giving you access to the depth and breadth of our team.
maiData's pre-existing relationships with data collection sites mean that we can dramatically shorten the time to get you the data you need.
maiData's consistent pseudonymization process makes it easier for your team to incorporate new data into your development and test sets.
It's clear that regulators want to carefully segregate your data, and refresh it from time to time. Let maiData advise you on regular deliveries of new data to meet these needs.
maiData will work closely with you to find exactly the data you're looking for.
maiData will work hand-in-hand with your team to exclude data that does not meet your requirements.
If you need special help with data augmentation, please let us know. We've been down this road before, and might be able to assist.
Does your product meet the specifications / requirements you have written?
Does your product meet the business needs that caused you to write those requirements?
What is the level of performance of your algorithm on a standalone, segregated data set that is representative of the target population?
FDA has promoted these types of studies for 510(k) filings. But, Reader Studies can also create a false sense of success.
Retrospective Studies are good because you can run on large data sets. But, Retrospective Studies do not reflect what happens in clinical practice.
Prospective Studies are the only way to understand how your A.I. product might be adopted into clinical practice and how it may affect outcomes.
Every clinician wants your algorithm proven in a Randomized Control Trial, but the cost often makes these studies prohibitive.
How you package you A.I. software matters to hospital IT departments.
Whether cloud-based or on-premise, your solution will have to connect to on-premise DICOM, HL7 and/or FHIR systems.
Security is table stakes for hospital IT departments and that makes post-market surveillance a real challenge.
Just because your algorithm runs doesn't mean it integrates smoothly into workflow. Too many button clicks, and your product may miss the mark.
What do you actually know about how the algorithm performed vs. what the radiologist reported?
How well does your algorithm actually perform on this facility's patient population?
How long does it take before you are alerted that your algorithm is failing to meet radiologist expectations?
We have lots of ideas ... if you need a few, we can help.
We can help you plan out a development that will get you to your goal quickly.
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