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Intrigued by this, do you have an example of how the analysis is presented to the patient? Does it take into account the reason behind performing a scan, e.g. Progression/regression of a cyst, interval abdominal pain etc?
Could it be adapted as a tool for clinicians, sort of like ecg machines that present preliminary findings but the clinician will still perform his/her own analysis of the ecg
Hey Jesper! @hsus Our algorithms run regardless of the reason for scan. They are intended to find incidental findings often missed in routine exams and alert users so that they go and consult their physicians about their findings. Our company offers solutions for radiologists and hospitals as well. If you are interested in learning more about these solutions, feel free to email me elad@zebra-med.com
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@eladbenjamin seems reasonable! Would still love to see a screenshot of what information is presented to the patient and how 😉 congrats on the launch!
@hsus Sure- feel free to send an email to our PM, Tom, at tomg@zebra-med.com she can provide some example!
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@eladbenjamin what sort of ministry of health [Israeli or abroad] did you get/still need to get for this to get a regulatory stamp of approval? What is your company's liability for misdiagnosis? What happens if I upload a scan and your product discovers something that the original scan practitioner didn't find --can Zebra be soubpeaonad as an expert medical witness/authority? Realise these are tough questions, but this is health, after all..
@amirmizroch Hi Amir, these are all important questions. All our algorithms have gone through extensive validation both internally and at various clinical institutions before being released. That being said, they can still be wrong, which is why right now we encourage anyone who has received a result from us - positive or negative - and who thinks they have a health issue, to consult with their physician. We do not replace expert medical care and any patients care is ultimately determined by their physician, not Zebra. Hope that helps!
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Can you explain more about the machine learning aspect? Are there doctors/ radiologists on your team going over the scans?
Sure- @alona_stein. Machine learning is an algorithmic technique that allows computers to analyze vast amounts of data and automatically detect patterns and features, or make predictions regarding certain conditions. We use deep learning techniques on an archive of millions of imaging scans to build software that automatically detects and diagnoses medical conditions. We do work with radiologists to train our algorithms and identify the most interesting challenges radiologists face in their work.
Congrats, this is a big launch! Quick question: what is the help you give the end user so that they don't have a "freak out" moment when you give them a second opinion that might be bad news or that requires them to talk to a medical professional afterwards?
@jalter Hi jessica! It is a good question that came up while we were working on the product. The algorithms that are featured on the platform produce prescreening results -we provide some information on the medical condition so that users understand them and we ask users to go and consult their physician regarding their results. While delivering the results- we also suggest users to try an online radiology service in order to get a second opinion on their scan.
@eladbenjamin@eldad_elnekave - Especially loving that you're offering this for free making quality healthcare available to people that otherwise wouldn't have access is something you can be super proud of.
Interesting to see how ML will impact doctors' jobs like some other "knowledge workers". Should change required skillset and become much result in better patient care hopefully?!
@hayush Thank you Eli! We witnessed how ML revolutionized areas such as finance and automotive and we are excited to bring the same revolution to Healthcare.
@donte_ll Hi Donte, there is always a certain margin of error in any medical diagnosis. We work hard and continuously improve our algorithms to increase their accuracy, but there may still be errors in our findings. Therefore, the results we provide do not replace a physician's diagnosis and cannot be considered as a recommendation for any treatment or follow-up. Some of the medical conditions we currently detect are fatty liver, low bone density, emphysema and calcium in the coronary arteries.
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