Diagnostic system from medical imaging

To meet the growing demand for care, medical technology leaders are turning to AI tools that can help radiation oncologists provide high-quality, individualized treatment faster.

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What AI can do with unstructured data in healthcare

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Improve data protection and minimize paperwork

Most security disasters in the healthcare industry are caused first by human error and misuse and physical theft and hacking second. Electronic Health Records or EHRs not only structure patient information but also allow for safe access and more personalized care.

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Increase diagnostics accuracy

In the US alone, over 12 million patients are misdiagnosed annually. Deep learning applied to healthcare can radically improve that statistic: Instead of following a routine checklist, healthcare professionals can benefit from the informative by-product of algorithms that can read data using computer vision,

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Boost clinical performance

Running any medical facility is a complex, multi-faceted undertaking rife with the possibility of making unfortunate decisions. The obvious, well-documented risk level is high. Data-driven software provides clinicians with the opportunity to make better sense of their data – schedule more efficiently, create

Forecasting system on pharmacological reactions

Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data.

“I knew that if I failed I wouldn’t regret that, but I knew the one thing I might regret is not trying.”

Jeff Homann

AI Lab Founder and CEO

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Showcase of awesome works we have done for our clients

Why AI lab?

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Reduction in healthcare costs due to optimized clinical processes and remote care
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Reduction in time spent on routine administrative tasks
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work time savings for nurses due to the elimination of unnecessary visits

Optimization for Healthcare Insurance companies

Insurers are using artificial intelligence and machine learning to review medical records, optimize care for chronic conditions, identify risk.

Payers manage risk

Insurers are using artificial intelligence and machine learning to review medical records, optimize care for chronic conditions, identify risk.

AI to optimize health

AI and machine learning move from a custom and reactive approach to more standardized and proactive management of patient care.

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