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How AI-Driven Diagnostics Can Save Lives When Humans Can’t

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Today, chronic diseases – long-term conditions that require ongoing medical attention – are pushing the healthcare system to the brink of collapse. According to NIH, more than two-thirds of all deaths in America are caused by just five chronic diseases: heart problems, cancer, stroke, chronic obstructive pulmonary disease, and diabetes. On top of that, the dwindling count of healthcare workers isn’t helping either. Over 400,000 people have left the industry since the Covid pandemic began and many more are expected to quit in the future, leaving the rest of the workforce under immense pressure. But AI can help, starting with the very first step of effective care: diagnosis.

How Can AI Take Care Of Diagnostics, And Improve Clinical Outcomes?

When the workforce is understaffed and the caseload is high, errors in diagnosis can easily occur. According to the estimates of the Society to Improve Diagnosis in Medicine, medical errors impact over 12 million Americans annually, leading to incorrect treatments and associated costs that likely exceed $100 billion. With AI in use, healthcare organizations can address this challenge, analyzing reports not only faster but also more accurately than humans could. In many cases, AI has even been able to diagnose medical conditions that humans could not. For instance, this very rare form of leukemia.

At the core of this effective diagnosis lies high-quality visual data and powerful machine learning and computer vision algorithms. When trained on high-quality data, the algorithms can identify intricate patterns within the patient samples, leading to better prediction of the condition and more insightful comparisons. They can process all sorts of medical imagery, right from MRIs and X-rays to ultrasounds, to detect subtle details that might elude human observation.

This can be very handy for the treatment of chronic conditions, which require accurate detection at first go for long-term care. Plus, since this is an AI system at work – and not a human, the process of analyzing the data and producing the diagnoses is relatively faster. Imagine someone getting to know about their tumor within a matter of days instead of weeks. They can get started with the treatment right away – which may lead to better clinical outcomes.

Source: Forbes