The AI-driven transformation of hematology is one of the several applications of machine learning in healthcare.
Recently, the conviction of Elizabeth Holmes after a years-long fraud case was in the spotlight. The entire case surrounding the billionaire and her company Theranos was about how several investors were defrauded in the name of a supposedly revolutionary blood-testing solution. Theranos had promised the investors that the company’s proprietary technology and expertise would provide comprehensive test results for patients after taking a minuscule amount of blood from them.
Unfortunately for Holmes, the emergence of AI and machine learning in healthcare and hematology have actually made it possible to conduct highly effective and accurate blood tests with just a few drops of blood. In fact, there are multiple ways in which AI can disrupt hematology, some of which are explored below:
1. By Improving Accuracy and Speed Cellular Study
Cytomorphology is defined as the study of cells to find irregularities that may point towards cancer or other cell-related medical conditions. The involvement of machine learning in healthcare allows doctors to accurately diagnose rare and common aberrant cells using blood tests. Carrying out such a test at an early stage can be incredibly useful to prevent the growth and spread of such cells to other parts of the body—a concept known as metastasizing in oncological terms. This hematological application is an example of how machine learning improves cancer diagnostics.
2. By Improving Detection of COVID-19
Different diseases are hard to track as they involve the use of different tests and operations. So, if a patient needs to know about the presence of pneumonia in their lungs, they’ll have to undergo respiratory tests and X-Rays. On the other hand, urinary tract infections and kidney stones necessitate CT scans for diagnosis. Machine learning-based blood test tools can scan blood samples to find patterns indicative of such diseases without all these tests. This makes AI and machine learning the faster and less invasive option for patients.
3. By Improving Treatment Selection
Once the diagnosis of a health condition is made, healthcare experts need to opt for surgery, medication or long-term therapy that will resolve the condition in the quickest, safest and most painless way for patients. Once again, this is a space in which machine learning’s pattern recognition abilities can be put to good use. AI algorithms can run through thousands of digitized medical records to shortlist the closest possible treatment option for an unknown or lesser-known condition in a patient. This provides doctors with data-driven clarity regarding the best path to be taken for specific patients and situations.
It is safe to say that machine learning and AI influence hematology in a way that would make Theranos and its partners incredibly proud.
