Hospitals, clinics and other healthcare organizations all around the world are working with software companies to develop administrative systems that are growingly digitized and automated. More importantly, scientists and researchers are using machine learning (ML) to churn out a number of smart solutions that can ultimately help in diagnosing and treating an illness. Patients are set to benefit the most as the technology can improve their outcome by analyzing the best forms of treatment for them. ML is capable of more accurately detecting a disease at an earlier stage, helping to reduce the number of readmissions in hospitals and clinics.
The technology has also come a long way in discovering and developing new drugs that have great potential in helping patients with complicated conditions. A cornerstone of ML is its ability to gather data and automate the output of smart solutions with robotic process automation (RPA) automation platforms. Intelligent automation company WorkFusion offers an RPA platform called RPA Express that can seamlessly move work between bots and humans and integrate manual inputs with an intelligent user interface.
Here are four applications of ML in the healthcare industry:
1) Disease Identification
One of the key components of a successful healthcare organization is its ability to identify a disease with speed and accuracy. With are hundreds of drugs currently on clinical trial, scientists and computationalists are entering the fray in high-need areas such as cancer identification and treatment. One such solution integrates cognitive computing with genomic tumor sequencing, while another uses ML to develop diagnostics and therapeutic treatments in multiple areas such as oncology. Another example is DeepMind Health, which is developing technology that can address macular degeneration in aging eyes.
2) Diagnosis in Medical Imaging
Another important element of diagnosing an illness is medical imaging and its ability to show a more complete image of an illness. Deep learning is playing a key role in this regard as it is becoming more accessible thanks to richer data sources that can be used in the diagnostic process. The technology has some limits as it is incapable of explaining how it arrived at its predictions, although these ML applications are correct a lot of the time. Nevertheless, the technology, combined with healthcare professionals, can offer treatment solutions quicker with these advanced diagnosis tools by interpreting a result and deciding whether the machine’s treatment suggestions are correct or not.
3) Drug Discovery
ML has the capacity to discover new drugs that offers great economic value for pharmaceuticals, hospitals and new treatment avenues for patients. Some of the major tech players such as IBM and Google have created ML platforms designed to discover new routes of treatment for patients. Precision medicine is a key term in this topic as it consists of identifying mechanisms for multifactorial diseases and finding alternative paths for therapy. Institutions such as the MIT Clinical Machine Learning Group have been using precision medicine research to develop algorithms that can help doctors better understand disease processes and create effective treatments for diseases such as Type 2 diabetes.
4) Robotic Surgical Tools
We will always need human intervention for surgeries due to the high-risk nature of these procedures, but ML has been helping greatly in the robotic surgery space. One of the most popular developments in the field has been the da Vinci robot, which allows surgeons to manipulate robotic limbs in order to perform surgeries with great detail and in tight spaces. These hands are often steadier and more accurate than human hands. There are also tools that use computer vision aided by machine learning to identify the distances of specific body parts in order to adequately perform surgery on them. One example of this is the identification of hair follicles for hair transplantation surgery.
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