Human sight equipped with a microscope is not even half accurate when compared to automated detection systems. Why engage in inaccuracy when we have the best alternative for accurate results? 21st century, commemorate the technological avail of development like Big data, Artificial Intelligence, Internet of Things(IoT) and but obvious Machine Learning(ML). According to a survey by Deloitte of 1,100 US companies that were using Artificial Intelligence, 63% were focusing on Machine Learning(ML). It is a broad technique that could have practical use in many industries and use cases.
When it comes to the data-driven industry, Healthcare is one of the major. With the surge in data occupancy, the complexity to maintain so is inevitable. Though a proper channelising of data can make healthcare more effective, sufficient, and pocket friendly, it is actually possible through machine learning techniques. Effective machine learning implementation enables healthcare professionals in better decision-making, identify trends and innovations, and improve the efficiency of research and clinical trials.
In lieu of the same, let’s focus on some of the recent instances. It is evident that Machine Learning in Healthcare technologies in oncology search for the cells affected by cancer at an accuracy level comparable to that of an experienced physician. Google recently proved it right by developing a machine-learning algorithm to identify cancerous tumors in mammograms, and researchers in Stanford University are using deep learning to identify skin cancer.
Headway into the recent sitch with ML
With the aid of ML, researchers are triumphant in building models that can quickly analyze data in real time and can make better decisions on clinical diagnoses and treatments.In fact, 54% of organizations are using or considering Hadoop as big data processing tool to get important insights on healthcare according to the Ventana Research Survey.
In a recent development of ML in healthcare, MIT engineers developed a telerobotic system to help surgeons quickly and remotely treat patients experiencing a stroke or aneurysm. The robotic movements are controlled by magnets and doctors in another hospital can take control of the robotic arm to conduct the surgery. With a joystick and live imaging, an operator can adjust the magnet’s orientation and manipulate the arm to guide a soft and thin magnetic wire through arteries and vessels. Endovascular surgery is a tough catch for even surgeons, they have to train themselves for long years to find the precision needed. MIT has been in close watch for the same, they even came up with a magnetically steerable, thread-like robot that can actively glide through narrow, winding pathways, such as the labyrinthine vasculature of the brain to make the toughest surgeries a bit easy to some extent.
Is ML spearheading drug development?
According to the Tufts Center for the Study of Drug Development, complete research on drug development can take up to 10 years before launching in the market and cost approximately 2.6 billion dollars.
It is not only strenuous but sort of out of the question for pharmaceutical companies to predict the effect of potential drugs on the targeted and non-targeted molecules with the traditional computerized technology. According to recent research, some 90% of drugs can’t get through the trial process, ML can reduce the time-to-market of the drugs and even reduce costs by up to 70%. Predicting molecular properties quickly and accurately is important to advancing scientific discovery and application in areas ranging from materials science to pharmaceuticals. To reduce the time and cost, scientists have suggested ML help for computational chemistry research. At Carnegie Mellon University’s College of Engineering pondered if they could use this large volume of unlabeled molecules to build ML models that could perform better on property predictions than other models.
Their work culminated in the development of a self-supervised learning framework named MolCLR, short for Molecular Contrastive Learning of Representations with Graph Neural Networks (GNNs).”MolCLR significantly boosts the performance of ML models by leveraging approximately 10 million unlabeled molecule data,”
ML Changes Regime for Healthcare: An Early Adopter
Machine learning and AI is expected to play a critical role in Central Nervous System (CNS) clinical trials in the future. With the evident surge in diseases and the ongoing horrendous pandemic, companies trusted in ML technology for the healthcare sector. Let’s find the same with some neoteric examples.
- With the help of IBM’s Watson AI technology, Pfizer uses machine learning for immuno-oncology research about how the body’s immune system can fight cancer.
- With the help of machine learning, Quotient Health developed software that aims to “reduce the cost of supporting EMR [electronic medical records] systems” by optimizing and standardizing the way those systems are designed.
- In seattle, Washington,KenSci uses machine learning to predict illness and treatment to help physicians
- Ciox Health uses machine learning to enhance “health information management and exchange of health information,” with the goal of modernizing workflows, at Alpharetta, Georgia.
- In Redmond, Washington, Microsoft’s Project InnerEye employs machine learning to differentiate between tumors and healthy anatomy using 3D radiological images that assist medical experts in radiotherapy and surgical planning, among other things.