How Machine Learning Is Transforming
The Healthcare Industry
To provide better healthcare services and bring personalized healthcare plans, the healthcare industry largely relies on computer technology. There is a huge amount of patient data in the healthcare industry. Healthcare providers collect data from different sources such as medical records, medical images, genomic data, mobile phone data, wearable devices (smartwatches and sensors), and more. Collecting this huge amount of data, collecting, processing, and extracting insights from this data is complex. This made it essential for companies in the healthcare industry to use smart electronic healthcare records. The application of machine learning in the system has made it possible to create smart electronic healthcare records. It uses records with built-in machine learning to assist with keeping medical records, interpreting health conditions, and suggesting treatment plans. Machine learning is an application of artificial intelligence that provides the system with the ability to learn and improve without being explicitly programmed. Machine learning systems are used to process and analyze these data instead of traditional methods.
Application of Machine Learning in Healthcare
In clinical practice, the prognosis is the process of predicting the development of a disease and the identification of symptoms. Machine learning has some useful applications in medical prognosis like machine learning models have been used in the classification and identification of certain tumors like brain tumors and lung nodules. It helped in the prognosis of these cases by predicting whether the tumor or nodule is new or existing.
Whether it is electronic health record interpretation or medical image analysis, machine learning-based methods are used for the extraction of clinical features from electronic health records to facilitate the diagnosis process. MRI, CT, Ultrasound and all types of medical images are being used by machine learning models to provide functional and anatomical information about different body organs. It helps in the detection and diagnosis of different abnormalities. To identify specific disease patterns and abnormalities like tumors and fractures in medical images. In an ordinary clinical approach, this process was executed by expert medical practitioners and often requires a lot of time and effort, machine learning methods have shown the potential in this task.
In medical treatment, natural language processing models are being used to produce and generate findings and conclusions for the given medical image. Many projects are working on this approach to increase the efficiency and effectiveness of the treatment process. Also, machine learning is being used in real-time health monitoring, which is very important in critical and dangerous cases. It is the process where affordable devices and smartphones are used to collect data from patients and then the data is being transmitted to the cloud for analysis and interpretation using machine learning models. The outcomes transmitted again for the device with appropriate instructions.
- Clinical Workflow
The last area of application of machine learning in healthcare is clinical workflow, this area explains the effect of all previous applications on the clinical workflow. Starting from the disease prediction and diagnosis to report generation using natural language processing models.
Major Areas In Healthcare Evolving With The Help Of Machine Learning
- Discovery Of Drugs
Drug discovery in one of the areas in healthcare is transforming rapidly using machine learning technology. Drug discovery is a challenge that involves huge expenditure and time consumption, the average time required for the invention to market is about 14 years and cost around $2.8 billion. Machine learning reduces discovery time and investment by leveraging the potential of data. Machine learning is being used in drug discovery to detect certain changes in cells that are produced by different compounds and thereby find out how compounds work. For example, drug discovery with machine learning gave birth to new methodologies such as precision medicine and next-generation sequencing that can ensure that a drug has the right impact on the patient.
- Personalized Treatment
Earlier in healthcare history, it was all about one-size-fits-all medicines for every patient, but patients respond very differently to the same medicine. With the help of machine learning, it has been made possible for healthcare providers to individualize treatments by compiling the individual medical data with machine learning applications. Machine learning helps medical professionals determine the risk of each patient by analyzing their symptoms, past medical records, and family history using micro-bio sensors. It monitors patient health and flag abnormalities in an efficient way which helps in providing more targeted treatment. For example, Somatix, a data analytics company that has launched a machine learning-based app that passively monitors and analyses an array of physical and emotional states. This helps physicians to understand what kind of behavioral and lifestyle changes are required for a healthy living. IBM Watson Oncology is a perfect example of delivering personalized treatment to cancer patients based on their medical history.
- Robotic Surgery
A robot operates in a medical emergency, in the hospital, the robot becomes a perfect teammate of the human being. Medical robotics is booming, it will revolutionize surgery in the future. Da Vinci Robot is the best example of robotic surgeries, it allows surgeons to control and manipulate the robotic hands to perform surgeries with precision in less space in the human body. In the same way, Mazor robotics uses artificial intelligence and machine learning to improve customization and keep the spread of a disease at a minimum in surgical procedures involving body parts with complex anatomies like the spine. Robotic surgery is also used in hair transplantation because of its detailed and efficient operations. Robotics powered by machine learning algorithms enhances the precision of medical tools by bringing in real-time surgery matrics, data from successful surgical experiences, and data from medical records with surgical procedures. A recent study shows that robotic surgery has reduced the length of stay in surgery by 22%.
- Early Diagnosis Of Medical Conditions
Machine learning is mainly used to collect and process the patient’s data to determine patterns and carry out the diagnosis of several medical conditions such as skin cancer. The early diagnosis ability with the help of machine learning is largely beneficial for the diagnosis of skin cancer because more than 5 million people in the USA are diagnosed with this disease annually. The traditional way of skin cancer diagnosis includes several processes such as long clinical screening, comprising a biopsy, dermoscopy, and histopathological examination. It is costly and time-consuming as it has to go through many processes, machine learning has enabled the medical practitioners to perform such diagnosis with less time and accuracy. For example, Moleanizer, an Australian based artificial intelligence software application that calculates and compares the size, diameter, and structure of the mole. It allows users to take a picture at the predefined interval to be able to differentiate between the beginning and grown infection in the skin.
- Clinic Performance
It is crucial for healthcare service providers to ensure that they are compliant and functioning with the legal boundaries. Healthcare service providers have to submit their reports to the government with necessary patient records that are treated at their hospital. Machine learning makes it easy to collect data from different sources using different methods. It brings accuracy and speed in the collection of such unstructured data located at different sources, it is complex to collect these data using the traditional approach.
- Improved Radiology
Machine learning has proved to be incredibly helpful in the field of radiology. In medical image analysis, several variables can get triggered at any moment, machine learning algorithms help analyze these variables altogether. Machine learning-based algorithms learn from various data samples, they can better diagnose and identify desired variables. Machine learning is leveraging the medical image analysis to classify objects like lesion into different categories such as normal or abnormal, lesion or non-lesion, benign, malignant, and so on. Researchers are using Google’s DeepMind Health to develop an algorithm that can detect the difference between healthy cells and cancerous cells, thereby enhancing the radiation treatment for cancerous cells.
There is a huge potential for machine learning in the healthcare industry, 75% of healthcare enterprises are planning to execute artificial intelligence and machine learning strategy by next year. With the increase in the adoption of machine learning in the healthcare industry, 2020 holds great opportunities to further enhance its potential. With 2 billion people getting middle-aged, a rise in the aging population, and a growing shortage of medical experts, artificial intelligence and machine learning will be useful in allowing communities to get efficient and consistent healthcare services. There are many reasons for machine learning to be adopted by the healthcare industry, but there are a few challenges to consider. The collection of unstructured data from different sources is one of the biggest challenges in implementing machine learning in the healthcare system. Healthcare data is located in many disparate systems such as SAP system, oracle system, hospital records, and more, it’s difficult to get those data at one place. It is not just enough to bring those data at one place, you have to transform that data to prepare for machine learning.
Piyush Jain is the founder and CEO of Simpalm, a mobile app development company that mainly focuses on the Healthcare Industry. Piyush founded Simpalm in 2009 and has grown it to be a leading mobile and web development company in the DMV area. With a Ph.D. from Johns Hopkins and a strong background in technology and entrepreneurship, he understands how to solve problems using technology. Under his leadership, Simpalm has delivered 300+ mobile apps and web solutions to clients in startups, enterprises, and the federal sector.