In this article, we will talk about 5 Machine Learning applications to improve the healthcare industry by 2022. Nowadays Machine Learning is becoming more valuable and has more scope for the future. Let’s Get into the topic.
The healthcare industry is under enormous pressure to deliver high-quality care and healthcare services as the world’s population continues to grow. People are now more than ever wanting smart healthcare services, programs, and wearables that will improve their quality of life and increase their lifespan.
The healthcare industry has consistently been one of the biggest supporters of cutting-edge technology, and machine learning and artificial intelligence are no exception. Similar to how AI and ML quickly impacted the commercial and e-commerce sectors, they also discovered a wide range of applications in the healthcare sector.
In fact, machine learning, a branch of artificial intelligence, has become increasingly important in the field of healthcare, with applications ranging from developing novel treatments and medications, lowering costs, and managing patient data, to remote monitoring and many other things.
The potential for artificial intelligence (AI) and machine learning (ML) applications to join the healthcare and pharmaceutical industries is growing as a result of this need for “better” healthcare services. There is no shortage of data in the healthcare industry, and now is the perfect time to use AI and ML technologies to fully utilize this data. Healthcare is not exempt from the effects that AI, ML, and deep learning are having on today’s world.
It is also crucial to include machine learning in the healthcare sector’s design because the data burden is expanding exponentially (due to the rising incidence of diseases and the population’s constant growth). The possibilities are unlimited with machine learning. Modern-day machine learning (ML) technologies are improving the healthcare sector.
According to research firm Frost & Sullivan, by 2021, AI will bring in close to $6.7 billion for the global healthcare sector. Big data and machine learning have the potential to create up to $100 billion yearly in the healthcare sector, according to McKinsey. The healthcare industry now has the opportunity to use cutting-edge tools to provide better care because of ongoing developments in data science and machine learning.
1. Analytical Pattern Imaging
- The use of machine learning techniques and algorithms to improve image analytics and pathology is currently of special interest to healthcare organizations all around the world. Radiologists can use machine learning software to assist them to recognize small changes in scans, which will enable them to discover and diagnose health risks early on.
- The use of Google’s ML system to detect malignant tumors in mammograms is one such ground-breaking development.
- Additionally, very recently, researchers at Indiana University-Purdue University Indianapolis achieved a big advancement by creating a machine learning algorithm to predict (with 90% accuracy) the likelihood that myelogenous leukemia will relapse (AML). In addition to these developments, Stanford researchers have also created a deep learning algorithm to recognize and diagnose skin cancer.
2. Individualized care and behavioral modification
- The percentage of electronic health records used in healthcare increased from 40% to 67% between 2012 and 2017. Naturally, this entails easier access to personal patient health information. Health care professionals (HCPs) can more accurately identify and evaluate health issues by combining this specific medical information about individual patients with ML applications and algorithms. Medical providers can use supervised learning to forecast a patient’s health risks and dangers based on his symptoms and genetic information from his medical history.
- Exactly this is what IBM Watson Oncology is carrying out. It is assisting doctors in creating better treatment plans based on an optimum selection of therapy options using the medical information and medical history of patients.
- The adjustment of behavior is a key component of preventative medicine. In order to affect patients’ positive behavioral reinforcements, ML technologies are helping to step up behavioral modification. An ML-based app that passively monitors and recognizes a variety of physical and emotional states has been released by Somatic, a B2B2C data analytics business. This aids medical professionals in comprehending the kind of behavioral and lifestyle modifications necessary for a healthy body and mind.
- Startups and healthcare institutions have begun to use ML apps to promote behavioral changes. A good example is the data-analytics B2B2C software platform Somatic. Its machine learning program uses “detection of hand-to-mouth movements” to assist people to comprehend and evaluate their behavior, enabling them to open up and make decisions that are life-affirming.
3. Drug Development & Production
- Machine learning applications have become increasingly prevalent in the early stages of drug discovery, from the initial screening of a medication’s ingredients to its predicted success rate based on biological parameters. The main foundation for this is next-generation sequencing.
- Pharma businesses employ machine learning in the development and production of new drugs. But right now, this is only possible with unsupervised ML, which can find patterns in unprocessed data.
- The goal is to create precision medicine that uses unsupervised learning to help doctors find the underlying causes of “multifactorial” disorders. One of the top contenders is the MIT Clinical Machine Learning Group.
- Its study on precision medicine attempts to create algorithms that can aid in better understanding disease processes and, in turn, produce effective treatments for conditions like Type 2 diabetes.
- In addition, research and development (R&D) technologies, such as next-generation sequencing and precision medicine, are being used to identify different therapy options for complex disorders. ML-based technologies are employed by Microsoft’s Project Hanover to create precision medicine. Even Google has gotten on board with the drug discovery trend.
4. Recognizing Illnesses and Making Diagnoses
A significant advancement in diagnosis has been made thanks to machine learning and deep learning. Today, thanks to this cutting-edge technology, clinicians may identify disorders that were previously difficult to identify, such as hereditary diseases or early-stage tumors or cancers. For example, IBM Watson Genomics combines cognitive computing with genome-based tumor sequencing to advance the diagnosis process and enable early therapy. Then there is Microsoft’s 2010 Inner Eye project, which seeks to create ground-breaking diagnostic tools for superior picture processing.
5. Robotic Surgery
Robotic surgery has made it possible for doctors to operate successfully and precisely even in the most challenging circumstances. The Da Vinci robot is one illustration. With the help of this robot, surgeons may control and manipulate artificial arms to carry out procedures in the human body’s small places more precisely and with fewer tremors. Given that hair transplant techniques require precise delineation and precision, robotic surgery is frequently utilized in these procedures. Today, robots are leading the way in the surgical industry. By incorporating real-time surgery metrics, data from successful surgical experiences, and data from pre-op medical records within the surgical procedure, robotics powered by AI and ML algorithms improve the precision of surgical equipment.
