In this post, we'll discuss 5 machine learning applications that, by 2022, will help the healthcare sector. These days, machine learning is more valuable and has a brighter future. Enter the subject now.

As the world's population continues to increase, there is tremendous demand for the healthcare sector to provide high-quality treatment and healthcare services. More than ever, people desire smart healthcare products, services, and wearables that will lengthen their lives and improve their quality of life.

One of the industries that have continuously made the highest investments in cutting-edge technology is the healthcare sector, and artificial intelligence and machine learning are no exception. The commercial and e-commerce industries were swiftly affected by AI and ML, and the healthcare industry also saw a wide range of applications for these technologies.

1. Analytical Pattern Imaging 

• The improvement of image analytics and pathology using machine learning methods and algorithms is currently of particular interest to healthcare organizations all over the world. Radiologists may identify subtle variations in scans with the use of machine learning software, which will help them identify and diagnose health issues early on.
• One such innovative development is the use of Google's ML system to find malignant tumors in mammograms.
• In addition, Indiana University-Purdue University Indianapolis researchers made a significant improvement recently by developing a machine learning system to predict (with 90% accuracy) the risk that myelogenous leukemia will relapse (AML). Stanford researchers have also developed a deep learning algorithm to identify and diagnose skin cancer in addition to these advancements.

2. Individualized care and behavioral modification

• Between 2012 and 2017, the proportion of electronic health records used in healthcare rose from 40% to 67%. Naturally, this results in simpler access to private patient health data. By merging this unique medical data regarding individual patients with ML applications and algorithms, health care professionals (HCPs) can more precisely diagnose and evaluate health conditions. Based on a patient's symptoms and genetic data from his medical history, medical professionals can use supervised learning to predict the patient's health risks and dangers.
• This is precisely what IBM Watson Oncology is doing. The medical data and medical histories of patients are being used to help clinicians develop better treatment regimens based on the best possible selection of therapeutic choices.

3. Drug Development & Production

• From the initial screening of a medication's ingredients to its expected success rate based on biological characteristics, machine learning applications have grown more and more common in the early stages of drug research. This is primarily supported by next-generation sequencing.
• Machine learning is used by the pharmaceutical industry in the creation of novel medications. But as of now, the only method that can do this is unsupervised machine learning (ML), which can identify patterns in raw data.
• The objective is to develop precision medicine that makes use of unsupervised learning to assist medical professionals in identifying the root causes of "multifactorial" illnesses. The MIT Clinical Machine Learning Group is a major contender.
• In its research on precision medicine, the company aims to develop algorithms that will help in the better understanding of disease mechanisms and the eventual development of effective treatments for ailments like Type 2 diabetes.
• In addition, various treatment options for complicated illnesses are being found using research and development (R&D) technologies including next-generation sequencing and precision medicine. Microsoft's Project Hanover uses ML-based technology to develop precision medicine. Even Google has embraced the trend of drug discovery.

4. Recognizing Illnesses and Making Diagnoses

Machine learning and deep learning have made tremendous progress in diagnosis. Clinicians may now diagnose diseases that were previously challenging to diagnose, such as inherited illnesses or early-stage tumors or malignancies, thanks to this cutting-edge technology. To advance diagnosis and enable early therapy, IBM Watson Genomics, for instance, blends cognitive computing with genome-based tumor sequencing. The 2010 InnerEye project from Microsoft aims to develop ground-breaking diagnostic tools for improved image processing.

5. Robotic Surgery

Even in the most difficult situations, doctors may now perform effective and precise operations thanks to robotic surgery. One instance is the Da Vinci robot. With the aid of this robot, surgeons may operate mechanical arms more accurately and steadily to perform treatments in the confined spaces of the human body. Robotic surgery is widely used in hair transplant techniques because they call for exact delineation and precision. In today's surgical sector, robots are setting the standard. Robotics driven by AI and ML algorithms increase the accuracy of surgical equipment by integrating real-time surgery measurements, data from successful surgical experiences, and data from pre-op medical records within the surgical procedure.