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Machine Learning in Healthcare

Machine Learning in Healthcare

In the age of developing digital innovation and big data, Machine Learning in Healthcare has become very important in terms of data-oriented health services, clinical decision support systems (CDS) and care services. Clinics, hospitals, doctors and other healthcare organizations are working on developing digital and automated clinical systems. Software developers are in collaboration with healthcare workers, to develop new systems that can help diagnose and treat diseases, looking for smart solutions with Machine Learning technology.

Machine Learning in Healthcare is an area of the healthcare industry that is rapidly developing with wearable devices and sensor technologies. The cooperation of medical science with technology make promising advances in finding new methods of treatment and care for patients. With the development of artificial intelligence, IoT technologies and big data exchange, machines have the ability to analyze patients, recommend treatment or identify risks.

What is Machine Learning?

Machine Learning is a study in which algorithms learn structural functions from data, and they make estimates based on the data they learn. ML, which is a sub-field of Artificial Intelligence, learns from the data by using mathematical and statistical models. There are several different algorithmic methods which include those mathematical and statistical models.

  • Supervised Learning: Predicts outputs with inputs from labeled data.
  • Unsupervised Learning: Predicts structures and patterns from unlabeled data.
  • Reinforcement Learning: Based on behavioral psychology, the algorithm interacts with the environment and creates its own movement style using the feedback it receives.

Today, these methods appear in many areas such as automotive, social media, and banking systems. For use in medicine, medical professionals and software developers have started working together.

Usage of Machine Learning in Healthcare

ML technology began to assist patients with complex conditions and to develop and discover new drugs. Some of the areas where ML is used in the health sector are as follows.

  • Disease Diagnosis

One of the main goals of health institutions is the rapid diagnosis of diseases. In matters such as cancer, doctors fight the disease by using various treatments and medications, however, there is always a margin of error. ML technology is used in many fields, such as oncology, for diagnosis and treatment. In 2017, Google trained its models to diagnose cancer and reached 89% success rate.

  • Diagnosis of Medical Imaging

Medical imaging and obtaining the disease image are one of the factors that facilitate treatment. ML technology plays a big role, thanks to the rich data sources that can be used, in the diagnostic process. ML estimates can be interpreted by healthcare professionals to provide a rapid diagnosis and treatment process.

  • Drug Discovery

Machine Learning technology has the capacity to discover new drugs. It can offer new drug recommendations by quickly matching the effect of diseases with the features of the human anatomy. It can quickly analyze all aspects of a disease. Companies such as IBM and Google have created ML systems that develop new treatments for diseases and have started looking for new treatment methods for diseases such as cancer and diabetes.

  • Robotic Surgical Tools

ML technology has taken its place in operating rooms. The use of robots has become widespread in surgeries that require surgeons to perform in very detailed or narrow areas. Robotic limbs have become more reliable than human hands. It is preferred in fine interventions because of its features such as no shaking, no tiring. It is also used to identify certain parts of the body, for example, it can define the hair follicles before hair transplant surgery.

Machine Learning and Real-World Evidence

The use of Machine Learning enables the analysis and use of large-scale medical data. Real World Evidence (RWE) contains a lot of data to assist with the study and investigation of diseases. Therefore, the more medical institutions start using ML, the faster progress will be made on issues such as treatment and prognosis. Medical examinations in countries and international dimensions can be automated using Machine Learning. This information may play a role in classifying patients and determining the treatments. It can enable patients to be matched with the most appropriate clinics.

What is Massive Bio Synergy AI?

Massive Bio Synergy AI is a ML system that pairs artificial intelligence with genomic biomarkers and clinical research. It can scan more than 30,000 studies by targeting more than 100 data titles. Proprietary Artificial Intelligence Based Clinical Research Matching and Registration Platform Synergy-AI is a multivariate analysis software that is promising and promotes cancer research for patients.

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