With the worldwide population booming, the healthcare industry must adapt and develop the most innovative technologies to gather and analyze such massive clinical data and information about patients. 

It’s not about MRIs, ultrasounds, and surgical tables anymore. It’s about bringing in machine learning (ML) and artificial intelligence (AI) to make the real-world healthcare system high-powered and error-free.

In 2017, Google developed an algorithm that can detect diseases, such as cancer, with 89% accuracy. Three years later, in 2020, smartphone-based mental healthcare apps are becoming the biggest buzzword, thanks to digital behavioral solutions, and machine learning in healthcare has just started.

The purpose of ML in healthcare is to minimize human error without limiting the human factor. A doctor’s assistance and a caring hand are crucial in every clinical decision. Patients need care and understanding, but it’s hard for medical staff to keep an individual approach when, on average, each doctor manages a panel of a whopping 1,400 patients.

Machine learning in healthcare is supposed to make it more efficient and reliable than ever before. In 2021, with increased demand for these specialized technologies, the market and the research field is booming with innovations. Read this article to learn more about how the latest machine learning algorithms perfect patient care and take the medical industry to the next level.

Personalized Medicine

Personalized treatments are developed by introducing the patient’s medical information to an appropriate algorithm. With the use of machine learning, statistical and computational tools, the application creates a personalized treatment strategy formed upon their symptoms and genetic predispositions.

Experts predict that the next few years will bring many revolutionary breakdowns concerning machine-learning-based healthcare technologies that can significantly reduce healthcare costs.

Prognosis of Liver Disease

The liver plays a crucial role in maintaining healthy chemical reactions in our bodies, supporting the cells’ and the entire organism’s living state. In recent years machine learning, artificial intelligence, and data mining experts have developed several concepts to predict liver diseases such as chronic hepatitis, liver cancer, and cirrhosis.

There are already a few applications on the market, such as MATLAB, SVM (Support Vector Machine), or Disorders Dataset, that could help predict diseases using large clinical data clusters. Machine learning can be applied to facilitate this process even more.

Clinical Trials and Research

Clinical trials take an extensive amount of resources and time and could take years to complete. With the use of machine-learning-based algorithms and technology, researchers can mark all potential clinical trial candidates by reviewing various data markers, like medical history, previous hospital visits, medical records, and so on.

Scientists can then target the best sample size for testing and ensure instantaneous monitoring of the participants’ medical records.

Robotic Surgery

Robotic surgery is the latest buzzword and most widely developed field in the healthcare industry. Machine learning applications supporting robotic surgeries are grouped into four main categories: 

• Surgical skill evaluation 
• Suturing
• Surgical workflow modeling
• Improvement of robotic surgical materials.

Some surgeries can take up to 96 hours, so the primary purpose of applying the machine learning approach is to reduce the length of the procedure and minimize surgeons’ fatigue.

Diabetes Prognosis

Diabetes mellitus is one of the most common diseases leading to the death of an individual. It affects the heart, nerves and can lead to kidney failure. By using machine learning and data science, scientists try to detect diabetes at an early stage and apply instant patient care and a precise treatment plan.

To improve the early diagnosis rate of diabetes, doctors use tools such as KNN, Decision Tree, or Random Forest. Some of the diabetes algorithm datasets, such as naive Bayes classifiers, are free and easily accessible online.

Drug Discovery

Drug discovery, next to disease prediction, is another healthcare area that can develop significantly with the help of machine learning. 3D printing technologies such as precision medicine and next-generation sequencing are already heavily utilized to discover alternative procedures for complex and deadly diseases.

ML approach provides a broad set of tools to improve discovery and medical decision-making for specifying questions with high-quality information. As such, it can help target validation, analyze pathological digital data, and identify prognostic biomarkers in clinical trials.

Machine Learning In Healthcare

Although machine learning and artificial intelligence are new terms in healthcare, they are worth over $46 billion as an industry.

After the COVID-19 outbreak, many hospitals had to put on hold all non-emergent surgical procedures for safety reasons and due to various restrictions. Since then, waiting lists continue to grow exponentially.

The medical and healthcare advantages of the robotic surgical and disease prediction approach have never been more vital than during and after this pandemic. Machine learning algorithms are meant to decrease hospital resource utilization at a time when the number of waiting patients is higher than ever before.

Katarzyna Środek

Katarzyna Środek is a Tech Journalist based in Poland. She writes about Machine Learning, Artificial Intelligence, and the Internet Of Things. Her goal is to make the latest technology news and innovations easy accessible to everyone. She strikes for opening her own publishing company, working with world-class authors, and travel the world.

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