The Revolutionary Use Cases of Machine Learning in Healthcare in 2021

Machine Learning in Healthcare: Lately, it seems like every time you open your browser or randomly scroll through your news feed, you certainly come across the term Machine Learning and its impacts on humans or on the advancements of artificial intelligence. Machine learning is one of the most used technologies in this era.

It has multiple capabilities that can alter the dimensions of businesses across the industries. From being considered a niche technology, machine learning is now thriving within companies in all sectors.

Globally, industries are leveraging machine learning to accelerate innovation and enhance customer experience. And with the increasing population, just like any other industry, the healthcare industry must adapt and develop the most innovative technologies to gather and analyze such massive clinical data about patients.

Now, more than ever, people are demanding smart healthcare applications, services, and wearables that will help them to lead better lives and increase their lifespan. By 2025, Artificial Intelligence in the healthcare field is estimated to increase from $6.7 billion to $27.2 billion.

The purpose of machine learning in healthcare is to decrease human error without curbing the human factor. Patients need the right care and understanding. And it’s hard for medical staff to keep a check on the individual patients when on average, each doctor manages a cluster of over 1000 patients.

Machine learning in healthcare is supposed to make it more efficient and reliable like never before. The above article tells you about how the latest machine learning algorithms play a significant role in perfect patient care and take the medical industry to another level.

Machine Learning in Healthcare – Here are a few popular machine learning use cases that are making it big in the healthcare industry.

1. Pattern Imaging Analytics

Today, healthcare sectors around the world are particularly interested in enhancing imaging analytics with the help of machine learning algorithms and tools.

Machine learning applications can help radiologists in identifying the subtle changes in scans, thereby helping them detect and diagnose various health problems at the early stages.

One such breakthrough advancement is Google’s ML algorithm to identify cancerous tumors in mammograms. Apart from path-breaking trends like this, researchers at Stanford have also developed a deep learning algorithm to identify and diagnose skin cancer.

3. Personalized Treatment – Machine Learning in Healthcare

The penetration rate of Electronic Health Records in healthcare rose from 40% to 67% in the past few years. This implies more access to individual patient health data. By integrating this personal medical data of individual patients with ML applications and algorithms, health care providers (HCPs) can detect and evaluate health issues better.

Based on supervised learning, medical experts can predict the risks and threats to a patient’s health according to the symptoms and genetic information in his medical background.

This is exactly what IBM Watson Oncology does. Using patients’ medical history and data is helping physicians to design better treatment plans based on an optimized selection of treatment choices.

3. Drug Discovery & Manufacturing

Machine Learning applications have paved their way into the field of drug discovery too, mainly in the preliminary stage, right from initial screening of a drug’s compounds to its estimated success rate based on biological factors.

Machine Learning is being used by pharma companies in the drug discovery and production process. However, today, this is limited to unsupervised ML that has the potential to identify patterns in raw data.

The main aim here is to develop precision medicine powered by unsupervised learning, which allows professionals to identify mechanisms for ‘multifactorial’ diseases. The MIT Clinical Machine Learning Group is one of the top players in the field.

4. Identifying Diseases and Diagnosis

Machine Learning, collaborated with Deep Learning, has made a remarkable breakthrough in the diagnosis process. All thanks to these advanced technologies, today, doctors can diagnose diseases that were previously hard to even think of being diagnosed- be it cancer or a tumor in any stage.

For example, IBM Watson Genomics combines cognitive computing with genome-based tumor sequencing to further the diagnosis treatment so that process can be started head-on.

5. Robotic Surgery – Machine Learning in Healthcare

Today, doctors can successfully operate even in the worst situation possible with great precision as robotic surgery is becoming the norm. These robots allow surgeons to control and manipulate robotic limbs to perform surgeries with the right precision and fewer tremors in tight spaces of the human body.

This application is also widely used in hair transplantation procedures as it involves fine detailing and delineation. Robotics empowered with AI and ML algorithms enhance the precision of surgical tools by including real-time surgery metrics, information from successful surgical experiences. According to the reports, robotics has reduced the duration of surgery by almost 21%.

6. Clinical Trial Research

The present-day applications of machine learning have a vast scope of improving clinical trials by researchers. By implementing smart predictive analytics to candidates of clinical trials, medical professionals could assess a comprehensive range of data, which not only reduces costs but also the time needed for conducting medical experiments.

And also deliver accurate results. Additionally, Machine Learning technologies can be used to identify potential clinical trial candidates, access their health history records, monitor them throughout the trial process, select the best testing samples, reduce data-based errors, and much more.

8. Improved Radiotherapy – Machine Learning in Healthcare

It is a proven fact that ML is immensely helpful in the field of Radiology. In medical image analysis, there is a multitude of discrete variables that can get triggered at any random moment. ML algorithms play a major role, as they learn from the many disparate data samples, they can better diagnose and identify the desired variables.

For instance, ML is used in medical image analysis to classify objects like lesions into various divisions- normal, abnormal, benign, malignant, lesion or non-lesion, etc. Researchers in UCLH are using Google’s DeepMind Health to develop such algorithms that can detect the difference between healthy cells and cancerous cells, and ultimately enhance the radiation treatment for cancerous cells.


Today, we are witnessing a medical revolution, and all thanks to advanced technologies like machine learning and artificial intelligence. However, technology alone will not improve healthcare, there should also be inquisitive and dedicated minds who can give meaning to such innovative technologies as ML and AI.

As there’s no stop to the emerging technologies, people are leveraging them at all costs. Hope we find new ways to discover more technological treatments.


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