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Machine learning is an emerging field of AI (Artificial Intelligence) that ML professionals are using in multiple fields and industries. With its undeniable importance and benefits, the new branch of AI is being used in fields like healthcare, finance, business, retail, travel, media, mechanical engineering, electronic engineering, and agriculture as well. But have you wondered what machine learning is and how it works? In this article, we will solely focus on machine learning in the healthcare sector. We will review the definition and types of machine learning, along with its application in healthcare and the top 10 apps developed for ML in healthcare.

What Is Artificial Intelligence?

The first step to understanding or learning a new field or topic is to go in-depth and find the roots of the topic. In the case of machine learning, it is necessary to understand what Artificial Intelligence (AI) is, as ML is the subbranch of AI. By definition, Artificial Intelligence (AI) is a computerized program that demonstrates the cognitive ability of a human being while performing different tasks.  

In simpler terms, AI is an umbrella term for all the programs that function humanly. These programs can think and resolve problems like a human being would. Tasks like image recognition, language processing, problem detection, etc., are all carried out by AI-enabled programs. 

A List Of Sub-Branches Of AI

Before moving to the concept of machine learning, we will review a list of subbranches or programs that come under the domain of AI. 

  1. Machine learning.
  2. Deep learning.
  3. Robotics.
  4. Analytics.
  5. Natural language processing.
  6. Cognitive computing.
  7. Fuzzy logic.
  8. Speech recognition.
  9. Data mining. 
  10. Expert system. 

Each of these systems and other systems of AI is responsible for functioning and carrying out tasks that human beings would do. The benefit of AI is that it makes task completion faster and easier. Industries and businesses are now using these technologies to perform tasks in a much more effective and efficient way.

What Is Machine Learning?

Machine learning is one of the most beneficial products of AI. Before its development, AI programs were limited to automating low-level tasks in a few industries. But, with the help of ML, computers can now perform tasks that they are meant to do and evolve and expand their working area. In other words, machine learning algorithms help utilize large amounts of data to learn and make decisions without needing to be programmed or trained.  ML included using statistical techniques to carry out essential tasks without human supervision. 

How Does Machine Learning Work?

As mentioned above, machine learning algorithms are trained to perform tasks and make inferences independently without needing constant human assistance. Still, they do need some training by individuals in the initial stage. Machine learning works by feeding the programs with a good amount of data and utilizing different algorithms. The machines are then trained on this data, which helps them make decisions and complete the tasks just as human beings or professionals would. 

If we compare the working of machine learning to traditional ways, the traditional procedure included feeding data and a well-written program into the machine to get the output. Meanwhile, in machine learning, the input data and output are both fed during the training phase, which helps the machines to work out a program for themselves. 

Types Of Machine Learning

We will now explore the various types of machine learning to understand each sub-branch in depth. 

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning

  • Supervised Learning

By its name, supervised learning includes the presence of professionals to feed label training data and variables into the algorithms. The machine learning programs are then allowed to assess the tasks and carry out procedures with both input and output of the data.

  • Unsupervised Learning

Unsupervised learning includes the usage of unlabeled data. Once the algorithm is presented with a specific data set, the program will try to find connections between available unlabeled data and the data presented to assess tasks. This kind of ML includes predetermined data, recommendations, and predictions the algorithm is fed with. 

  • Semi-supervised Learning

This type of ML includes the mixtures of the above-mentioned methods. Data scientists or professionals might feed the algorithm with labeled data. But still, the program is free to explore data sets and develop an understanding to carry out essential tasks. 

  • Reinforcement Learning

Reinforcement learning includes training an ML algorithm to complete a multi-step process with definite rules under the supervision of a data scientist. The professional will train the algorithm to do so by giving positive or negative cues with the completion of each step. This way, the program will be trained and ready to complete tasks independently. It is important to remember that while training, the algorithm will decide what step to take on its own, and only then will the trainer present the cue.  

Machine Learning In Healthcare Industry

With its benefits and multiple uses, machine learning has entered almost every developing field worldwide. One such field is the healthcare industry. Machine learning in healthcare is not a new thing. It has been utilized by professionals and researchers to introduce new and efficient ways of task completion. But what gave it rise was Covid-19. In the time of need, the healthcare sector focused on using machines to carry out essential tasks as a source of help. Machine learning enables the successful completion of clinical operations, drug development, surgery, data analysis, and much more. 

Moreover, patients are now starting to put their valuable trust in ML. With the help of these programs and chatbots, ML can keep healthcare records, analyze or recommend treatment plans, and improve overall health. In the next section of the article, we will review the practical applications of machine learning in healthcare.  

Practical Applications Of Machine Learning In The Healthcare Sector

ML has focused on improving the quality of medical services in many ways. With the help of research on these practical applications, doctors and patients are now moving toward new ways of modifying behaviors and treating illnesses. Now we will discuss a few applications of ML in the medical sector. 

  • Used For Disease Identification.
  • Medical Imaging.
  • Keeping Health Records.
  • Behavior Modification.
  • Robot-Assisted Surgery.
  • Discovering New Drugs.

  • Used For Disease Identification

The first application of machine learning in healthcare is identifying and diagnosing diseases. With the help of symptoms and medical history, ML programs can easily identify diseases that humans sometimes cannot diagnose.  This helps in the early detection as well as treatment of illness. 

  • Medical Imaging

Another application of ML in healthcare is that it helps professional doctors to detect abnormalities and areas of problem in various medical images. Machine learning in healthcare carries out crucial medical imaging by analyzing MRIs, radiology scans, infections, tumors, injuries, etc.

  • Keeping Health Records

Keeping physical health records and reports of patients in one place is a task on its own, which includes the added responsibility of maintaining past as well as present records. With the help of machine learning in the healthcare sector, it is easier to keep an essential record of patients, along with their medical history, in one place. This record helps in designing and implementing treatment procedures in the future to compare and know the effectiveness of the current treatment plan. 

  • Behavior Modification

Machine learning is not only famous for its usage in the surgical and medical domains. Instead, the scope of machine learning in healthcare is vast. It focuses on the overall health of an individual. While keeping track of your health record and condition, this technology will also help you in improving your overall behavior by modifying healthy patterns and eliminating the threatening ones. 

  • Robot-Assisted Surgery

One of the most shocking but valuable developments in the history of the medical industry was made possible through machine learning in healthcare. Just like chatbots, technology has now introduced ML-enabled robots to carry out complicated and critical surgeries with less time consumption and energy usage. These robots are trained to conduct surgeries like professional doctors with reduced blood loss, speedy recovery, and pain risks or side effects. 

  • Discovering New Drugs

Last but not least, machine learning in healthcare has also benefited the industry in discovering new drugs for treatment. The process of discovering and creating drugs is way faster than usual pharmaceutical experimental procedures. This has not only helped the healthcare sector but has aided the economic sector as well. 

Top 10 Apps Of Machine Learning In The Healthcare Industry

In the last section of the article, we will review ten machine-learning apps in the healthcare industry that facilitate patients and professionals. Healthcare app developers focus on devising apps for single and multiple healthcare purposes. Some of the examples are.

  • Tempus
  • Microsoft
  • PathAI
  • Ciaox Health
  • Subtle Medical
  • Pfizer
  • BioSymetrics
  • Orderly Health
  • MD Insider
  • Prognos

  • Tempus

Tempus was founded in 2015 in Chicago. The main focus of this application was to target one of the most dangerous diseases: cancer.  It helped collect clinical and medical data to develop effective treatment plans for cancer patients. It also helps in genomic profiling, clinical trial matching, and academic research.  

  • Microsoft

Microsft, founded in 1975 in Redmond, Washington, has proved to be a pioneer in developing significant technological advancements. One of its projects, ‘InnerEye,’ has helped utilize machine learning to differentiate between tumors and health anatomy. It utilizes 3D radiological images to help experts identify abnormalities. Another focus of  Microsoft is to utilize AI to produce medicines based on individual patient needs.

  • PathAI

PathAI is an ML-enabled app that focuses on facilitating pathologists in making quick and accurate diagnoses for diseases. It was developed in 2016. Other benefits of this app include keeping medical records, processing samples, and carrying out procedures of clinical trials and drug development. 

  • Ciaox Health

Ciaox Health was founded in 176. The software of this application mainly focuses on the data of patients. Keeping recording and maintaining individual or specific data reports enables doctors to have access to the needed record for quick scanning, diagnosis, and treatment designing,

  • Subtle Medical

The focus of Suctle Medical is on medical imaging. Developed in 2017, this application provides experts with more explicit medical images to help diagnose and provide services to patients as quickly as possible.

  • Pfizer

Pfizer was developed in 1848. It utilizes AI technology of machine learning and natural language processing to identify how the body’s immune system can fight cancer. 

This enables the company to gather and analyze a massive amount of data to recommend possible ways of immuno-oncological treatment.  

  • BioSymetrics

By its name, BioSymetrics focuses on improving the precision of medicines in the medical sector. Using machine learning in the healthcare industry, BioSymetrics helps organizations analyze raw data and relevant human disease models to understand and recommend improving medicines. 

  • Orderly Health

Orderly Health was developed in 2015. The main goal of this app was to serve organizations a way to introduce  ML-enabled chatbots that would help users to connect and communicate through chat, email, or video conferencing. It helps individuals in finding the least expensive medical providers. 

  • MD Insider

MD Insider also focuses on connecting patients with skilled doctors by gathering large amounts of data through ML technology. This app provides results based on the area of expertise, reviews, years of experience,e locality, qualification, etc. 

  • Prognos

Prognos was founded in 2010 to gather patient medical history in order to provide early diagnosis, quick treatment, and personalized medicines for each disease. 


With its fast-improving success, machine learning in healthcare is all set to make its name in the market. Multiple healthcare app developers are developing to provide different medical services and solutions. If you are someone who is looking for guidance and consultation in the app development journey. It is time for you to connect yourself with Celect Studios – a leading mobile app development company known for its valuable and success-guaranteed app solutions.

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