Healthcare is a vital industry that provides value-based healthcare to millions of people and also makes a lot of money for many countries. The US Healthcare industry alone has a total revenue of $1.668 trillion today. The US spends more per capita on healthcare than any other developed or emerging nation. Healthcare professionals and stakeholders all over the world are searching for innovative ways to fulfill this promise. Smart healthcare that is technology-enabled is not a fanciful idea. Internet-connected medical devices keep the health system from crumbling under the burden of the growing population.

Today’s technology allows healthcare professionals to develop alternative staffing models, IP capitalization and provide smart healthcare. It also plays a crucial role in billing and patient care. One area that is slowly gaining acceptance is machine learning in healthcare. Google has recently created a machine-learning algorithm that can identify cancerous tumors from mammograms. Stanford University researchers are also using deep learning to detect skin cancer. 

Machine Learning (ML), is being used in a variety of healthcare situations. Machine Learning (ML) in healthcare allows for the analysis of thousands of data points to suggest outcomes, give timely risk scores, allocate resources precisely, and many other applications. This article will focus on the most important applications of machine learning in healthcare and how they can change our perception of the industry in 2022.

Healthcare Machine Learning: The Potential

Machine Learning is one sub-division of Artificial Intelligence. It is used to “train” models using data. According to, 63% of the 1,100 US companies using Artificial Intelligence were focusing on Machine Learning. This is a versatile technique that can be used in many industries.

What does this have to do with healthcare? The industry’s organizational side could be boosted by the use of ML. 25 percent of regular nurse in the United States spends her time on administrative and regulatory activities. These routine tasks, such as claims processing and revenue cycle management, clinical documentation, and records management, could be easily automated by technology.

Another survey conducted by Harvard Business Review found that over 300 healthcare executives and leaders claim there is a problem in patient engagement. Over 70% of respondents stated that less than half their patients are engaged in treatment, and 42% claimed that less that 25% of their patients are engaged.

Greater patient involvement definitely brings better health outcomes for patients. Machine Learning can send targeted messages and automated alerts to trigger actions at crucial moments. There are many ways that ML can improve and personalize the treatment process.

What are the applications of machine learning in healthcare and medicine?

A bot system is one of the most useful Machine Learning for Healthcare applications. It makes the treatment process much simpler. Virtual nurses for patients act as healthcare assistants that can be spoken to and provide information about many diseases, conditions, and medications. A virtual assistant can be very useful if the patient requires real-time advice or it is difficult to reach a doctor. Data engineers are developing solutions to all medical problems that involve overall health monitoring and even helping to prevent or cure disease.

Apart from the popular use of Chatbots you should be very attentive to the implementation Machine Learning in Healthcare algorithms in:

• Oncology
• Pathology
• Rare diseases

Machine Learning in Healthcare algorithms is mainly Artificial Neural Networks. CNN (convolutional neuro networks) is an example of image recognition, detection, and identification. These layers of artificial neurons are linked together and pre-trained using a set of damaged cell images to “memorize the appearances of dangerous cells.”

Data scientists have a difficult time understanding these medical specialties, especially if they require deep learning and the most complex area of Machine Learning. It allows the creation of neural network and detection of cells that are harmful to the organism (like cancer cells).

Machine Learning in Healthcare technology in oncology searches for cancer cells with an accuracy level comparable that of an experienced doctor. Machine Learning can help pathologists when they examine organic fluids (blood, urine, and tissues). An automated model can analyze human sight faster than a microscope. Hospitals and research centers can reap the benefits of CNN-based patient diagnosis applications.

We must also accept that there is still a lot to learn about rare diseases, and how they relate to the characteristics of those who are afflicted. Machine Learning in Healthcare startups offer new ways to analyze patient photos and trace features.

Machine Learning can be used in healthcare in many other ways. Let’s take a look at them.

Top Machine Learning Applications for Healthcare

As smart medical devices become more common, technology-enabled health care is becoming a reality. Innovation is welcomed by the healthcare industry. AI in healthcare’s future looks bright. Google already has an algorithm that can detect cancer in mammograms. Stanford University scientists are able to identify skin cancer using Deep Learning. Artificial Intelligence processes thousands of data points and predicts risks and outcomes with precision.

Diagnosis and identification of disease.

This is a fair point to make. ML is a very effective tool for diagnosing cancer. There are many types of genetic diseases and cancers that can be difficult to diagnose. However, ML could help with many of these in the early stages. IBM Watson Genomics is an excellent example. This project combines genome-based tumour sequencing with cognitive computing to help in quick diagnosis. For example, PReDicT or Predicting response to depression treatment is a P1vital project that aims to make AI practical and useful in improving diagnosis and treatment in regular hospitals.

Improvement in health records

Even with all the technological advances, maintaining health records can still be a pain. Although it’s much faster today, it still takes a lot time. You could use vector machines or ML-based OCR recognition methods to classify records. Cloud Vision API from Google is one of the most prominent examples. MathWorks also offers ML handwriting recognition technology.

Diabetes prediction.

Diabetes is one of the most dangerous and common diseases. Diabetes can not only cause serious health problems, but can also lead to other serious diseases. Diabetes is most damaging to the heart, nerves, and kidneys. Machine Learning could save lives by allowing for early diagnosis of diabetes. A system that predicts diabetes could be built using classification algorithms such as Decision Tree, KNN, and Naive Bayes. Naive Bayes is the fastest in terms of computation time and performance.

Prediction of liver disease.

The liver is a key part of metabolism. It is susceptible to liver cancer, chronic hepatitis and cirrhosis. Although it is difficult to predict liver disease with large amounts of medical data accurately, there have been significant improvements in this area. Machine Learning algorithms such as classification and clustering have made a significant impact in this area. This task could be performed using the Liver Disorders Dataset (ILPD), or the Indian Liver Patient Dataset.

The best treatment.

Machine Learning is another great tool for drug discovery. Microsoft currently uses AI-based technology for Project Hanover. This project aims to discover personalized drug combinations to treat Acute Myeloid Leukemia.

Making diagnoses via image analysis.

Microsoft’s InnerEye project is revolutionizing healthcare data analysis. The startup uses Computer Vision for medical images processing to determine a diagnosis. InnerEye continues to make waves in healthcare analytics software as technology advances. Machine Learning will be more efficient and more data points can be analyzed in order to provide an automated diagnosis.

Individualizing treatment

Machine Learning in Medicine is making huge progress. IBM Watson Oncology, a leader in this field, offers many treatment plans that first analyze the patient’s medical history. Personalized treatment plans will become even more possible as advanced biosensors are available on the mass market.

Adjusting behavior.

This is an area that’s very fascinating to watch. How can you prevent cancer by giving tips about your day? This is exactly what Somatix, a B2B2C company, does. The application monitors our unconscious actions every day and warns us if they are potentially dangerous.

Clinical trial improvement and medical research.

Clinical trials can take many years and require significant investment. It is not a secret that they can be costly. ML offers predictive analytics that can identify the most qualified candidates for clinical trials based on factors such as one’s past history of doctor visits and social media activity. This technology could also reduce the chance of data-based errors, and suggest the most appropriate sample sizes.

Crowdsourced medical data can be used.

Researchers have today access to a vast amount of data that patients make available to them. This data is the future source for Machine Learning in Medicine. What is the importance of data analytics in healthcare? A partnership between Medtronic, IBM has already led to the ability to analyze, accumulate and make insulin information readily available in real time. There will be more opportunities as the Internet of Things (IoT), develops. Public data will also improve diagnosis and the issuance prescriptions for medication.

Controlling an epidemic

Data analytics is a term that refers to experts having access to data from satellites, social media trends and news websites. All of this data could be processed by neural networks to draw conclusions about epidemics around the globe. It is possible to stop dangerous diseases before they can cause severe damage. Third World countries lack the most advanced medical systems, so this is crucial. ProMED-mail is an Internet-based reporting platform that monitors outbreak reports across the globe. It’s probably the best example in this area. Artificial Intelligence is used extensively in Food Safety to help prevent outbreaks of disease on farms.

Artificial Intelligence Surgery.

This area is the most important for Machine Learning and will continue to grow in popularity. These are the main categories of robotic surgery:

• Automated suturing
• Modeling of surgical workflows
• Robotic surgical materials are being improved
• Evaluation of surgical skills.

The act of suturing is basically to close an open wound. This process can be automated to make it faster and relieve the surgeon of any pressure.

Although it’s too early to speak of surgeries performed solely by robots, these machines can now assist doctors in manipulating surgical instruments. It is expected that it will become an industry of special importance with a capital of around $39 billion. The robot can fetch the instruments needed for a medical procedure by lifting them with its robotic hands. This practice reduces surgical complications by half and decreases patient stay time by 20%. It collects data from every Artificial Intelligence Surgery and uses Machine Learning algorithms to analyze healthcare data.

Machine Learning in Healthcare Informatics

Machine Learning in Healthcare Informatics is a powerful tool for analyzing data. The electronic information available to doctors is improving. Doctors can now access information such as risk factors for stroke, kidney disease, and coronary heart disease. Doctors can access patients’ indicators based on blood pressure readings, race, family background, gender, and the most recent clinical examination data. The doctors then have valuable clinical insights that help them create a treatment plan and give the best care. The possible outcomes allow them to estimate the cost of the procedure, which makes treatment more affordable.

Conclusion

Machine Learning in Healthcare is a powerful tool that helps doctors classify and sort health data. It also speeds up doctor’s clinical decisions. Machine Learning can make any predictions that could save lives or simplify surgery (e.g. the prevention of hypoxemia). Isn’t that enough? Human life is, without any doubt, the most precious thing. ML in Healthcare is a technology that contributes directly to future medical diagnostics and medicine. We will also discuss AI in Nutrition and other solutions.