Constant advancements are going on in the healthcare sector.
As new technologies evolve, it’s becoming challenging for healthcare professionals to stay updated. In the healthcare domain, many manual processes are involved, and those processes take an entire day/ night.
There comes the need for machine learning technology that smoothens doctors’ workflow and improves patient healthcare.
Imagine a situation where it would be easy for doctors to predict various diseases of patients, such as stroke, kidney failure, etc., based on machine learning algorithms and get accurate results within a few minutes.
In this blog, we will talk about the increasing popularity of machine learning in the healthcare domain and its probable benefits.
Table of Contents
What is Machine Learning In Healthcare?
Machine learning is the subset of AI that allows systems to learn from data, detect patterns and use such insights to automate healthcare professional workflows.
Machine learning algorithms in the healthcare sector effectively handle complex data, rearrange data in structured data sets and provide actionable insights to healthcare professionals.
The prime objective of ML-based solutions is not to compete with healthcare professionals; instead, it helps them streamline workflows, predict outcomes, provide risk assessment, and much more.
But that doesn’t mean that machines are there to eliminate doctors because patients need the human touch and expect care that doctors can provide.
ML technologies are considered a boon for the healthcare sector ranging from maximizing hospital efficiency to making an accurate diagnosis.
Moreover, healthcare professionals can effectively utilize this life-saver technology to make future predictions and get various insights into large amounts of data that might go unnoticed by healthcare providers.
For Instance – A doctor recommends some medication to patients. In this case, Machine learning technology can help doctors validate the treatment plan by identifying patients with similar medical histories and who might be benefitted from this treatment plan in the past.
8 Benefits of Machine Learning In Healthcare
Here are 8 benefits of Machine learning in healthcare-
1. It Helps in Identifying Diseases And Diagnosis
One of the most important advantages of machine learning in healthcare is that it helps medical professionals to analyze medical images and reports.
As machine learning automates image diagnosis, doctors can detect abnormalities and do effective treatments for improving liver and kidney infections and much more.
Besides that, radiologists can better find the root cause of problems in the early stages of diseases because technology such as AI and ML provides a bigger picture of where the problem lies.
For example – A machine learning algorithm would be able to evaluate whether the patient has breast cancer by classifying an image in the way it did and generate valuable insights for health experts.
2. Drug Discovery And Manufacturing
Pharmaceutical companies are integrating machine learning technology into drug discovery to automate repetitive data processing and analysis tasks.
Moreover, drug discovery is a time-consuming process wherein thousands of elements must be tested together. Machine learning for healthcare helps to find new drugs and provides new ways to treat complex diseases.
Machine learning provides several benefits in the drug discovery process-
It reduces the duration of clinical trials by predicting how potential drugs will perform
Find out if existing drugs can be combined to create new treatment
It helps you discover new drugs by testing various compounds
With the help of deep learning models, researchers can predict successful drug molecules leading to a speedy drug discovery process.
For Instance– Pfizer’s pharmaceutical company used IBM Watson for oncology research. The company used Watson, a cloud-based offering for discovering new drugs.
The researcher took around 3-4 hrs. to read 200-300 articles a year while Watson processed around 25 million Medline abstracts, and Pfizer employees quickly created treatment plans for drug combinations.
Thus, it makes the drug-creation process faster. Moreover, doctors can use machine learning to treat multifactorial diseases and suggest personalized medicines and treatment options.
3. Robotic Surgery
Another significant benefit machine learning provides in the healthcare sector is Robotic surgery. Research studies suggest that India lacks doctors and trained medical staff.
According to the World health organization, the ratio of doctors to patients stood around 1.2 doctors per 1000 patients in 1991. That’s why machine learning systems exist to provide correct patient treatment at the perfect time.
Here’s how Machine learning is helping in the process of robotic surgery-
With the help of robotic surgery advancements, robotization makes it possible for surgeons to reduce the length of surgeries and exhaustion of surgeons.
Machine learning provides improved surgical robotics, which means there are many times when the hands of surgeons shake during surgery, or it could lead to further complications for patients. So, there comes the need for surgical robots.
4. Personalized Medication
Machine learning provides personalized treatment medication by analyzing patients’ past and real-time data and creating more customized treatment plans.
Besides that, healthcare professionals need machine learning technology to generate customized medicine solutions that align with individual characteristics. These treatment plans are not only effective but also help you in better treatment of disease.
In the traditional approach, physicians are limited to making treatment plans from specific diagnoses. With machine learning, everything is taking the front seat.
For Instance – IBM Watson technology uses machine learning to analyze patients’ medical history and then come up with curating multiple treatment options for patients.
5. Clinical Trial And Research
Many healthcare professionals wonder how to use machine learning in the healthcare industry. But, when it comes to research and clinical trials, machine learning has a variety of applications.
If you asked someone who has been in the pharma industry, they would tell you that clinical trials cost a lot of money and would take years to complete.
On the other hand, machine learning contributes to increasing the efficiency of clinical trials ranging from pre-clinical drug discovery to pre-trial planning.
Moreover, researchers can predict who might be suitable candidates for these trials by analyzing data from social media or previous doctor visits.
6. Better Radiotherapy
Radiotherapy involves treating cancer through radiation. When treating cancer with radiotherapy, making a map of where the cancer is and where healthy organs are inside the body is important. The process becomes time-consuming for physicians.
With Machine learning, automatic image analysis helps doctors automate the detection of cancer and physiological structures in the human body.
Machine learning algorithms make diagnosis easier as it learns from many available samples.
Research studies suggest that using deep learning algorithms reduces manual contouring time from 20 minutes to 7.8 minutes.
Thus, healthcare professionals are taking full advantage of machine learning ranging from patient consultation to monitoring the effects of treatment.
7. Smart Electronic Health Recorder
If you are manually maintaining health records, that’s a whole tiring and exhaustive process. Health professionals can save time, effort, and money through machine learning.
Earlier electronic health record systems created challenges for healthcare professionals, such as cognitive workload, endless documentation, and user burnout.
A smart electronic health recorder system aims to sort out patient queries or refine the manual record system into an automated system. Such a system substantially reduces data errors such as duplicate data.
By using AI and ML technology, routine processes can be automated, improving the clinical documentation processes.
For Instance – MIT is using advanced technology to create smart health records. It uses machine learning tools to help with clinical treatment diagnosis and suggestions.
8. Prediction Of Liver Disease
Research studies suggest that liver disease is a significant issue that might affect millions of people globally. If such diseases can be detected early, it would lead to better patient outcomes and reduce the healthcare system’s burden.
Doctors predicted liver-related diseases by analyzing liver function blood tests and scan reports. Such traditional methods take more time and are expensive.
With the help of machine learning, predicting liver diseases has become easier than ever. Now, data mining technologies are used extensively for medical diagnosis.
For predicting liver diseases, healthcare professionals are making use of the Indian liver Patient dataset system. Therefore, the significance of machine learning algorithms in healthcare can’t be ignored.
Besides that, AI/ML technologies are used to predict liver diseases at a lower cost and provide better health care treatment.