Technology in healthcare helps provide better treatment and save lives. But do you think it is as simple as it sounds? Quality of services, valuing human life, and delivering better outcomes are the primary purposes of using advanced technology like Machine Learning or ML in medicine.
Businesses and SMEs are extensively adopting artificial intelligence and machine learning (a subset of AI) in various industries. The medical sector is no different. Many research centers and healthcare organizations have recognized the potential of machine learning and are actively improving their patient care services and administrative processes and systems.
Machine learning algorithms become more accurate as they gather and process data over time. As a result, it increases accuracy and efficiency. From screening a patient to prescribing the right medicines, AL can empower the healthcare providers to understand the patients’ condition down to the minute details.
ML services help in automating recurring tasks. It saves time for the nurses and allows them to focus on the patient rather than spend their energy on filing records, processing claims.
Patient management is one of the most challenging things to handle for healthcare centers. Artificial intelligence and machine learning can help streamline the processes to provide high-quality treatment and services to patients.
Chatbots are not the only way to use machine learning. Machine learning is fast becoming a part of the pathology, oncology, and other such departments. ANN (artificial neural networks) helps with image modeling, disease diagnosis, identifying harmful cells (cancerous cells) in the early stages, and so on.
Of course, this is only limited to direct patient care. Machine learning algorithms are just as valuable for medical research, where labs run clinical trials, discover and develop new drug combinations, and process a vast amount of historical and real-time data to study and control the spread of an outbreak or an epidemic.
It is one of the main reasons why the demand for artificial intelligence in the medical industry has doubled during the last year. The Covid-19 pandemic resulted in various researchers and healthcare centers relying on technology to understand the spread of the virus and look for ways to stop it.
Several organizations hire offshore machine learning consulting companies to implement AI technology in their processes. ML has various roles to play in the medical industry, and here are some of the most important ones.
Data entry might have become more accessible during the last few years, but maintaining the health records up to date is still a labor-intensive job. Nurses and the non-medical staff spend a lot of time updating the records. If this were to be handled by machine learning, wouldn’t it save time, money, and other resources?
AI can help build and maintain smart health records for every patient:
– Whether it is about storing the records on the cloud and making them easily accessible to the medical staff or
– Using ML-based handwriting technology to understand and convert written files into other formats. When the necessary patient details are already available on the file, the doctors will have more data to understand the patient’s medical condition. Hence, it contributes to a higher quality of treatment.
It would seem quite natural that machine learning is very good at diagnosing diseases. This is one of the prominent areas where machine learning is highly effective. Machine learning algorithms can quickly identify diseases like cancer that are hard to diagnose early (especially skin cancer). IBM Watson Genomics by IBM Watson Health in partnership with Quest Diagnostics is a prime example.
Using genome-based tumor sequencing with cognitive computing, harmful cells were detected faster and with greater accuracy. Artificial Intelligence consulting services are used by healthcare centers for predictive analytics to diagnose brain diseases like depression. Furthermore, it helps plan a proper treatment chart for the patient before getting too late.
Research and development (R&D) is an inherent part of the medical industry. By using machine learning during the early stages of discovering and developing drugs, researchers can know the possible outcomes and the success of using the medicine to cure a disease.
Researchers can identify the potential side-effects of using the drug and find alternate components to reduce the side-effects while increasing efficiency.
Next-generation sequencing and precision medicine are two AI technologies used in discovering and manufacturing drugs.
Precision medicine helps in identifying multifactorial diseases and finding alternative therapies. Project Hanover (by Microsoft) used ML technology to treat cancer and helps in personalizing the drug combination for patients suffering from Acute Myeloid Leukemia.
Personalization is seen everywhere, and the medical industry is no exception. It helps to provide better and accurate treatment to patients based on their health conditions rather than based solely on the disease troubling them. In a way, this point is an extension of the previous one. Though doctors can only choose from a limited set of data, ML makes giant leaps in this region.
IBM Watson Oncology is the first thing that comes to mind when you talk of using the patients’ history to predict their health and make a list of treatment methods that are best suited for their conditions.
Diabetes is both common and dangerous. It leads to more health complications by damaging other vital organs in the body such as the heart, kidneys, and even the nervous system. While type-2 diabetes is something many people are familiar with, type-1 diabetes (also called juvenile diabetes) is still not known by many. Do you know that WHO estimated around 1.5 million deaths in 2019 due to diabetes?
Using Artificial Intelligence in Healthcare to predict diabetes can help physicians detect the disease in its early stages and help patients find the correct method to control their blood sugar levels. Predicting diabetes can save lives and allow the patients to lead a quality life.
Fatty liver or liver cirrhosis is a liver disease caused by alcohol abuse. The simplest way to reverse it is to stop consuming alcohol. However, people who aren’t aware of their condition and continue drinking can lead to untimely death. Liver cancer and chronic hepatitis are two other major causes that lead to patients’ demise.
Detecting harmful changes and cells in the liver during the initial stages significantly increases the patient’s lifespan. ML algorithms like classification and clustering can help doctors process vast amounts of data and predict liver diseases before becoming life-threatening.
A well-known B2B2C data analytics company, Somatix, has released a machine learning-based app that recognizes and tracks our activities (conscious and unconscious) from day-to-day life. This data helps people understand what triggers them, what makes them happy, how they react to different situations, etc. The app aims to help people make changes to their behavior. Things that could affect the person in the long term can be controlled and gradually changed before making life harder for the person.
Clinical trials take years. Labs and governments invest millions of money into clinical trials every year. But by using AI solutions for predictive analytics, data can be collected from several sources and processed in real-time.
Machine learning makes it easy to identify the best sample for testing, closely monitor the trial participants, and reduce the risk of human error. ML algorithms can help researchers finish the trials faster than before, too, with greater accuracy.
When personal health records are willingly uploaded and shared by patients, it becomes easier for researchers and doctors to analyze the data and gain in-depth insights. Be it Apple’s Research Kit or IBM’s new product, apps and devices are developed to collect data in real-time and help doctors detect diseases in the initial stages. While IBM’s product deals with diabetes and insulin-based data, Apple collects data about Asperger’s and Parkinson’s diseases.
Surgery is perhaps one area where many people know AI in the health industry. We are already aware of robotic arms assisting doctors in performing complex surgeries. Robotic surgery will soon become a common sight in most hospitals. Relying solely on robots to perform surgery will take a while, though.
Until then, robots will supply the instruments to the doctors in the operation theatre, thereby reducing the operating time by around 20%. This increases the chances of saving the patients’ lives and reducing complications. It is estimated that robots will become the standard assistants to doctors by 2025.
Data from social media, websites, the satellite, etc., are collected in real-time to track how a virus is spreading and when it would reach the peak stage. Predicting the outbreak of pandemics and epidemics will help governments take the necessary measures to control the virus and minimize its impact on people. Artificial neural networks (ANNs) are used to process the collected information and arrive at a conclusion to help the authorities make the right decisions.
Though AI and ML have been making significant progress in the medical industry, there are specific challenges healthcare organizations need to address before adopting AI solutions.
Successful ML implementation in the medical industry is possible when the healthcare and research centers overcome the challenges.
Allied Market Research says that the global healthcare market for AI will touch $22.8 billion by 2023. Applications like robotics surgery would value about $40 billion by 2026. Virtual nursing assistants are said to value about $20 billion by the same year (2026). The medical industry is also expected to generate 2 million jobs by 2025 with artificial intelligence.
Machine learning has a vital role to play in the medical industry. Machine learning consulting company provides services to improve diagnosis and drugs to result in quality outcomes.
With increasing investments in AI, we’ll be witnessing the use of advanced technology to improve the overall health of patients and give them a chance to lead happy and pain-free lives as much as it’s possible.
Data Engineering
AI & Machine Learning
By Use Cases
Business Intelligence & Tableau
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