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Role of Machine Learning in Healthcare
Medical Science has grown significantly with time. The quality of healthcare services and the ability to treat complex diseases constantly improve. Computers have helped doctors to do advanced diagnoses and analysis of the health condition of patients. Nowadays with the help of machine learning algorithms, the healthcare industry is reaching new heights. Artificial Intelligence engineers are also trying to develop robots that can operate patients, without any supervision. This will ease the task of doctors and medical providers as well as the precision of medical treatments will also get better.
Some advantages of using machine learning in medical science are given below −
Improvement in Diagnosis − Machine learning algorithms can be used to analyze images like MRI scans, X-Rays, etc., and detect the disease the patient has been suffering from. Since machine learning models are being used for diagnosis, the accuracy of the stuff has increased. Nowadays machine learning models are 92% accurate in predicting the mortality of COVID-19 patients.
New Medication and Treatment Plans − Deep Learning model can be used by healthcare organizations and pharmaceutical companies to identify relevant information in data that could lead to drug discovery, the development of new drugs by pharmaceutical companies, and new treatments for diseases. Also, these models can be used to analyze the data retrieved from medical research and find possible side effects of a certain drug. Likewise, by analyzing the patient’s data and the data for the drugs the models can be used to predict the most effective treatment for the patient.
Cost Reduction − Machine Learning models can be used to automate tasks like maintaining and updating patient records, scheduling appointments with doctors, etc. This would help organizations to cut out the costs of extra and redundant labor, ultimately medical treatments would become cheaper. Also, ML models can help patients by suggesting to them the most optimal therapy alternatives based on the patient’s medical history, lifestyle choices, genetic data, constantly changing pathological testing, genetic diseases, family history, etc.
Better Tracking − Machine Learning Algorithms can be used by industry professionals to develop devices that can proactively monitor a patient’s condition, the doctors in charge of the patient will be kept notified of the changes in the patient’s health status. This can help doctors to take well-informed and well-timed decisions.
Clustering − machine learning algorithms can be used to group similar medical cases and form clusters. These clusters can help analyze the patterns and conduct research.
Predictive Treatment − Identifying diseases like cancer, Alzheimer’s, diabetes, etc. in their early stages is very crucial, as it increases the chances of successful treatments. So machine learning algorithms can be used to predict these diseases at their early stage from the patient data, and start the treatment beforehand. The naive Bayes Algorithm is used to identify signs of diabetes.
Behavior Adjustments − “Prevention is better than cure”, as it’s said we tend to overlook many things in our daily life which could ultimately result in health issues. Somatix is an application that when used notices one’s daily routine and points out habits that have the potential to risk our health.
Robotic Surgery − Surgical robots assist surgeons while performing complex surgeries, by providing a better view of the area of surgery, more precision and flexibility than conventional methods.
In recent years, ML has been successfully integrated into pediatric care to predict the best and most individualized treatments for children.ML systems can help healthcare institutions to anticipate potential epidemic outbreaks in various parts of the world, by collecting data from satellites, real-time updates on social media, and other crucial information from the web. It can transform into a boon for third-world countries, where healthcare facilities are not readily available.
Machine Learning algorithms can also be used for 3D visualization of Biomedical Data such as RNA sequences, protein structure, and gnomic profiles.AI Care of Elderly and Low-Mobility Groups: the AI tech used in Tesla cars for auto driving and avoiding obstacles can also be used in the wheelchairs of aged and immobile or low-mobile people. Japan runs a plan to provide 75% of elderly care by AI.
Machine Learning Use Cases
Microsoft’s InnerEye Project uses computer vision and machine learning to differentiate between healthy cells and tumors using 3D radiological images, this assists medical experts in radiotherapy and healthy anatomy.
Tebra’s Kareo is used for data management, one can send patient health and financial data to Kareo’s billing platform to ease the process of managing records and completing transactions. Additionally, Kareo applied AI tech for the automation of repetitive tasks.
Ciox Health’s Datavant Switchboard platform is another example of data management. Here organizations can use personalized controls which allows their staff to request specific types of data.
Beta Bionics’ iLet, it’s a wearable ‘bionic’ pancreas that is usually used for Type 1 diabetes patients. This device keeps track of blood sugar levels; thus the patient is saved from the burden of tracking their blood glucose levels daily.
Subtle Medical’s SubtleMR is used for obtaining clean and clearer medical images for radiologists. Cleaner and clearer images mean the radiologists and examine and understand the diseases more precisely with lesser effort.
IBM’s Watson AI system is used by Pfizer for immuno-oncology research about how the immune system of our body can fight cancer.
There are cons of machine learning in healthcare too. Some of the cons are listed below −
For the machine learning algorithms to work properly and give accurate results, the data fed to them should also be accurate and precise. So the analysis and cleaning of data is done by humans. Moreover, there is always a possibility of defective diagnosis, so human surveillance is required all the time. Also for the algorithms to work for every patient a wide range of data is needed.
Machines can provide optimal treatment plans but they do not consider the social boundaries of a patient as a parameter for their results, which is not acceptable by the patients at all.
Another issue comes out to be autonomy issues, machine learning algorithms can be used to help psychological patients make decisions to improve their health, but sometimes the patients start taking the machine instructions too seriously and stop their thinking. It’s a challenge to attend a balance such that the patients don’t lose their human/natural thinking abilities.
Every patient wants their data to be kept private, nonetheless, it’s their right also, but when data is stored in some remote locations with no one monitoring them there’s always a chance of data leakage.
Machine Learning models should be trained on a wide variety of data as the same treatment can prove to be fruitful for one patient and lethal for some other patient. So, the model should be trained with a wide variety of data from different races and from different places. But, such chunks of big data with high precision are not available at present.
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