With the advancements in processing power, computers are now more powerful than they have ever been. Add machine learning, a branch of computer sciences which focus on giving computers the “ability” to progressively improve their performance.
Now, pair that with the mountain of data the medical field is sitting on and you get the perfect setting for a machine learning system to showcase its power.
Quite unsurprisingly such a system has been making waves in recent times due to its continuous integration in a wide array of science, technology, engineering and mathematics fields.
This article, however, will focus on the medical diagnosis and how it can be sped up and democratized thanks to machine learning.
For instance, people would no longer have to visit a doctor for a preliminary diagnosis. From the get-go, this solves the problems of having to make unnecessary appointments.
It also solves the issue of having to wait in lines and, as a result, waste a lot of time. And when it comes to hospitals and cabinets, it provides a solution to the problem of overcrowded emergency rooms, leaving doctors more time to deal with critical problems.
This list of benefits barely scratches the surface on what the power of machine learning can do to transform the medical diagnosis. If you are interested to find out more about it, then continue reading, as this article provides essential insight into this topic.
The process of obtaining a diagnosis for ailments is one of the primary uses for machine learning in medicine.
Pairing machine learning with data gathered by researchers and medical professionals can automatically speed up the process of accurately identifying various types of diseases. And this is not something which belongs in the future.
Medical institutes and big tech companies are already heavily involved in research and development of such data-driven diagnoses. And here are only a few examples:
- IBM announced their initiative known as IBM Watson Genomics, which is a partnership with Quest Diagnostics, an American clinical laboratory. The purpose is to better integrate cognitive computing in genomic tumor sequencing to effectively simplify the process of determining the DNA sequences of cancer tumors.
- DeepMind Health, a Google project which includes multiple partnerships between UK-based colleges and institutes which use the process of deep learning in order to identify potential health issues. By leveraging algorithms to gather collective data from images, eye scans and medical records, it’s possible to spot emerging threats to a patient’s health and thus to help the clinician make better healthcare decisions.
There are also strides in the field of neuroscience, where institutes are using machine learning to better understand mental health issues. Projects such as Oxford’s PReDicT are using predictive analytics to provide an accurate diagnosis of depression.
Their aim is to develop a possible treatment which could be made publicly available for use in clinics.
Another way in which machine learning is used to improve the medical diagnosis is by allowing for more curated treatments to be issued.
By pairing multiple variables of data collected from individuals, it is possible to offer treatments specifically targeted at one person or another.
In the vein of disease assessment, machine learning makes use of the data obtained from medical records and generates results personalized for each patient.
Patients can use sensory devices or mobile apps that have health monitoring features to gather data. That data would then provide valuable information that could increase the efficacy of future treatments.
Also, physicians can use this technology to estimate the risk factor that a patient might be exposed to based on the symptoms they are presenting.
Not only that but it also has a potential to optimize an individual’s health. By understanding what are the key risk factors that contribute to the emergence of an affection, it will be possible to accurately prescribe ways on how to effectively prevent it.
Speaking of prevention, having heaps of data at their disposal allows companies and institutes to identify if behavioral patterns are linked with the occurrence of certain ailments.
For example, with the help of machine learning, there are efforts being made in the field of cancer to recognize what factor is prevalent in the emergence of the disease.
In another case, many data samples of hand-to-mouth gestures are used to understand what drives people to smoke. The same behavior studies can be used in the process of helping people quit smoking.
The Improvement and Production of Drugs
When machine learning is used for a medical diagnosis, it helps to identify risks to a patient’s health. The data, however, can inversely be used to better understand the effects of drugs on patients.
With a large sample size of data, screenings can be made in order to predict potential success rates of drugs when used as a treatment for a certain type of disease.
Data analysis in this field has led to the development of precision medicine – medicine produced as a result of the data collected from the identification process of a disease.
The purpose of this process is to identify patterns in the behavior of a disease and its reaction to different possible treatments in order to devise potential cures for it.
One of the leading institutions conducting research in this field is MIT Clinical Machine Learning Group. Their efforts are focused on using machine learning to develop a precision medicine that can lead to the effective treatment of Type 2 diabetes.
More Good News about the Medical Diagnosis
There’s no doubt about it; the onset of powerful computers and the unprecedented volume of medical data are reinventing healthcare. And it will continue to do so in the foreseeable future.
Now, with the power of new technology, individuals have the opportunity to self-diagnose reliably and safely, without having to leave their homes.
Diagnosio is a software that uses machine learning to analyze your symptoms and provide you with an list of possible diagnosis.
This way, you can avoid the unnecessary risks that come with searching for your symptoms on the internet and getting inconsistent information. Start your free trial right now for a safe and reliable diagnosis!