Machine Learning is one of the most exciting technological innovations to further drive the field of artificial intelligence (AI) into the future and is currently being used in a wide variety of commercial and industrial roles. From finance and agriculture to education and manufacturing, Machine Learning is transforming the way in which many industries operate.
One of the industries currently being revolutionized by machine learning today is the healthcare industry and, in this article, we’ve gathered together our list of ten applications of machine learning within healthcare that are currently either being used or developed.
So, let’s jump straight in.
1-Enhanced Medical Imaging/Diagnostics
Identifying and diagnosing diseases and other medical issues is one of the many healthcare challenges machine learning is a being applied to.
IBM Watson Genomics, a joint venture between IBM Watson Health and Quest Diagnostics, is looking to integrate cognitive computing with genomic tumor sequencing in order to help advance precision medicine.
Recent results published in The Journal of the American Medical Association (JAMA) showed how machine learning algorithms also had a high-sensitivity for detecting diabetic retinopathy and macular edema in photographs of retinal fundus.
It is hoped that further research could go on to show how feasible this technology would be for widespread clinical adoption as well as how much it could improve healthcare outcomes for patients.
2-Understanding Medical Data
With healthcare-focused Internet of Things (IoT) devices such as the Fitbit on the rise, the number of ways in which to collect vast amounts of medical data from anonymous sources is increasing.
Machine learning is helping to make sense of all that data.
For example, there are apps and services available that help to gather data in order to aid research into certain conditions such as Parkinson’s disease or Asperger’s syndrome by gathering data from users over time using machine learning for facial recognition.
The apps then track a user’s condition and collect their data regularly in the hope that it will aid further studies.
3-Drug Discovery & Development
From next generation sequencing to applications in precision medicine, machine learning has various roles to play with drug discovery and development both now and in the future.
The initial screening of drugs in early stage and preliminary testing could utilize machine learning systems as could the methods used to predict a drugs success rate when taking into account a plethora of biological factors.
Unsupervised learning is also being used within precision medicine to better understand disease mechanisms and, therefore, understand better treatment routes for these diseases.
Both MIT and Microsoft have projects using machine learning algorithms to enhance and improve both our understanding and treatment of diseases and similar efforts focused on cancers and leukemia are also ongoing.
As machine learning applications within healthcare go, this is by far the most futuristic sounding, however, robotic surgery is nothing new and machine learning technologies look to add to what is already possible using robots for surgical procedures.
The benefits of replacing human surgeons with robots include allowing for operation in tighter spaces, with finer detail, and drastically reducing the chances for human-based challenges such as trembling hands.
Machine learning within robotic surgery mainly focusses around machine vision and is used to measure distances to a much higher degree of accuracy or identifying specific parts or organs within the body.
Machine learning systems within robotic surgeons are one way in which automation could find itself integrated into healthcare systems of the future.
With other industries quickly adopting autonomous systems and equipment, healthcare surely won’t be too far behind.
Using techniques such as machine and deep learning, robotic surgeons could eventually be fully automated and remove humans from surgical procedures altogether.
However, human surgeons might have a few things to say before that happens.
Automation may also find itself integrated into machine learning healthcare solutions in other ways too. Insulin pumps are but one of many medical technologies that may adopt automated systems so as to eliminate their intrusion on the patient’s life.
Understanding and being able to detect the differences in healthy and cancerous tissues and cells is key to then building the most appropriate treatment plans.
This is especially true for radiation treatments such as radiotherapy, where the risk of damaging healthy cells is particularly high.
Experts predict that radiologists and their departments will look vastly different if the use of machine learning algorithms proves fruitful and both DeepMind and UCLH are currently working machine learning applications that will look to both increase the accuracy of the therapy planning and improve the segmentation process of radiotherapy and similar procedures.
7-Creating Electronic Smart Records
With the huge volumes of medical and healthcare data now available, the implementation of smart electronic healthcare records has become essential. This, however, creates even more challenges for those already working within the industry.
Machine learning projects make a up a significant percentage of the efforts going into assisting in the creation of such smart records with handwriting recognition technologies and vision APIs from MATLAB and Google at the forefront of such developments.
Other machine learning applications in the creation of electronic smart records involve using records with built-in artificial intelligence or machine learning so as to assist with keeping medical records, interpreting health conditions and suggesting treatment plans.
8-Optimizing Clinical Trials
There have been several calls for machine learning technologies to be more closely involved in clinical research trials as they could provide several benefits including identifying ideal candidate groups based on factors such as genetics.
This in turn, it is argued, would make clinical research trials that were not only smaller in size and, therefore, quicker and more efficient, but also much less expensive in both financial terms and with regards to clinical resources.
Using machine learning during these trials could also be of major benefit to those organisations and institutes undergoing such trials with regards to the safety and accessibility of their subjects and data.
Personalized medicines and treatments have been discussed and debated for many years now, however, with technological advances in healthcare devices, artificial intelligence and machine learning, such treatments could one day soon be available.
As previously mentioned, machine learning technologies can be used to interpret the high volumes of patient data collected by IoT and healthcare devices and then use these interpretations to predict conditions or suggest treatments.
With both the widespread adoption of AI and machine learning and the increasing prevalence of data-gathering medical devices, such solutions are currently under investigation by industry players such as IBM and could soon be commonplace within healthcare institutions and facilities.
The ability of machine learning and artificial intelligence to interpret data and use it to predict future outcomes is also being used in other fields of healthcare that could prove to benefit all of humanity.
In one example, neural networks were used alongside support vector machines to help predict outbreaks of malaria in the Indian state of Maharashtra. The project took into account rainfall, temperature, reported cases and other details in order to generate its predictions.
With climate change mostly threatening those living in areas susceptible to malaria and other such diseases, technological breakthroughs making it possible to accurately predict where outbreaks will occur could prove to be life-saving in the not-too-distant future.
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