Machine learning (ML) and Artificial Intelligence (AI) are invading the medical sphere. They used to be abstract concepts not fully applicable in healthcare industry, but now they have become practical tools that can help organizations improve implementation of their services as well as care quality, increase revenue, and reduce risks.
Almost all advanced healthcare organizations have already started utilizing the technology in their daily practice.
“Machine Learning and Artificial Intelligence will be used to empower and improve every business, every government organization, every philanthropy – basically there’s no institution in the world that cannot be improved with Machine Learning and Artificial Intelligence.”
Jeff Bezos, founder, chairman, and chief executive officer of Amazon
However, let’s first figure out what actually machine learning is. It is an application of AI to allowing systems automatically comprehend and analyze situations or issues with no people’s assistance. Thereby, Machine Learning is designed to decrease human interference.
Machine learning and Artificial Intelligence have been making a profit in the healthcare field so far. The research firm Frost & Sullivan has investigated that AI systems will generate $6.7 billion in global healthcare industry revenue by 2021.
About 35% of healthcare companies are going to introduce AI solutions during the following 2 years. Additionally, more than a half of these companies are planning to follow the lead during the following 5 years.
The increase in supply has been taking place since there are various specific needs to doing any business in healthcare industry, including the necessity of developing secure enough EHRs to comply with privacy laws. For instance, between 2012 and 2017, the spread of EHRs increased from 40 to 67.
Machine learning provides many opportunities for healthcare
Some innovators are cooperating in an attempt to improve the existing reality by experimenting with AI and machine learning in healthcare.
Computers and the algorithms used by these innovators can scrub enormous amounts of information much faster and more precisely than scientists or healthcare experts. This way, patterns and predictions are unearthed to improve disease diagnostics, public health and safety as well as to inform treatment plans.
There are several startups and companies working on creating tools to facilitate the development of healthcare in addition to the giants like IBM and Microsoft.
Healthcare industry has become the major one to invest into and work on AI and machine learning, as their potential impact in saving many patients’ lives and financial resources is incredible.
Actually, the savings would be breathtaking. One of McKinsey reports estimates that big data could save medicine and pharma up to $100B a year as a result of upgraded efficiencies in clinical trials and research, better understanding for making decisions and new solutions that will allow insurers, regulators, doctors and patients to take better decisions.
Machine learning algorithms improve most data they are exposed to. Indeed, there is plenty of data in the healthcare systems.
By means of a well-established process that lets people to share information easily, it would be real to analyze the huge amount of data. This analysis can’t be carried out yet because of the differences between storage systems, problems with ownership and privacy. If the analysis could be carried out, this would bring excellent results for consumers, physicians and healthcare companies.
Present Machine Learning Healthcare Applications
Let’s view a list of existing applications of machine learning in the healthcare industry.
Diagnostics in Medical Imaging
The process of diagnostics is extremely complicated. It still embraces huge amounts of factors which are impossible for machines to put together in order to obtain the big picture. Nevertheless, there is no doubt that the machine can be of help to doctors in proper consideration to diagnosis and treatment.
That was the reason for Memorial Sloan Kettering (MSK)’s Oncology department to cooperate with IBM Watson. MSK has cancer patients’ information which has been utilized for decades, and it can bring ideas for treatment or variants to physicians in dealing with specific future cancer cases starting with accumulated experience. This type of tool, which is hard to sell in this dog-eat-dog world of healthcare facilities, is already in preliminary use.
Scaled Up / Crowdsourced Health Data Collection
Most of the attention is given to gathering data from different mobile devices to combine and sort out actual medical data.
For instance, Apple’s ResearchKit is planning to do that in the treatment of Parkinson’s disease and Asperger’s syndrome through empowering users to utilize interactive applications, one of them employs ML for facial recognition, that assess their state of health over time. Ongoing data is forwarded to an anonymous pool for further study.
Despite the enormous flow of healthcare information brought by IoT, it looks like healthcare is still experimenting in the ways of understanding this information and make real-time changes to treatment. Experts and consumers also can be optimistic as this trend continues, and researches are going to have more “weapon” to fight serious diseases and unique cases.
Although the healthcare field is generally complicated by various laws and criss-cross incentives of different stakeholders (hospital CEOs, physicians, patients, insurance organizations, and so on), drug discovery differs in comparatively clear economic value for ML healthcare application makers.
In addition, drug discovery relates to one comparatively clear client that usually has a large budget such as pharmaceutical companies.
The giants as IBM and Google have also jumped on the bandwagon of drug discovery process. For example, IBM’s own health applications have had initiatives in drug discovery for almost the whole period of the company’s existence.
Google has also joined the team of organizations which are already raising money and having income operating in the field of drug discovery by means of ML.
The da Vinci robotic surgical system has attracted the most attention for a reason. The system enables surgeons to manage deft robotic arms to carry out the least traumatic, detailed operations, yet impossible for humans to perform.
Moreover, there are some systems employing computer vision with the help of ML to detect distances as well as particular parts of the body.
Furthermore, ML is sometimes utilized to steady the motion and movement of robotic arms manipulated by professionals.
Future ML Health Applications
We are providing you with the existing applications which are more and more in demand as they are being funded and researched.
The aim of precision medicine is a new era bringing the conditions under which each patient’s health recommendations and treatment of a disease are based on their case history, genetic traits, etc.
For example, utilizing precision medicine for cancer treatment covers identifying characteristics which could facilitate the prediction effectiveness of the particular treatment for a particular patient, according to OncLive.
“Imagine if you could take results of all of the tests … and the results of the treatment that was done, and aggregate and anonymize all of that data, and apply machine learning to learn from that which treatments were the most effective. Not only could you reduce the amount of the chemotherapy that was required for a patient, but you could also reduce the number of patients who received an unnecessary dose – or who received a type of chemotherapy that didn’t work.”
Mike Flannagan, senior vice president of SAP Analytics.
Automatic Treatment or Recommendation
Envision a machine which could set up a patient’s dose of medicine by tracking information on their blood, nutrition, sleep, and the stress level.
In order to make sure you will not forget the number of pills must be taken, a tiny ML table will be able to give you the medicine, track the number of pills which must be taken, and even call in the doctor in case you are feeling unwell.
Improving Performance (Beyond Amelioration)
Orreco and IBM recently declared a partnership to increase athletic performance, and IBM has entered into a similar partnership with Under Armor in January 2016.
Although western medicine is mainly concentrated on treatment and amelioration of disease, the absolute necessity for high-quality prophylaxis does exist. Recent IoT devices as Fitbit are moving these applications forward.
ML might be introduced to indicate employee productivity or stress levels at work in addition to looking for enhancements in at-risk groups (not only relief of symptoms).
Autonomous Robot-Assisted Surgery
Nowadays, robotic surgical systems similar to the famous “da Vinci” improve surgeon’s skills significantly.
One day, ML might be utilized to bring together visual data and motor patterns by means of devices like the da Vinci to enable machines to perform surgeries.
This kind of machine will be able to carry out lots of hip replacement operations in order to ultimately perform the procedure on anybody, and this will be done better than a team of the best surgeons can do today.
New popular technologies often turn to be overvalued, however it is obviously not relevant to ML and AI.
Healthcare is already experiencing an increase in productivity and profit through these two technologies.
A huge number of the main healthcare players are already putting money into AI, understanding its future leading role in the industry.
Machine learning and Artificial Intelligence in the healthcare industry will definitely keep developing and improving disease prevention and diagnostics as well as help create customized medicine based on a patient’s unique DNA and suggest treatment variants in addition to other capabilities.