Twelve practical applications of machine learning (ML) in healthcare
Whenever the discussion about human morality, their health, and the science of medicine pops up, any technologies that can impart a more effective, supportive, and quicker analysis to put out a precise treatment plan in time are massively valuable. Artificial intelligence (AI) and its subset, machine learning (ML) is conquering the planet at the present moment. On an everyday basis, and an increasing number of business use cases prop in technological news pieces all over the globe. Domains such as finance and banking appear to be the ideal choices for the technologies, but what happens to other industries? Is the medical industry any different? Obviously not. In reference of artificial intelligence in the healthcare domain, we must know the massive potential and the alterations machine learning can confer to the medical field.
In this blog post, you’ll gain an understanding of how machine learning can revolutionize both patient care and the managerial processes of the industry. We possess adequate research to prove that machine learning often supersedes human beings when it comes to diagnosing an illness. Algorithms are presently serving as an improvement and are spotting malignant tumors more, more so than actual conventional radiologists. Machine learning in the healthcare domain furnishes algorithms with self-learning neural networks that are capable to enhance the quality of care through the analysis of external information on patient’s state, their X-rays, CT scans, several tests, and screenings. Another thing that is worth specifying, deep learning is now majorly being leveraged for identifying cancer cells. The framework is input with a massive number of cancer cells pictures to record in their memories what they look like.
However, we are ways off from supplanting human beings with machinery in the domain of healthcare. This blog post is about the possible uses of artificial intelligence in healthcare and the hurdles for broad adoption.
The possibilities for healthcare driven by machine learning
Machine learning is one of the typical subsects of the broader umbrella term of artificial intelligence, it is aimed at the training of models with large datasets. Going by a survey taken by Deloitte, of 1,100 USA enterprises that were leveraging artificial intelligence, 63% were concentrating on machine learning. It is a wide strategy that could have a practical use in several industries, domains, and use-cases.
How is this associated with medical care? The leveraging of ML could enhance the organizational aspect of the industry. A typical nurse in the U.S.A. is allocating a quarter of her work time on regulatory and managerial tasks. Technology could simply assume these tedious tasks like claims process, revenue cycle administration, and clinical documentations and records administration.
Harvard Business Review carried out another survey, in excess of three hundred medical executives and clinical leadership made the claim that there is an issue with patient engagement. In excess of 7/10ths of respondents reported that lesser than 50% of their patients are very engaged in the treatment procedure, while 4/10ths of the respondents made the claim that less than a ¼ of the patients were showing increased levels of engagement.
Improved patient involvement surely brings improved health-related results for patients. Machine learning can provide automated message warnings and relevant targeted material that incites actions at critical junctures. Typically, there are several ways in which ML can customize and enhance the treatment procedure.
What are the exact applications with regards to artificial intelligence and machine learning in the domains of medicine and healthcare?
One of the best use cases with regards to machine learning for medical applications is a robot framework that renders the treatment period much easier to bear. A digital nurse for patients operates as a voice-managed healthcare assistant that furnishes data on several diseases, health disorders, and medicines. Personalized AI assistants are a very useful thing if the patient requires real-time suggestions and it might be tough for him or her to physically get themselves to a practitioner. Data engineers are undertaking research on solutions for all medical functions that handle the cumulative health monitoring in addition to assisting cure or event averting illnesses.
Save for the very famous leveraging of chatbots, you should focus closely on the implementing of machine learning in healthcare algorithms in:
- Rare illnesses
Therefore, ML in healthcare algorithms are primarily artificial neural networks. For instance, CNN (convolutional neural networks) execute image identification, detection, and recognition. These are a complex framework of artificial neuron layers tagged to one another and receive prior training on a dataset of damaged cell pictures to “record” the appearances of malicious cell.
These medical specialties are a tough nut to solve for data scientists, as long the are demanding the most complex sphere of machine learning to integrate deep learning. It facilitates the development of neural networks and the identification of cells causing damage to the organism, such as cancer cells.
Machine learning deployed in medical technologies with regards to oncology – the treatment of cancer searches for the specific cells impacted by cancer at a precision level that can be equated to one made by a veteran physician. The operations of a pathologist whenever they’re evaluating organic fluids of patients, like fecal matter, blood, urine, sweat) can be facilitated by machine learning’s capacity to undertake analysis in a better and more improved fashion. Human eyesight supplemented with microscope tools is not even 50% as quick in analysing when contrasted to an automated model. Also, we have to admit to the fact that we still stumble with lacking data on the source of uncommon diseases and the linkage of these illnesses to specific traits and characteristics of individuals impacted with the illness. In the same vein, no pun intended, some ML in medical startups facilitate the field with innovative ways of analysis of patient’s photos and tracing features.
There are several other ways to leverage machine learning in healthcare:
- Presently, tech-driven medical care is an actuality as intelligent medical devices experience mass adoption. The medical domain always ushers in innovation, that’s why the outlook for AI in healthcare shines very bright indeed. Google has released an algorithm that correct identifies cancer within mammograms, while researchers from Stanford University can detect skin cancer with the help of deep learning. AI is in command of processing numerous differing data points, forecasting risks, and results with accuracy, in addition to several other functions.
Diagnosis and illness identification
It is common sense to begin at this point, as machine learning is very precise at diagnosis, as a matter of fact, this is one sphere where it demonstrates the most efficiency and effectiveness. They are several variants of cancer and genetic illnesses that are difficult to find out, machine learning, however, can manage several of them in the preliminary stages. IBM Watson Genomics is a brilliant example of that. This project is bringing together cognitive computing with genome-based tumor sequencing and gives assistance in influencing a swift diagnosis. PReDicT (Predicting Response to Depression Treatement) from P1vital is attempting to develop a practical method to bring artificial intelligence to enhance diagnosis and treatment in conventional hopsitals.
Health documentations enhancement
Regardless of all of these technological advancements, maintaining health documentation remains a chore. Yes, it is a lot faster today, but it is still very time-consuming. Documentation could be categorized by vector machines and ML-drive optical character recognition strategies. The forerunning instances of these are Cloud Vision API from Google and ML handwriting identification technology from Mathworks.
The forecasting of diabetes
Diabetes is one of the most typical, and as a matter of fact, pretty hazardous illness. It not only impacts an individual’s health by itself, but it also leads to other severe ailments. Diabetes primarily exerts damage on the heart, the kidneys, and nerves. ML could assist in diagnosing diabetes at a very early phase, rescuing lives in the process. Classification algorithms such as KNN, Decision Tree, and Naïve Bayes could be a foundation to develop a system that forecasts diabetes. Naïve Bayes is the most effective amongst them with regards to sheer performance and computational times.
Forecasting liver disease
The liver has a leading functionality with regards to metabolism. It is susceptible to illnesses such as chronic hepatitis, cirrhosis, and liver cancer. It is a very difficult activity to effectively forecast liver illness leveraging massive amounts of healthcare data, however, there has already been some considerable evolution in the sphere. ML algorithms such as classification and clustering are playing the role of game changer here. The Liver Disorders Dataset or the Indian Liver Patient Dataset (ILPD) could be leveraged for this activity,
Identifying the ideal cure
Another brilliant application is leveraging machine learning at the first stages of medicine discovery for patients. Presently, Microsoft is leveraging AI-driven technology in its Project Hanover, which intends to identify customised drug combos to treat Acute Myeloid Leukemia.
Making diagnosis through image analysis
Microsoft is inciting a revolution in healthcare data analysis owing to its InnerEye Project. This startup leverages computer vision to undertake processing of medical imagery to influence a diagnosis. As technology experiences evolution, InnerEye is influencing more shockwaves in healthcare analytics software. In the not-too-distant-future, ML will have even increased levels of efficiency, and even a greater number of data points, could undergo analysis to make an automated diagnosis.
Machine learning in healthcare is leading to great advancements. IBM Watson Oncology is a sureshot leader in this sphere by imparting several treatment plans that initially undertake analysis of a patient’s medical records. As progressive biosensors experience mass adoption, inputting more data for algorithms, everything is bound to improve with regards to developing customized treatment plans.
This is a very fascinating area to have observations on. Providing tips on your everyday tasks to avert cancer? That’s precisely what an app from Somatix, a B2B2C-based organization is going. This app undertakes tracking of the unconscious activities we perform on a daily basis and cautions us with regards to the ones that might be hazardous over the longer-term.
Healthcare Research and enhancement of clinical trials
It’s not really a confidential, top secret that clinical trials could take several years to finish, with considerable investing needed. Machine learning can provide predictive analytics to identify the best candidates for clinical trials, on the basis of aspects such as one’s medical history of clinician visits or social media activity. The technology will also reduce the number of information-based errors and could indicate the best sample sizes to be evaluated.
Utilizing crowdsourced healthcare information
Currently, scientists have accessibility to a massive amount of data on the public domain by the patients themselves. This is the origin of enhancements of machine learning in healthcare in the future. Why is data analytics critical in healthcare? Well, a collaboration between IBM and Medtronic has already had the outcome of the capability of deciphering, accumulating, and making insulin data available in real-time. As the internet-of-things (IoT) experiences evolution, the potential is limitless. Also, publicized information will enhance the diagnosis procedure and the issuance of scripts for medicine.
With regards to information analytics, last year, specialists have access to a rich amount of data from satellites, social media analysis, new websites, and video streaming websites. Neural networks could undertake processing of all of that and come to conclusions on epidemic outbreaks all over the planet. Hazardous illnesses could be prevented before they begin and actually cause extensive impact. This is critical in Third world countries, as they are lacking in sophisticated medical practices and systems. Mostly likely the best instance of this sphere will be ProMED-mail, an internet-based reporting platform which surveys outbreak reports internationally. Artificial intelligence is also extensively implemented in food safety, assisting prevention of epidemic illnesses on farms.
Artificial intelligence surgery
This is most likely the most influential sphere with regards to machine learning, and it will become much more famous in the upcoming future. You can compartmentalize robotic surgeries in the following classifications:
- Automated suturing
- Surgical workflow modelling
- Enhancement of robotic surgical materials
- Surgical skill assessment
Suturing essentially implies stitching up an open wound. Automation of this procedure makes the time taken shorter, while removing pressure from the operator.
Although we are still in the preliminary phases of this technology to begin speaking about surgeries that exclusively executed by robots, they can now help and assist a practitioner manipulate surgical devices. Over the next half a decade, it is forecasted to become a specialty industry with a capital of approximately 39 billion USD. When a healthcare process is carried out, the robot will get obtain instruments for the practitioner with its robotic arms. This type of process reduces surgical complications by ½ and minimizes the time patients remains in the operating theatre by approximately 20%. ML algorithms for medical data analytics also evaluates and provides new avenues for upcoming surgeries, as it gathers information on each Artificial Intelligence Survey.
The largest hurdles for AI in healthcare
- Data Governance: Healthcare information is still private and restricted for access. Although, going by a Wellcome Foundation Survey conducted in the United Kingdom, a mere 17% of public responders were opposed to sharing their healthcare data with 3rd parties.
- Transparent algorithms: The need for transparent algorithms is not just needed to meet stringent drug development regulatory standards, but also, generally, individuals are required to comprehend how precisely algorithms produce conclusions.
- Optimization of electronic records: There is still a lot of fragmented data between several databases that require additional structuring. When this scenario shows signs of improvement, it will cause progression in personal treatment solutions.
- Embracing the capability of data silos: The medical domain should alter its perspective on the worth of information and the way it confers value upon the enterprise, in the big-picture perspective. Pharma enterprises, for instance, are usually reticent to alter their product strategies and research when assured financial gains are on the cards.
- Data Science experts: Enticing more ML experts and data science experts is of critical importance for both the pharma and medical industries.
The outlook of artificial intelligence in healthcare
There is a cumulative apprehension that artificial intelligences will wind up hacking jobs, but in actuality, that’s couldn’t be more distant from the reality of the situation. It was forecasted that in 2020, AI would create new jobs, more than it would eradicate, as an outcome of automated mechanisms. Further, in four years from now, there are predictions that AI will give way to 2 million more additional employment opportunities. This is as ML and AI only go about automating tedious activities, some of which exceed human capacities. Therefore, this provides more avenues for human specialists to take over more complex and dynamic activities.
Going by Allied Market Research, the international AI healthcare market will attain $22.8 billion in 2 years from now. We are speaking about $150 billion yearly savings for the medical domain, owing to machine learning and artificial intelligence solutions.
Why is medical diagnosis prone to error?
Doctors are human beings after all. They make errors, they forget, they lose focus, the list goes on. They are only mere mortals with a rigorous understanding of the human condition. Some unanimous aspects make up the probability of an incorrect decision. Included in them are: inefficiently leveraged information from medical information technologies, communication problems between clinical staff, patients, and their family and friends, and, obviously, the operation of the medical care system itself. The final one is surely the most difficult to alter, but the answer for the first two reasons is already in front of us.
ML in Healthcare informatics
ML in Medical Informatics has robust analytical capabilities. Therefore, the digital data furnished to practitioners is becoming much superior. Doctors now find it easy to access parameters such as the risk for a stroke, coronary artery illness, and failure of kidneys. They obtain patient’s indicators based on several blood pressure readings, family history, gender, race and the update medical experimentation information. Following this, critical clinical insights are made to assist practitioners prescribe a treatment plan and furnish the best care as an outcome. Potential outcomes assist them in estimating how much the process will cost – therefore, making treatment more accessible.
In summary, the criticality of the benefits of ML in medicine, the highest score goes to its potent capabilities categorizing and classifying health information in addition to quickening up practitioner’s clinical decisions and any types of forecasts that can rescue lives or make surgery less complex. For instance, averting hypoxemia during surgery. That’s already a lot as is. Human life is definitely the most valuable thing on this planet. At the present moment, ML in medicine furnishes technologies that overtly contribute to the future of sophisticated medical diagnostics in addition to the future of medicine. There are various other solutions like artificial intelligence in nutrition, which we feature an upcoming article on!