The ever increasing population of the world has put tremendous pressure on the healthcare sector to provide quality treatment and healthcare services. Now, more than ever, people are demanding smart healthcare services, applications, and wearables that will help them to lead better lives and prolong their lifespan.
By 2025, Artificial Intelligence in the healthcare sector is projected to increase from $2.1 billion (as of December 2018) to $36.1 billion at a CAGR of 50.2%.
The healthcare sector has always been one of the greatest proponents of innovative technology, and Artificial Intelligence and Machine Learning are no exceptions. Just as AI and ML permeated rapidly into the business and e-commerce sectors, they also found numerous use cases within the healthcare industry. In fact, Machine Learning (a subset of AI) has come to play a pivotal role in the realm of healthcare – from improving the delivery system of healthcare services, cutting down costs, and handling patient data to the development of new treatment procedures and drugs, remote monitoring and so much more.
This need for a ‘better’ healthcare service is increasingly creating the scope for artificial intelligence (AI) and machine learning (ML) applications to enter the healthcare and pharma world. With no dearth of data in the healthcare sector, the time is ripe to harness the potential of this data with AI and ML applications. Today, AI, ML, and deep learning are affecting every imaginable domain, and healthcare, too, doesn’t remain untouched.
Also, the fact that the healthcare sector’s data burden is increasing by the minute (owing to the ever-growing population and higher incidence of diseases) is making it all the more essential to incorporate Machine Learning into its canvas. With Machine Learning, there are endless possibilities. Through its cutting-edge applications, ML is helping transform the healthcare industry for the better.
Research firm Frost & Sullivan maintains that by 2021, AI will generate nearly $6.7 billion in revenue in the global healthcare industry. According to McKinsey, big data and machine learning in the healthcare sector have the potential to generate up to $100 billion annually! With the continual innovations in data science and ML, the healthcare sector now holds the potential to leverage revolutionary tools to provide better care.
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Here are 12 popular machine learning applications that are making it big in the healthcare industry:
1. Pattern Imaging Analytics
Today, healthcare organizations around the world are particularly interested in enhancing imaging analytics and pathology with the help of machine learning tools and algorithms. Machine learning applications can aid radiologists to identify the subtle changes in scans, thereby helping them detect and diagnose health issues at the early stages.
One such pathbreaking advancement is Google’s ML algorithm to identify cancerous tumours in mammograms. Also, very recently, at Indiana University-Purdue University Indianapolis, researchers have made a significant breakthrough by developing a machine learning algorithm to predict (with 90% accuracy) the relapse rate for myelogenous leukemia (AML). Other than these breakthroughs, researchers at Stanford have also developed a deep learning algorithm to identify and diagnose skin cancer.
There are various techniques for image recognition in machine learning algorithms. Those techniques are-
- Statistical Pattern Recognition
- Neural Pattern Recognition
- Syntactic Pattern Recognition
- Template Matching
- Fuzzy Model
- Hybrid Model
These techniques are unique in their own way and serve different purposes. For example, a statistical pattern recognises the historical data, and as it implies, this allows the machine to learn from the previously existing examples. After collecting, studying, and studying the data, it derives new laws that the machine learns to apply to the new data. Neural Pattern Recognition, as the name implies, uses the process of neural networks.
Artificial Neural Networks (ANN) are based on the neural network of a human brain. This is a very advanced technique to analyse patterns in varied types of data such as textual, visual, etc.
Syntactic Pattern Recognition, the alternate name for this type of technique, is structural pattern recognition. It is ideal for solving problems that are complex in nature. There is the involvement of recognizing sub-patterns.
Also, there is a huge application of machine learning in healthcare where Pattern Imaging Analytics plays an important role. This brings a lot more accuracy to the decision-making in the healthcare industry.
2. Personalized Treatment & Behavioral Modification
Between 2012-2017, the penetration rate of Electronic Health Records in healthcare rose from 40% to 67%. This naturally means more access to individual patient health data. By compiling this personal medical data of individual patients with ML applications and algorithms, health care providers (HCPs) can detect and assess health issues better. Based on supervised learning, medical professionals can predict the risks and threats to a patient’s health according to the symptoms and genetic information in his medical history.
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This is precisely what IBM Watson Oncology is doing. It is helping physicians to design better treatment plans based on an optimized selection of treatment choices by utilizing the patient’s medical history.
Behavioral modification is a crucial aspect of preventive medicine. ML technologies are helping take behavioral modification up a notch to help influence positive behavioral reinforcements in patients. For example, Somatix a B2B2C-based data analytics company has launched an ML-based app that passively monitors and recognizes an array of physical and emotional states. This helps physicians understand what kind of behavioral and lifestyle changes are required for a healthy body and mind.
Healthcare startups and organizations have also started to apply ML applications to foster behavioral modifications. Somatix, a data-analytics B2B2C software platform, is a fine example. Its ML application uses “recognition of hand-to-mouth gestures” to help individuals understand and assess their Behavioral, thus allowing them to open up to make life-affirming decisions.
Machine learning healthcare applies behavior modification to provide remedies for serious conditions such as Obsessive Compulsive Disorder (OCD), Traumas and Phobias, Separation Anxiety, etc. Apart from positive reinforcement, there are other methods used for behaviour behavior modification, such as negative reinforcement, aversion therapy, etc.
Also, there are two ways in which behavior modification works classical conditioning and operant conditioning. This allows for better coping for depression, anxiety, bipolar disorder, etc.
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3. Drug Discovery & Manufacturing
Machine learning applications have found their way into the field of drug discovery, especially in the preliminary stage, right from the initial screening of a drug’s compounds to its estimated success rate based on biological factors. This is primarily based on next-generation sequencing.
Machine Learning is being used by pharma companies in the drug discovery and manufacturing process. However, at present, this is limited to using unsupervised ML that can identify patterns in raw data. The focus here is to develop precision medicine powered by unsupervised learning, which allows physicians to identify mechanisms for “multifactorial” diseases. The MIT Clinical Machine Learning Group is one of the leading players in the game.
Its precision medicine research aims to develop such algorithms that can help to understand the disease processes better and accordingly chalk out effective treatment for health issues like Type 2 diabetes.
Apart from this, R&D technologies, including next-generation sequencing and precision medicine, are also being used to find alternative paths for the treatment of multifactorial diseases. Microsoft’s Project Hanover uses ML-based technologies for developing precision medicine. Even Google has joined the drug discovery bandwagon.
According to the UK Royal Society, machine learning can be of great help in optimizing the bio-manufacturing of pharmaceuticals. Pharmaceutical manufacturers can harness the data from the manufacturing processes to reduce the overall time required to develop drugs, thereby also reducing the cost of manufacturing.
Drug discovery has a lot many uses in machine learning applications in the healthcare industry. It allows medical professionals to bring precision, accuracy, and better delivery within the timelines. Mechanisms like clustering, classification, and regression analysis. Technologies like nanofluidics, automation, imaging software, etc, play a vital role in drug discovery.
Also, AI is not limited to providing gene sequencing in the process but also predicting how well the chances are for a drug to work and what are the expected side effects.
Deep learning in healthcare also has a major role to play. It speeds up the process of drug discovery and comes as a savior in finding the drug to stop the spread of infectious diseases. It was highly useful for identifying the drugs for Coronavirus. It mapped the potential drugs that could work against the infection to curb the spread.
4. Identifying Diseases and Diagnosis
Machine Learning, along with Deep Learning, has helped make a remarkable breakthrough in the diagnosis process. Thanks to these advanced technologies, today, doctors can diagnose even such diseases that were previously beyond diagnosis – be it a tumour/or cancer in the initial stages to genetic diseases. For instance, IBM Watson Genomics integrates cognitive computing with genome-based tumour sequencing to further the diagnosis process so that treatment can be started head-on. Then there’s Microsoft’s InnerEye initiative launched in 2010 that aims to develop breakthrough diagnostic tools for better image analysis.
It allows the practitioners to study the medical history and find correlations to build a robust diagnostic model. The data is of varied types, such as the data of diseases, genes, etc. This brings relief to both the medical professionals as well as the patients as this reduces the timeline to find the problem, serves the purpose of taking lesser, the diagnosis is accurate and most importantly reduces the number of visits that are required for a patient.
Machine learning for healthcare also works to reduce the chances of misdiagnosis and for the early prediction of diseases. And the potential research shows how machine learning is helpful in curing dangerous diseases like cancer.
5. Robotic Surgery
Thanks to robotic surgery, today, doctors can successfully operate even in the most complicated situations, and with precision. Case in point – the Da Vinci robot. This robot allows surgeons to control and manipulate robotic limbs to perform surgeries with precision and fewer tremors in tight spaces of the human body. Robotic surgery is also widely used in hair transplantation procedures as it involves fine detailing and delineation. Today robotics is spearheading the field of surgery. Robotics powered by AI and ML algorithms enhance the precision of surgical tools by incorporating real-time surgery metrics, data from successful surgical experiences, and data from pre-op medical records within the surgical procedure. According to Accenture, robotics has reduced the length of stay in surgery by almost 21%.
Mazor Robotics uses AI to enhance customization and keep invasiveness at a minimum in surgical procedures involving body parts with complex anatomies, such as the spine.
Also robotic surgery also allows the practitioners to perform the surgeries with sharp precision in complex areas. They are also famously known for non-invasive surgery and are usually done with smaller incisions. They are commonly done for kidney transplants, coronary artery bypass, hip replacements, etc.
Robotic surgery allows the practitioners to perform the surgery with lesser pain during and after the surgery. Also, there is a lesser scope of blood flow in robotic surgery procedures. It is expected to be the future of medicine.
6. Personalized Treatment
By leveraging patient medical history, ML technologies can help develop customized treatments and medicines that can target specific diseases in individual patients. This, when combined with predictive analytics, reaps further benefits. So, instead of choosing from a given set of diagnoses or estimating the risk to the patient based on his/her symptomatic history, doctors can rely on the predictive abilities of ML to diagnose their patients. IBM Watson Oncology is a prime example of delivering personalized treatment to cancer patients based on their medical history.
There are various benefits to personalized treatment, such as better specific diagnosis and reducing the trial and error-based approach. The inclusion of multi-modal data from the patient opens the chances of giving patient-centric medication. And most importantly it reduces the risk to health and reduces the cost that is borne by the patients.
The application of machine learning on genomic datasets facilitates giving better-personalized treatment. Understanding health on a much deeper level also grows as the large volume of data can be understood very well. The capability of analyzing the hidden patterns helps to predict the diseases that can be prevented, reducing the risk to human health.
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7. Clinical Trial Research
Machine learning applications present a vast scope for improving clinical trial research. By applying smart predictive analytics to candidates for clinical trials, medical professionals could assess a more comprehensive range of data, which would, of course, reduce the costs and time needed for conducting medical experiments. McKinsey maintains that there is an array of ML applications that can further enhance clinical trial efficiency, such as helping to find the optimum sample sizes for increased efficacy and reducing the chance of data errors by using EHRs.
Machine Learning is fast-growing to become a staple in the clinical trial and research process. Why?
Clinical trials and research involve a lot of time, effort, and money. Sometimes the process can stretch for years. ML-based predictive analytics help brings down the time and money investment in clinical trials-but would also deliver accurate results. Furthermore, ML technologies can be used to identify potential clinical trial candidates, access their medical history records, monitor the candidates throughout the trial process, select best testing samples, reduce data-based errors, and much more.
ML tools can also facilitate remote monitoring by accessing real-time medical data of patients. By feeding the health statistics of patients in the Cloud, ML applications can allow HCPs to predict any potential threats that might compromise the health of the patients.
8. Predicting Epidemic Outbreaks
Healthcare organizations are applying ML and AI algorithms to monitor and predict the possible epidemic outbreaks that can take over various parts of the world. By collecting data from satellites, real-time updates on social media, and other vital information from the web, these digital tools can predict epidemic outbreaks. This can be a boon particularly for third-world countries that lack proper healthcare infrastructure.
While these are just a few use cases of Machine Learning today, in the future, we can look forward to much more enhanced and pioneering ML applications in healthcare. Since ML is still evolving, we’re in for many more such surprises that will transform human lives, prevent diseases, and help improve healthcare services by leaps and bounds.
For instance, Support vector machines and artificial neural networks have helped predict the outbreak of malaria by considering factors such as temperature, average monthly rainfall, etc.
ProMED-mail- is a web-based program that allows health organizations to monitor diseases and predict disease outbreaks in real-time. Using automated classification and visualization, HealthMap actively relies on ProMED to track and alert countries about the possible epidemic outbreaks.
9. Crowdsourced Data Collection
Today, the healthcare sector is extremely invested in crowdsourcing medical data from multiple sources (mobile apps, healthcare platforms, etc.), but of course, with the consent of people. Based on this pool of live health data, doctors and healthcare providers can deliver speedy and necessary treatment to patients (no time wasted in fulfilling formal paperwork). Recently, IBM collaborated with Medtronic to collect and interpret diabetes and insulin data in real-time based on crowdsourced data. Then again, Apple’s Research Kit grants users access to interactive apps that use ML-based facial recognition to treat Asperger’s and Parkinson’s disease.
The crowdsourcing data collection helps in improvising the techniques that are used by machine learning and improves the quality of diagnosis given to the patients using AI. This reduces human intervention and brings better time delivery and reduces the risk of error by gathering the data in real-time which is the opposite of procuring the data through the traditional way.
10. Improved Radiotherapy
Machine Learning has proved to be immensely helpful in the field of Radiology. In medical image analysis, there is a multitude of discrete variables that can get triggered at any random moment. ML-based algorithms are beneficial here. Since ML algorithms learn from the many disparate data samples, they can better diagnose and identify the desired variables. For instance, ML is used in medical image analysis to classify objects like lesions into different categories – normal, abnormal, lesion or non-lesion, benign, malignant, and so on. Researchers in UCLH are using Google’s DeepMind Health to develop such algorithms that can detect the difference between healthy cells and cancerous cells, and consequently enhance the radiation treatment for cancerous cells.
11. Maintaining Healthcare Records
It is a known fact that regularly updating and maintaining healthcare records and patient medical history is an exhaustive and expensive process. ML technologies are helping solve this issue by reducing the time, effort, and money input in the record-keeping process. Document classification methods using VMs (vector machines) and ML-based OCR recognition techniques like Google’s Cloud Vision API help sort and classify healthcare data. Then there are also smart health records that help connect doctors, healthcare practitioners, and patients to improve research, care delivery, and public health.
Today, we stand on the cusp of a medical revolution, all thanks to machine learning and artificial intelligence. However, using technology alone will not improve healthcare. There also needs to be curious and dedicated minds who can give meaning to such brilliant technological innovations as machine learning and AI.
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Understanding the importance of people in the healthcare sector, Kevin Pho states:
“Technology is great. But people and processes improve care. The best predictions are merely suggestions until they’re put into action. In healthcare, that’s the hard part. Success requires talking to people and spending time learning context and workflows — no matter how badly vendors or investors would like to believe otherwise.”