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 has 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.
Here are 12 popular machine learning applications that are making it big in the healthcare industry:
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 the 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 leukaemia (AML). Other than these breakthroughs, researchers at Stanford have also developed a deep learning algorithm to identify and diagnose skin cancer.
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.
This is precisely what IBM Watson Oncology is doing. Using patients’ medical information and medical history, it is helping physicians to design better treatment plans based on an optimized selection of treatment choices.
Behavioural modification is a crucial aspect of preventive medicine. ML technologies are helping take behavioural modification up a notch to help influence positive beahavioural reinforcements in patients. For example, Somatix a B2B2C-based data analytics company that 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 behavioural and lifestyle changes are required for a healthy body and mind.
Healthcare startups and organizations have also started to apply ML applications to foster behavioural modifications. Somatix, a data-analytics B2B2C software platform, is a fine example. It’s ML application uses “recognition of hand-to-mouth gestures” to help individuals understand and assess their behaviour, thus allowing them to open up to make life-affirming decisions.
Drug Discovery & Manufacturing
Machine learning applications have found their way into the field of drug discovery, especially in the preliminary stage, right from 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 which 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 for 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.
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.
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 in 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.
By leveraging on 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.
Clinical Trial Research
Machine learning applications present a vast scope for improving clinical trial research. By applying smart predictive analytics to candidates of 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 the clinical trial efficiency, such as helping to find the optimum sample sizes for increased efficacy and reduce chance 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.
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 the 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 the 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, a web-based program 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.
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 fulfiling 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 ResearchKit grants users access to interactive apps that use ML-based facial recognition to treat Asperger’s and Parkinson’s disease.
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.
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 helps sort and classify healthcare data. Then there’s 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.
Understanding the importance of people in the healthcare sector, Kevin Pho states:
“Technology is great. But people and process 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.”
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