Healthcare facilities and laboratories produce tons of electronic data every day. Computational techniques can be applied to this data to study the patterns which can help in research, and lead to innovations and lifesaving discoveries in the healthcare sector. Today, machine learning and artificial intelligence are quite popular in the healthcare industry as they are helping in developing novel medical treatments, managing the patient’s data, and treating chronic ailments. Artificial Intelligence gives machines an ability to simulate human intelligence to perform human-like tasks. On the other hand, machine learning is a subset of artificial intelligence that deals with studying computer algorithms that learn automatically and enhance their performance with experience.
There are many applications of machine learning and artificial intelligence in the healthcare sector. Some large enterprises are investing in these technologies to develop algorithms and medical devices which will transform the future picture of the healthcare industry entirely. In this article, we will discuss some prominent artificial intelligence and machine learning applications in healthcare industry.
Improving the Diagnosis of Different Diseases
Enhancing the ability to diagnose different ailments that are otherwise difficult to identify is one of the major advantages of leveraging machine learning in the healthcare industry. These diseases range from life-threatening diseases to minor ailments, which when left untreated can pose a serious threat for the patient. Today, a significant amount of medical imaging data is stored electronically and is at the disposal of researchers. Machine learning algorithms can process this data to identify patterns and incongruities. For example, an AI or ML-powered system can analyze the data like an experienced human radiologist. It can determine the location of tumors, identify spots on skins and lesions. Many patients wait in a queue to get an appointment from an experienced and trained radiologist. ML and AI-powered systems resolve this issue by automating the whole procedure.
Tech giants like Microsoft, IBM, and Google are employing ML techniques to build intelligent medical devices and systems for the improved diagnosis of the diseases. Microsoft has launched a project named Microsoft project inner eye that employs ML techniques to categorize and detect tumors. This is achieved through 3D radiological images. This advancement is significant for the radiotherapy department because it is expected to help in accurate surgery planning and management in the future. Another example of using ML for better research and diagnosis is IBM Watson Genomics. It helps in quick diagnosis by combining cognitive computing with genome-based tumor sequencing. Google is also striving to make tools and devices by leveraging AI to detect various diseases related to eye, cardiovascular, anemia, and breast cancer.
Reducing the Cost of Healthcare
Healthcare costs have skyrocketed in the last few years. Another benefit of integrating artificial intelligence and machine learning in healthcare is cost reduction. AI and ML deliver some promising results when it comes to optimizing the healthcare operations to curtail the costs. For instance, AI-powered tools can forecast the length of stay of the patient. Based on the patient’s stay, the hospital can allocate its resources, thus saving costs and reducing waste. To predict the duration of stay, ML models need to be trained through historical data, for instance the patient’s past diseases and his crucial measurements such as blood pressure, respiration, and pulse rate. Moreover, healthcare facilities incur huge costs in lieu of administrative expenditures. A significant percentage of administrative expenditures are due to high waste, low value-added work, fraud, and lack of coordination between different departments. ML and AI-powered systems can reduce this waste significantly by automating the manual operations. When the administrative section of a healthcare facility is fully automated, then the hospitals have more time to focus on the quality of their services. It takes a lot of cost and time to maintain and update health records. Technology has made data entry and maintenance much easier, however, these processes still require huge resources. However, during the last few years, machine learning in the healthcare industry is offering some promising benefits in this regard by slashing the time and cost of maintaining this data significantly. Machine learning-based OCR (Optical Character Recognition) technology supports the healthcare organizations to automatically capture, record, and store the data with minimal errors.
Some companies are also developing medical devices and applications, that can not only save the costs for patients, but also reduce the load on healthcare facilities. For example, Athelas has introduced a new blood testing device by employing machine learning and computer vision technologies, which are subsets of artificial Intelligence, to detect morphology. In addition to this, using just a small finger prick of blood, the device can illustrate the types of cells. According to the CEO of Athelas, health plans can save thousands of dollars through this device. The savings will be in terms of reduced hospitalizations and early detection of diseases because of frequent Athelas tests.
Augmenting Medical Research
To further the research in certain areas, physicians and healthcare companies must go through and interpret volumes of past information. Tens of thousands of research papers are published annually which makes it challenging for the researchers in the medical field to go through each paper and extract the relevant information. Instead of reading all these articles manually, medical researchers can employ NLP (Natural Language Processing) tools to extract essential information. NLP is the branch of Artificial Intelligence that enables the computers to read, interpret, and comprehend a large amount of data and present it in a useful form. Through this technology, researchers can go through a vast amount of scientific literature to find the studies, they need for their research. Moreover, ML-powered algorithms can process large amounts of data and detect the patterns in the complicated datasets accurately and at a much faster pace. Not only this, but AI tools can also combine various datasets for the ease of researchers. For example, the researchers have developed a canSAR database at the Institute of Cancer Research. This database can syndicate patients’ genetic and clinical information with independent biology, chemistry, and disease data. AI systems can use ML algorithms for pertinent cancer drug discovery forecasts after translating this vast amount of data into a common language.
Helping in Precision Medicine
The huge amount of patients’ data available in the form of electronic health records and genetic information is making the field of medicine more personalized, thus giving rise to a newly emerging field known as precision medicine. Precision medicine is a new buzzword in the field of medicine. This technique refers to providing personalized treatment to each patient while keeping in view the individual risk factors such as variation in genes, lifestyle, and environment. Because the machine learning can process large amounts of historical data at a much faster pace than humans, therefore some experts are of the view that precision medicine is not possible without integrating machine learning algorithms in the whole process. One of the major benefits of precision medicine is that it can forecast if the person can catch a certain disease in the future or not. Thus, it leverages the medical professionals to comprehend the conditions in which the disease may progress. With the integration of AI, the medical professionals can predict the disease of the patient more accurately, develop new drugs, and suggest individualized treatments to the patients.
Building New Tools Using Crowdsourced Data
Crowdsourcing permits the medical researchers to have access to the huge amount of medical data, which people upload willingly. This live health information will transform the way the healthcare industry will operate in the future. The progress in artificial intelligence and machine learning is creating new opportunities to use this real-time data for enhancing the treatment of certain diseases. Take an example of an Apple Research Kit that gives users access to ML-driven facial recognition applications for the treatment of Asperger’s and Parkinson’s diseases. IBM and Metatron have collaborated recently to create a tool that would comprehend, gather, and share insulin and diabetes data of the people in real-time based on crowdsourced data.
Discovering Drugs for the Existing and New Diseases
It sometimes takes more than a decade to develop a drug through clinical trials. Therefore, the pharmaceutical industry was always looking for a way that could lead to the faster discovery to save the lives of millions of people. AI tools are helping biotechnology and pharmaceutical companies in restructuring their research and development processes. They do so by processing a large amount of data, presenting it in an easier and digestible form, detecting good molecules from datasets, recommending chemical modifications, and predicting the future responses of patients towards a combination of drugs.
During the Ebola virus outbreak, an AI-powered program was used to examine the current medicines to determine how they can be recreated o fight the disease. This program was able to identify two medicines that may lead to a decline in virus infectivity. This kind of discovery requires months or years, however with AI tools it was achieved in a short period.
Enhancing Patients Experience through Digital Nursing Assistants
Digital nursing assistants are becoming increasingly popular in the healthcare sector because of their ability to provide timely support to the patients by responding to their queries, monitoring their activities and medicine intake, and scheduling their doctor appointment. These assistants are available 24/7 to help the patients. These digital nurses are AI-powered programs that are fed with a vast amount of data so that they can interact with the patients independently without requiring human intervention. These programs not only save the time of the patient and the physician, but they are also reducing a load of patients on the hospitals and laboratories.
Predicting Future Outbreaks
Artificial intelligence when combined with statistical modeling helps to predict the outbreak of future diseases. For example, a tech company AIME Inc has developed an AI-powered tool to predict the locations and timings of dengue outbreaks. Another recent example of the use of AI in predicting future outbreaks is the BlueDot algorithm. This algorithm was designed by the Canadian Artificial Intelligence firm BlueDot, and it predicted the outbreak of coronavirus days before the warnings from WHO (World Health Organization) and Centers for Disease Control and Prevention. The algorithm predicted the outbreak by utilizing information from various sources including the official and unofficial data on the number of reported cases.
Monitoring Health of the Patients
AI is leveraging the development of wearable health tracker devices that monitor the heart rate and activity level of the users. Based on their gathered data, these devices send notifications to the users to improve their health. For instance, a wearable device can monitor the activity level of the user and advise him to incorporate more physical activity in his routine. Besides this, they also share this information with the user’s physician to give him insights about patients’ behavior and requirements. Some prominent examples of wearable health trackers include Fitbit, Apple watch, and Garmin.
AI-powered Robots for Assisting in the Surgical Process
Hospitals in Europe and the United States are using robotic surgery to treat various diseases for many years. Unlike traditional methods, robotic surgery helps surgeons to perform complicated procedures with a high degree of accuracy and control. One of the common clinical robotic surgical systems which are used today encompasses a camera and mechanical arms with attached surgical instruments. These arms are controlled by the surgeon through a computer console. By integrating AI with traditional surgical robots, we can improve the performance of the surgeries performed by these robots. These robots can be fed with data gathered from past medical procedures. Manufacturers are already using AI to optimize performance, such as control accuracy and precision at the submillimeter level of these surgical robots. Consequently, these robots will reduce the probability of complexity during surgery significantly.
Combating Health Insurance Frauds
Each year, the healthcare industry incurs huge losses due to healthcare insurance frauds. Both health insurance providers and healthcare facilities can be a victim of health insurance frauds. For instance, a healthcare facility charging the health insurance provider for a treatment that was never received by the patient or overcharging for a treatment. These scenarios are difficult to identify by the health insurance providers because volume of data is generated every day in the healthcare sector. It is impossible to manage this data manually or detect anomalies in it. Fortunately, AI systems can go through large data sets, categorize, and interpret them in a short period. They are also not prone to human error as they do so with a high level of accuracy. AI-powered systems can learn what a fraudulent transaction seems like. Whenever it goes through the volumes of data and discovers an anomalous behavior, it alerts the system.
No doubt, machine learning, and artificial intelligence are changing the landscape of the healthcare sector. The applications of AI and ML in the healthcare sector are expected to grow exponentially in the coming years. The integration of these applications will have a life changing impact on us as we can hope for a future where we can access quality healthcare services at lower costs.
