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Artificial intelligence (AI) is driving massive improvement and innovation in the healthcare industry. It is expediting advances in drug research and discovery and allowing for better and faster diagnoses.
The outbreak of the Covid-19 pandemic has further pushed the healthcare industry to actively adopt this modern tech.
AI in healthcare can be extremely beneficial to both providers as well as patients when used in the following areas – improving care, chronic disease management, early risk identification, and workflow automation and optimization.
In this article, we will look into artificial intelligence at length to you an idea of how AI is used in healthcare and how the technology will shape the industry in the coming future.
From making an accurate diagnosis to maximizing hospital efficiency, AI has proven to be a boon for the healthcare industry. Here are a few ways AI is revolutionizing the healthcare industry and driving it towards digital transformation to better engage with users and generate more revenue.
It is one of the most common applications of AI in healthcare. AI and collaborative robots have revolutionized surgeries in terms of speed and accuracy. These systems can perform complex surgical procedures with reduced risks of side effects, blood loss, or pain. Likewise, post-surgery recovery is faster and easier.
For instance, Maastricht University Medical Center has been utilizing AI-powered robot to suture small blood vessels, some no thicker than 0.03 millimeters.
Using AI in medicine and healthcare, professionals and surgeons get access to real-time information and insights into a patient’s current health condition. This AI-backed information enables healthcare providers to make prompt, intelligent decisions before, during, and after procedures to ensure the best outcomes.
The US Justice Department claims that 3% of healthcare claims in the country are fraudulent. This translates into a hundred billion dollars lost annually. Using AI, the healthcare industry can detect invalid claims before they are paid for and help to speed up processing, approval, and payment of valid ones. Apart from detecting insurance frauds, AI also prevents patient data from being stolen.
Leading healthcare service providers such as Harvard Pilgrim Health are embracing AI to root out healthcare fraud. They are using AI-based fraud detection systems to identify claims and detect suspicious behavior.
Artificial intelligence in healthcare is changing the way clinical providers make their decisions. AI delivers data to providers to aid in diagnosing, treatment planning and population health management. The technology is also used to support decisions in data-intensive specialties like ophthalmology, radiology, and pathology. It may even be possible to perform certain tasks autonomously using AI in the near future.
AI, with natural language processing, can also help translate clinical notes in EHRs. This means a clinician only needs to enter data once.
Healthcare is now moving towards the world of Cognitive Assistants who come with reasoning, analytical capabilities, and a complete range of medical knowledge. The recently launched algorithm, Medical Sieve, has been stated qualified to provide assistance in decisions related to cardiology and radiology.
The cognitive health assistant, analyzes the radiology images to then spot and detect issues faster and with more reliability.
Medical Sieve is one of the many examples of artificial intelligence in healthcare. There are other technologies as well, like Enlitic, which aim to mix deep learning with medical data for aiding advanced diagnostics and for improving patient outcomes.
Babylon App is a working example of how AI can change doctor consultations. The app offers medical consultation and healthcare services online. The app provides medical AI advice on the basis of a patient’s medical history and the available medical knowledge.
These AI-based app works in a way that the users only have to report the symptoms of their illness and the app then checks the symptoms against the database of the diseases using the speech recognition method. Then, after noting the patient’s history and their circumstances, they offer a course of action that the patient should take.
The growing popularity and need for healthcare apps that store data and generate reports on the basis of AI technology are visible from the fact that over 54% mHealth app users are willing to engage with AI and Robotics for their medical consultation needs.
Apps like these, when developed rightly with the help of a healthcare software development company, not only assist patients in managing their health but also help lower the waiting room crowd and wait time.
Sense.ly, a medical startup developed the world’s first digital nurse, Molly. The virtual nurse has an amicable face and comes with a pleasant voice and her only goal is to monitor the condition and treatment of patients. The mobile app uses machine learning for supporting patients that have chronic conditions when in-between doctors’ visits.
The app provides tested, customized monitoring and follow-up care, with a focus on chronic diseases.
By being present to inform patients when to take medications and then monitoring if they did, has made AI in medicine a very important technology when it comes to Health Assistance and Management of Medication.
The creation of pharmaceuticals using clinical trials can take more than a decade and even costs billions. Introducing AI to drug creation, doesn’t just make the process faster but also extremely cost-effective.
Atomwise is one such network that makes use of supercomputers, which roots out therapy from the databases of molecular structure. In 2015, Atomwise used its AI technology to find out the existing medicines in the market, which could be redesigned for treating the Ebola virus and they found two drugs that they found could help solve the epidemic. The analysis that would have taken years, happened in one day through Atomwise AI technology.
AI in medicine has a big impact on genomics and genetics. AI helps identify patterns in massive data sets containing medical records and genetic information, which help look for links to diseases and mutation.
In the coming future, AI will even be able to tell the doctors what happens in the cell when a DNA is changed through genetic variation, whether therapeutically or naturally.
With more and more healthcare invoices becoming digital, every data related to the doctor, the treatment, and the medical establishment can be easily retrieved. Upon the mining of data, hospitals can generate reports on the mistakes they are continuously making in treating a certain type of condition, to help improve and even avoid unnecessary hospitalizations of patients, wherever needed.
A company in the Netherlands, Zorgprisma Publiek, has been analyzing the invoices shared by the hospitals and using the Watson technology to mine the collected data.
Computer vision capabilities of AI benefit the healthcare industry a lot. Hospitals and clinics use AI to recognize abnormalities in different kinds of medical images as CT or radiology scans. Image recognition assists doctors in diagnosing tumors, kidney and liver infections, improving cancer prognosis, and more.
The best example of AI-powered visual perception is the tool used at the UVA University Hospital. Utilizing ML algorithms, the tool analyzes children’s biopsy images to distinguish between environmental enteropathy and celiac disease, doing it as reliably as doctors do.
Now that we have seen how AI is transforming healthcare in the form of immense benefits and applications, let’s dive into the different types of AI technologies relevant to the healthcare industry.
Artificial intelligence in healthcare is a collection of many technologies. Most of these technologies have immediate relevance to the healthcare field, but the tasks and processes they support may differ. Some of the important AI technologies have been described below:
It is one of the common forms of artificial intelligence in hospitals and healthcare. Machine learning focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. In healthcare, the most common application of ML learning is precision medicine. It predicts what treatment procedures are likely to be successful with patients based on various patient’s attributes and treatment. The great majority of precision medicine applications and machine learning require a training dataset for which the end result is known. This is termed as supervised learning.
The most complex form of machine learning involves deep learning or neural network models with many levels of variables or features to predict outcomes. A common application of deep learning is the recognition of potentially cancerous lesions in radiology images.
[Also Read: Estimating the Time, Cost, and Deliverables of an ML App Project]
NLP includes applications such as text analysis, speech recognition, and other goals related to language. A common use of NLP in healthcare involves creating and classifying clinical documentation and published research.
NLP systems can analyze clinical notes on patients that are unstructured, giving incredible insight into improving methods, understanding quality, and better results for patients.
RPA uses automation technologies that can learn, mimic, and then execute rules-based business processes. Compared to other forms of AI, they are inexpensive, easy to program, and transparent in their actions. In healthcare, they are used for automating repetitive tasks like updating patient records or billing.
A rule-based expert system is the simplest form of artificial intelligence and uses prescribed knowledge-based rules to solve a problem. The aim of the expert system is to take knowledge from a human expert and convert this into a number of hardcoded rules to apply to the input data.
In healthcare, they are widely employed for ‘clinical decision support’ purposes. These rule-based systems work well up to a point and are easy to understand. But when the number of rules increase, they begin to conflict with each other and break down. However, now they are being replaced in healthcare by more approaches based on data and machine learning algorithms.
The wide implementation of innovative technologies like AI comes with several challenges. From the lack of quality data to security issues, a number of challenges exist for the healthcare industry using AI technologies.
So, without further ado, let’s take a look at them:
Data availability: One of the biggest challenges with AI systems is that training them requires huge amounts of data from several sources which include electronic health records, pharmacy records, etc. Since the data is fragmented and patients often see different health providers, the data gets complicated and less comprehensible. This results in errors and higher costs.
Privacy concerns: One of the key challenges of AI for healthcare is the amount of data collected that contains sensitive information requires additional security measures to be implemented. So, it’s important to look for the right AI software development partner who can offer a wide range of security options to ensure your customer data is appropriately handled.
Errors and injuries: There are chances that the AI system might at times be wrong in detecting potential risks or treatment. For instance, if an AI-based system suggests a wrong drug to a patient or makes an error in locating a tumor in a radiology scan, it could result in the patient’s injury or dire health-related consequences.
[Also Read: Key Healthcare Trends That Will Redefine Industry in 2022]
In healthcare, AI is already changing the patient experience, how clinicians practice medicine, and how the pharmaceutical industry operates. The journey has just begun.
In the future, AI will enable the next generation of radio tools that are precise and detailed enough to replace the need for tissue samples in some cases. This may help service providers to better define the aggressiveness of cancers and target treatments more appropriately. AI is also enabling “virtual biopsies” and advancing the innovative field of radiomics.
Furthermore, electronic health data can help highlight patients at risk and identify infection patterns before they start showing symptoms.
Leveraging machine learning and AI tools to drive these analytics can create faster, more accurate alerts for healthcare providers. AI can also provide earlier warnings for conditions like seizures or sepsis, which often require intensive analysis of highly complex datasets.
Leveraging AI for risk scoring, clinical decision support, and early alerting are some of the significant areas of development for this revolutionary approach. AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.
As we can see artificial intelligence and healthcare go hand-in-hand because of the multiple benefits that this technology offers. Despite the challenges, AI for healthcare can produce more accurate diagnoses and treatment plans and lead to better patient outcomes overall. Thus, all healthcare institutions must invest in AI solutions to offer novel experiences and excellent services to customers.
At Appinventiv, we work with healthcare companies on different custom AI and ML-based models that help in improving revenue, reducing costs, and offering enhanced customer experience.
For instance, we helped YouCOMM build a multi-request format platform for in-hospital patients to connect with nurses in real-time for medical help. The system enables patients to call/notify the staff through voice commands and the use of head gestures. Since the launch of the app, 5+ hospital chains in the US are running on YouCOMM solution.
In case you are also looking for AI software development services, get in touch with our experts. We can help you create and implement AI in healthcare solutions and cater to your needs in the most tech-friendly manner.