AI in healthcare is something that is revolutionizing the industry and medical treatment that we as the patients receive. But AI, in general, is making inroads into virtually every field and aspect of society. Healthcare AI companies like NVIDIA healthcare and Google DeepMind Health are breaking new ground, with innovations that are helping to save lives. Let's dive into the world of AI so that you can have a better understanding of what it is all about and where it is going.
AI stands for artificial intelligence. It uses complex algorithms to reflect human intelligence processes. With AI, machines can learn and analyze through their software systems without any human participation.
Just before moving to the pros that artificial intelligence brings for the medicine, let’s look first at some statistics of this industry. Regardless of this technology has been developing increasingly only for a few last years, the artificial intelligence in the healthcare market has scaled up incrementally and is forecasted to grow further under Statista:
Hence, let's look at what benefits AI delivers for healthcare:
In 2017, total healthcare spending in the USA grew by 4.6 percent. The estimate was about 3.5 trillion dollars under the research. It is thought that by 2026, the total will be closer to 5.7 trillion.
However, the cost of medication is only going higher these days, and not every patient can afford the treatment that they so desperately need. With that many people seeking out medical care, it should be evident that everything possible must be done to help mitigate those costs. As long as insurance remains privatized, any advancements in this field are going to be welcome.
Thus, the high cost of medical services is a huge factor when it comes to the role of AI in the future of healthcare. AI automating many tasks in facilities where the medication is manufactured will drive the cost down. Medical providers can save, thereby making their products more accessible.
For example, AI in healthcare can be helpful when it comes to many everyday tasks that were once under the exclusive purview of humans. The inputting of data for recordkeeping and analysis is a great example. There was an advisory report that came out which found that doctors spend around 49% of the time during their workday on health record input in EHR. AI applications can reduce this time significantly, freeing up the doctors for other, more critical tasks. Hence, AI can optimize the healthcare providers’ expenses and reduce in such a way the cost for medical treatment for patients.
If you think about how AI will change healthcare, doctor burnout also has to be considered. Being a doctor is a stressful job. Patients are often quite demanding, and doctors can become cynical and not as effective in their work if they must also spend a great deal of time dealing with menial tasks. AI automation can be an answer to that. Doctors who don't need to dedicate as much of their time to repetitive, mindless tasks are going to be better able to serve their patients.
Human errors that might negatively impact a patients' treatment and recovery can be prevented with AI. Artificial intelligence can also suggest new kinds of treatment that humans might have overlooked, through the study of existing medical facilities and the allocation of resources. Several diseases also require non-traditional forms of treatment, and there are many ways that AI can help with that.
Now, you know what are the AI benefits for healthcare including for providers and patients both. Hence, it’s time to check what are the most prominent AI use cases in medicine that deliver such pros for this industry.
In a nutshell, AI can be used in healthcare to analyze vast amounts of medical data, more than humans could ever process on their own.
Besides, such things like software for scheduling of medical appointments or disease diagnosis, even for treatment prescriptions can be much smarter and automated due to AI than was ever the case before. It is only through the use of certain algorithms that patterns in human behavior can be recognized.
More simply put, AI can automate many processes and perform them better than a human medical staff ever could. Errors can be avoided, and lives can be saved.
Just consider some of the other use cases of AI applications in healthcare and forecasted annual values for each technology under the Accenture report.
So, let’s consider the most innovative artificial intelligence technologies for healthcare in more details:
AI-assisted robotic surgery is something that has been planned for years, but now it is finally here. It is one of the most revolutionary applications of AI in healthcare. The basic idea is that robots can analyze data from pre-op medical records to guide a surgeon's instrument during a complex operation.
The robots can also use data from past operations to inform new surgical techniques. A study of 379 orthopedic patients at nine surgical sites discovered that an AI-assisted robotic procedure created by Mazor Robotics delivered a five-fold reduction of surgical complications compared to when surgeons operated alone. That's a staggering statistic, and if you want to reduce mortality rates associated with many different kinds of surgery, that is sure to get your attention. Here is one more example of already working AI-assisted robotic surgery applied at the hospitals:
The idea of virtual assistants for nursing & patient health monitoring is also one that has been long in coming. This technology can be especially powerful together with AR/VR For example, the company Sensely recently came out with "Molly," an AI-powered nurse avatar. It is being used by UCSF and the UK's NHS to interact with patients. It works a little like the voice assist features on phones and the hubs that people are installing in their homes. It asks them questions about their health, assess their symptoms, and directs them to the most effective care setting based on what it is told. Look how it works in action:
AI trends in healthcare strongly indicate that AI-based functionalities are among the must-have features for mHealth apps. Artificial intelligence benefits hospitals through the automation of clinic administration and workflow. Less time is spent on time-consuming tasks, which saves money and lowers the cost of various services for patients.
One example of is EHR with AI-based voice/video recognition capabilities is Dragon Medical Virtual Assistant by Nuance.
It makes clinical documentation and data management much easier by capturing clinical notes with natural language processing algorithms. This AI-powered system can be even more helpful when applying AR/VR technologies for medial needs like building smart virtual environment for doctors’ work. That allows medical staff to optimize their time and focus on their patients rather than documentation.
As we have said, AI‑enabled prescriptive analytics systems can analyze greater amounts of data far faster than humans. This often makes them more adept at coming up with correct medical diagnoses, more so than even the most skilled and experienced doctors.
For, example, image-recognition diagnosis ability allows these AI-enabled applications to identify even the rarest of diseases correctly. Take the Beth Israel Deaconess Medical Center. They developed an application with an AI-enabled cancer screening. It can accurately predict which patients are likely to be no-shows and which ones will allow their treatment to lapse. Doctors and medical monitors can then step in ahead of time.
AI predictive analytics is affecting incrementally the area of mobile health app development too. Moreover, using AI-driven algorithms in healthcare application is considered as one of the must-have features for mHealth app. It simply makes such apps much smarter. Today already there are some examples of AI-based diagnosis appі or symptoms checker apps that use predictive analytics like this one named Ada:
One more example of AI used in medicine is for the clinical trial participant identification. It is often necessary for a tremendous amount of information to be collected during clinical trials. All of it needs to be carefully chronicled so that theories can be proven or disproven. The various AI applications that are becoming commonplace facilitate the outcome-driven approach of clinical trials. This reduces the possibility of bias in whoever is running them.
The real-life example of using AI for clinical trials is IBM Watson for Clinical Trial Matching. One of the medical centers that had implemented it, found that there was an 84% increased enrollment to the clinical trial cancer in the 18 months after start using this solution (from 3.5 patients/month to 6.4 patients/month). Moreover, the use of IBM Watson for Clinical Trial Matching reduced incrementally by 78% the time to screen patients for trial eligibility from 1 hour and 50 minutes to 24 minutes. Hence, the implementation of such AI software can improve the clinical trials process much.
AI healthcare apps in the pharmaceutical industry can be used for faster drug discovery and development. Just consider the boost of AI development for the pharma industry, which results in the growth of these deals funding.
The influence of new drugs in clinical trials can be assessed more expediently. AI solutions are also being developed to identify new potential therapies from vast databases of information on existing medicines.
Algorithms for artificial intelligence in healthcare can also be used to produce an automatic scoring system that analyzes and measures the impact of any drug within just a few hours. In the past, it would have taken a minimum of several months. This allows for better and more immediate decision making.
Those who wondered in the past about how artificial intelligence benefits hospitals have been thrilled about the dosage error reductions that this new tech has provided. AI-based applications have been used recently to figure out the optimal medication dosage for patients that are being held for observation following surgery.
A recent case study in California found that a mathematical formula developed with the help of AI correctly determined the correct dose of immunosuppressant drugs for better treatment. One of the innovative examples of such type of application is Insulin Dosage calculator with predictive AI coaching that simplifies diabetes management:
Another one of the AI in healthcare examples that is noteworthy is the fraud detection capabilities of this new tech and the dramatic reduction in errors across a broad spectrum. It can help third-party payers such as health insurance organizations to extract the most useful information from the thousands of claims that they receive every day. AI, especially when applied with blockchain in healthcare can help these companies identify a smaller subset of the claims or claimants for further assessment if the algorithms determine that fraud is likely.
Brain-Computer interfaces are even more impressive. They will soon replace other types of computer interfaces in the medical field. This is particularly helpful for people with permanent or temporary disabilities.
For example, the right AI-enabled brain-computer interface can help stroke patients communicate with healthcare providers right after the incident has taken place. That's going to yield positive results much faster than painstaking rehabilitative therapy. This type of technology will likely fuel the next generation of healthcare bots that are even now being developed. Here is a brief and clear explanation on how the brain-computer interface works by O’Reilly’s and Intel AI:
The use of AI in medicine can be seen in many apps that are only now starting to be used in hospitals, clinics, private practices, and patients as well. Apps that use healthcare chatbots and AI algorithms are seen as revolutionary today, but as the public uses them more, they will come to be regarded as necessities rather than aberrations. Anyone who visits a medical facility will use them to do things like schedule appointments, report their symptoms, check-in, and then fill their prescriptions afterward.
However, before we will be able to benefit all the advantages of AI in healthcare, there are some problems with applying it. The most significant challenges to AI in the medical field where research and practical involvement are concerned will doubtless be in the areas of data collecting, privacy issues, and various legal impediments. Another objection for implementing artificial intelligence in healthcare is the technical side and lack of AI-skilled specialists. Here is a list of some of the most common difficulties for implementing AI into the health market under TechEmergence research:
But with a careful system of data storage, some efforts put in setting the technical equipment and legal regulations in place will allow accepting AI in different fields in medicine more effectively.
Despite privacy concerns and so forth, it is undeniable that AI is going to play a significant role in healthcare as we venture further into the 21st century. This technology in cooperation with other innovations as telemedicine application, AR/VR or blockchain is going to be a game-changer for all sorts of entities in the medical field. At the same time, apps featuring scaled-down versions of this tech will become available for public use.
Hybrid models will appear that support clinicians in the diagnosis phase of their practice. They will play a part in treatment planning and identifying risk factors. The ultimate responsibility for a patient's care will remain in human hands, as it should be. However, our automated helpers, backed up by cutting edge AI, will be genuinely helpful in the saving and prolonging of human lives. This is the immediate future of AI in healthcare.
We should look toward this new era with excitement, but that should be tempered with caution. As long as we proceed slowly and carefully in our implementation of this new tech, there is a reason for the hope that it's going to make all of our lives better.