Artificial intelligence (AI) in healthcare is no longer just the stuff of science fiction. It was first established as a field in 1956. The ideas was to explore the possibility of machines solving problems that humans typically solved using their natural intelligence. Now, AI is part of our everyday lives; you needn’t look any further than your Siri or Alexa device to find an example of AI in action.
Today, AI is also making an impact in the healthcare field. Recent studies reveal that AI in healthcare was valued at $2.5 billion in 2018 and is expected to grow at a compound annual growth rate of 41.5% through 2025. What’s driving the medical artificial intelligence market? Numerous factors are cited. These include: the need to lower healthcare costs, the growing importance of big data in healthcare, rising adoption of precision medicine, and declining hardware costs.
The Three Levels of Artificial Intelligence
The idea of artificial intelligence can make some people a little uneasy. But don’t worry—the machines aren’t taking over anytime soon! There are three levels of artificial intelligence development, and we’re still at level one.
- Artificial narrow intelligence (ANI), which has a narrow range of abilities. This form of AI doesn’t replicate human intelligence. Rather, it simulates human behavior based on a narrow range of parameters and contexts. Virtual assistants are obvious examples of this. Healthcare professionals are already recommending them for people suffering from memory loss caused by Alzheimer’s disease.
- Artificial general intelligence (AGI), which is on par with human capabilities. This form of AI would mimic human intelligence and/or behaviors and could learn and apply its intelligence to solve any problem.
- Artificial superintelligence (ASI), which is more capable than a human. This hypothetical AI is where machines become self-aware and surpass the capacity of human intelligence and ability. This is the type of AI found in those dystopian sci-fi books and movies.
IBM’s Watson is the perfect example of level one artificial Intelligence in healthcare. Watson Health was created to help solve some of the world’s most pressing health challenges through data, analytics, and AI. It combines human experts with augmented intelligence, translating data and knowledge into insights to help medical professionals make more informed decisions about care in hundreds of hospitals and health organizations. Pfizer is one company currently employing Watson to power their search for immuno-oncology drugs.
7 Clinical Applications of Artificial Intelligence in Healthcare (Today and Tomorrow)
AI that is currently on the market or in the FDA approval process generally involves pattern recognition, robotics, and natural language processing. Some AI in healthcare also engages in “machine learning”. This is a process in which software algorithms learn from and act upon new data to continuously improve performance. So, how does healthcare use AI? Here’s a look at some of the exciting ways AI is improving patient care and potentially saving lives.
1. Improving Disease-Related Therapies with AI
Known as a Brain-Computer Interface (BCI), AI combined with robotics can create direct interfaces between technology and the human mind without the need for keyboards, mice, and monitors. BCIs typically consist of a robotic arm and hand that uses digital algorithms to detect motions that patients can’t execute on their own. BCIs are poised to greatly improve the quality of life for patients with ALS, strokes, and other neurological diseases, as well as the hundreds of thousands of people worldwide who suffer spinal cord injuries each year.
2. Diagnosing Disease and Reducing Medical Errors with AI
It’s estimated that misdiagnoses result in the death of about 60,000 people each year, and causes serious harm to another 120,000. Often, a misdiagnosis occurs through human error—a result of incomplete medical histories and large physician caseloads. Unencumbered by these factors, AI can typically diagnose disease better and faster than most medical professionals, reducing the number of yearly misdiagnoses. AI can also extract and analyze the wealth of information that lies in a patient’s Electronic Health Record (EHR) in an accurate, timely, and reliable manner to predict disease.
3. Improving Diagnostic Tools with AI
According to the CDC, heart disease is the leading cause of death in the United States. Can AI reduce these statistics? The FDA thinks so, which is why it just approved a new AI-powered software called Caption Guidance. Caption Guidance is designed to help medical professionals capture, without any specialized training, echocardiographic images of a patient’s heart that are of acceptable diagnostic quality. Machine learning trains the software to recognize high-quality 2D ultrasound images of the heart and even record video of it for further analysis.
4. Developing New Medicines with AI
The cost of research and development for new drugs has skyrocketed while also requiring thousands of human work hours. Developing a new drug is estimated to cost $2.6 billion. And 90% fail somewhere between phase I trials and regulatory approval. Many experts believe that AI and machine learning will allow for quicker, cheaper, and more effective drug discovery. AI in healthcare can scour billions of data points in a short amount of time—faster than any human—predicting outcomes as it hunts for new disease-fighting drugs, therapies, and more.
5. Improving Electronic Health Records (EHR) with AI
Ask any physician what they hate most about their job and many will probably say “the EHR system.” The healthcare industry’s move towards digitalization has created ongoing problems associated with “cognitive overload, endless documentation, and user burnout.” Today, EHR developers are using AI to create more intuitive interfaces and automate some of the routine processes that consume so much of a user’s time. Voice recognition and dictation are improving the clinical documentation process, while medication refills and result notifications can also be handled by AI.
6. Advancing Immunotherapy for Cancer Patients
Immunotherapy for cancer patients uses their body’s own immune system to attack malignancies, helping them fight back against stubborn tumors. However, the treatment only works on a small number of patients, and oncologists don’t yet have a tried-and-true formula for predicting which patients will respond to it. It’s expected that AI and machine learning algorithms may eventually be able to highlight new options for targeting therapies to a patient’s unique genetic makeup.
7. Making Medical Devices and Machines Smarter
Everything is smarter, from our phones to our cars. But smart devices are also critical for monitoring patients, especially those in the ICU. A smart medical device or machine becomes even more intelligent with AI. This can provide an enhanced ability to identify health deterioration, recognize the warning signs of sepsis, or spot the development of health complications. By becoming smarter through AI, medical devices and machines can significantly improve patient outcomes.
The Challenge with Artificial Intelligence in Healthcare
It’s an exciting time for the medical field, and there are many benefits of AI in healthcare. The Harvard Business Review made a great case for AI in medicine in their recent story AI Can Outperform Doctors. So Why Don’t Patients Trust It?:
“Medical artificial intelligence (AI) can perform with expert-level accuracy and deliver cost-effective care at scale. IBM’s Watson diagnoses heart disease better than cardiologists do. Chatbots dispense medical advice for the United Kingdom’s National Health Service in lieu of nurses. Smartphone apps now detect skin cancer with expert accuracy. Algorithms identify eye diseases just as well as specialized physicians. Some forecast that medical AI will pervade 90% of hospitals and replace as much as 80% of what doctors currently do.”
There is one challenge, however: overcoming patients’ distrust of AI. As mentioned at the start of this post, many people become uneasy at the thought of artificial intelligence; they simply don’t trust it. The same Harvard Business Review story describes a study they conducted in which 200 NYU and BU students were given the opportunity to take a free assessment of their stress level and receive a recommended course of action to help manage it. 40% signed up when they were told that a doctor was to perform the diagnosis; only 26% signed up when an AI was administering the assessment.
Support for AI Medical Device Startups
The AI in healthcare boom has opened a lot of possibilities. Regulatory agencies have even started thinking about how to adapt their framework to new and evolving technologies. For example, the FDA introduced a new framework in 2019 that enables it to pre-approve the manufacturing of adaptive AI-powered software. So clearly there is a demand for it if its creators can overcome the “human challenge.”
If you have a new AI-powered medical device or product, or are in development, you may need some help bringing it to market. That’s where Diberin Solutions comes in. We champion artificial intelligence healthcare innovation and can help with marketing and consultancy so that you can reach your company’s full potential. Contact us to learn more.
