In 2017, Stanford University published a study describing the successful use of Artificial Intelligence (AI) algorithms to detect skin cancer against the diagnosis of 21 dermatologists. This is just one of many examples of Artificial Intelligence impacting the healthcare industry. Many people think AI will radically change healthcare. What may surprise you is it already is. Changes are here and will continue exponentially over the next 5-10 years and beyond.
Per a 2020 National Institutes of Health research study on artificial intelligence and scoping: “It is predicted that digital health technologies that incorporate artificial intelligence will transform health care delivery in the next decade.” Because of this, healthcare workers need to have a solid understanding of artificial intelligence, the current implications for healthcare, and what’s coming soon.
Understanding Artificial Intelligence
First, we must understand what Artificial Intelligence (AI) is. AI is a broad term that refers to the ability of a machine to perform a task that would ordinarily be performed by a human. Specifically, Google researcher, Francois Chollet, who does extensive research and development in the field of AI and Machine Learning, defined AI as “A system’s ability to adapt and improvise in a new environment, to generalize its knowledge and apply it to unfamiliar scenarios.”
Most of us grew up watching and reading futuristic books and movies showing robots perform tasks in the home and also assisting institutions like the police force. But AI is more than that. As Chollet explains, true artificial intelligence relates to a machine’s ability to learn and adapt based on the stimuli it is presented with and the complexity of its programming. And this is where AI will likely have its biggest impact on the healthcare industry.
What AI Means for Healthcare Workers Now
Let’s take a look at a few current examples of AI applications in healthcare:
- Google’s DeepMind technology successfully trained a neural network to detect more than 50 types of eye disease, by analyzing 3D retinal scans, in a collaborative study with joint research with Moorfields Eye Hospital, London, UK.
- Buoy Health is an AI-based symptom and cure checker that uses algorithms to diagnose and treat illness. Here’s how it works: a chatbot listens to a patient’s symptoms and health concerns, then guides that patient to the correct care based on its diagnosis.
- Harvard University’s teaching hospital, Beth Israel Deaconess Medical Center, is using artificial intelligence to diagnose potentially deadly blood diseases at a very early stage.
- *One of the biggest AI breakthroughs in drug development came in 2007 when researchers tasked a robot named Adam with researching the functions of yeast. Adam scoured billions of data points in public databases to hypothesize about the functions of 19 genes within yeast, predicting 9 new and accurate hypotheses. Adam’s robot friend, Eve, discovered that triclosan, a common ingredient in toothpaste, can combat malaria-based parasites.
Technology like these examples will become more popular in the next few years as healthcare systems look for ways to cut costs and medical errors using relative innovations. AI is also predicted to help make diagnoses, treatment, and invasive procedures more accurate and efficient, with fewer complications.
These are a few specific examples, but in a more general sense, AI is also impacting the following segments of the healthcare industry:
- Clinical Analytics
- Operational Analytics
- Behavioral Analytics
Each of these centers around how medical assessment, clinical pathways, disease progression, and disease management. Additionally, areas such as surgery are seeing rapid growth in robotics and AI. In 2017, we witnessed surgeons using AI assisted robotics to suture extremely narrow blood vessels –.03 to .08 millimeters across– at the Maastricht University Medical Center, Netherlands. And there’s much more to come. Let’s take a look at a few healthcare ai technologies in development (as of May 2021).
*PathAI is developing machine learning technology to assist pathologists in making more accurate diagnoses.
* Enlitic develops deep learning medical tools to streamline radiology diagnoses. The company’s deep learning platform analyzes unstructured medical data (radiology images, blood tests, EKGs, genomics, patient medical history) to give doctors better insight into a patient’s real-time needs.
* Zebra Medical Vision is working on providing radiologists with an AI-enabled assistant that receives imaging scans and automatically analyzes them for various clinical findings it has studied. The findings are passed onto radiologists, who take the assistant’s reports into consideration when making a diagnosis.
But with anything, there are obstacles to overcome for the implementation of AI across systems and platforms in the healthcare industry.
Challenges of AI
One of the many challenges of AI is seamless adoption. How do we integrate innovations with systems that already exist without causing disruption in flow and efficiency? Many healthcare systems are complex, with numerous pieces, parts, and interdisciplinary teams that must communicate and work in sync for optimal client/patient outcomes.
It’s not enough to develop a revolutionary AI technology. That technology needs to be implemented and integrated with current systems in a way that is not disruptive. This is one of the main challenges of AI. It is vital that all factors are considered before implementing any new technology. AI may seem innovative, but it is not always most efficient and it doesn’t always lead to the best patient outcomes 100% of the time.
We can only foresee technological innovations coming in the near future (the next 5 years or so) because as we have witnessed in the last 20 years, technology changes come fast and furious. Also some innovations, like 3-D printing, for example, seem to pop up out of nowhere, unexpectedly. There are few people or institutions who predicted 3-D printing. It is a glaring example of the unpredictability of technological inventions.
AI is dubbed ‘transformational technology for a reason. It will change how we live, work, and interact. There are few industries that will not be impacted. Healthcare will adapt and evolve with these new technologies. The key is for us to stay attuned to what’s developing and to be open to these changes, whilst continuing to maintain a positive patient outcome approach.