5 Ways Artificial Intelligence Is Transforming Healthcare

Whether we are ready to accept it or not, Artificial Intelligence (AI) is already solving healthcare problems and revolutionizing the industry. Valuable time and money are lost daily because of misdiagnoses, delays, and poor communication. Resources are wasted on something that could have been detected earlier, and both the patient and the physician are strained by the results. There is a solution on the horizon: Artificial Intelligence.

The question thus becomes: how can artificial intelligence transform healthcare?

The solution to healthcare complications

Computers can analyze data much faster than humans can. They are able to see patterns and algorithms which can enhance disease diagnosis and overall public health, much faster. With the ability to learn algorithms, machines can be greatly utilized to process the abundance of data that forms part of the healthcare industry. The more data it receives, the better it is able to improve the industry in the future.

Here are five ways in which AI is already transforming the healthcare industry.

1. Identifying diseases and providing diagnosis

Heart disease is one of the leading causes of death around the world. Because of this, a lot of time has been invested in developing machine learning in such a way as to pick up on algorithms which can predict when people might be at risk. The results have been significantly more accurate than those made by other guiding systems. This is the case with Google and its healthcare technology subsidiary, Verily, where scientists created an algorithm that can predict this disease by looking at the back of a person’s eyes, and accurately pinpoint early signs of heart conditions. In a published article citing its findings, these scientists used data from over 300,000 patients, along with a deep learning algorithm, to conduct their research.

Other cases in which AI is leading by leaps and bounds are in computer imaging. This has led to advanced algorithms being used to effectively detect melanoma, one of the deadliest forms of skin cancer. This disease has a survival rate between 15% and 65%, from early stages to terminal stages. In some studies, this technology has been able to produce a 98% survival rate after five years.

2. Crowdsourcing treatment options and monitoring drug response

In order to reap the rewards of AI, we require data, and lots of it. Past attempts to understand patients and treatment options were extremely limited due to the many obstacles of obtaining data points in a way that was actionable, unobtrusive, and across multiple spectrums of the patient record. Nowadays, because of the advancement of wearable devices, mobile applications, and improved data interoperability thanks to new standards such as FHIR, data scientists and clinicians are beginning to leverage the power of machine learning in order to gain insights never seen before, and create customizable treatments that can provide better results.

Similarly, the way in which clinical information is gathered and aggregated with other datasets is crucial to successful diagnosis and treatment. In this capacity, AI is being used on social media to connect people in sharing different treatment options and to source information about different drug trials that might be available. This allows the flood gates of data to open up, and gives data scientists the ability to continue to track results once a patient is outside a clinical setting.

An interesting study conducted by AstraZeneca showed how the use of a recurrent neural network (RNN) — a data algorithm well suited to data that changes over time — helped scientists train generative models for molecular structures, in order to enrich libraries toward a biological target.

Although AI can accelerate the discovery of new treatments and drugs, the medical community may be slow to adopt them without hard evidence and the backing of relevant medical bodies.

3. Monitoring health epidemics

Sifting through mountains of government intelligence, millions of social media posts, and countless news feeds, computers are able to gather a myriad of sparse data that, when combined with ecological, biogeographical, and public health information, can help authorities pick up on current health threats as they begin to happen. One example is the 2014 Ebola outbreak, caused by filoviral hemorrhagic fever.

According to the National Center for Biotechnology Information, filoviral hemorrhagic fever is associated with multiple hemorrhagic manifestations, marked hepatic involvement, disseminated intravascular coagulation, and shock. Patients who eventually recover have a fever for about five to nine days, while in cases resulting in death, clinical signs develop early, with death occurring between days six and sixteen. Mortality is high and varies between 30% and 90%, depending on the virus.

This is what drove a team of scientists and researchers to develop artificial intelligence algorithms to identify species of bats that were more likely to carry the disease, and therefore predict a future outbreak. In a study conducted by Barbara Han, a disease ecologist at the Cary Institute of Ecosystem Studies, the team was able to successfully predict outbreaks with an 87% accuracy rate — a clear illustration of what AI is extremely effective at: classifying and predicting the unknown.

By applying the power of AI globally, we can create one of the most comprehensive disease databases encompassing all known parasites and pathogens, in both animals and humans. With this type of information at our disposal, a great many lives can be saved.

4. Virtual assistants help patients and physicians communicate clearly

Patient engagement in healthcare, just as in commercial marketing efforts to retain and educate consumers, fosters trust between patients and the medical professionals treating them, and is key to maintaining continuity of care. This leads to many long-term benefits for all involved parties, including insurance companies and physicians.

One of the biggest challenges in any such initiative has been patient adoption of new technology designed to improve communication. This has been the case with some approaches involving mobile technology or patient portals that, although meant to enhance the overall experience, have always placed the burden on the patient of following multiple steps and lengthy questionnaires. This is where AI can bring a new paradigm to how we interact with patients. Advancements in machine learning have facilitated the creation of software that learns how to best communicate with the patient, synthesizing complex medical terminology and concepts into digestible bits of information — a frictionless approach that enables physicians to quickly access the information or results they need.

Some examples of how AI is making it easier for patients to communicate with their doctors include using machine learning algorithms to better craft messages that elicit a more desired response from the patient, such as around medication adherence or post-discharge follow-ups. Another example is how ZocDoc uses AI to verify health insurance eligibility before a patient sees their doctor, ensuring they’re better informed about their coverage.

Another AI technology gaining popularity is the virtual assistant. Without having to look for lab results, a physician can use a voice command to request a virtual assistant to quickly pull up the information required — allowing the doctor to spend more time with the patient, leaving them more satisfied with the experience.

Other examples include computer vision technology, in which trained AI algorithms can monitor multiple patients and help staff prevent falls, and allow personnel to remotely communicate with patients to ease their time in the PACU or ICU.

5. Improving clinical documentation to develop better care management

Computers are very good at looking at thousands, if not millions, of rows of data in a fraction of a second, and discerning patterns, classifying information, and predicting outcomes with high precision. With access to high-performance AI algorithms, we can quickly process data points that at first glance appear disconnected — such as user-written patient notes — using machine learning to develop tools that pick up on irregularities by comparing hundreds of thousands of similar notes, determining outcomes, and surfacing what delivered positive results. Likewise, doctors can be notified of any missing data or if clarification is needed for any procedure, in order to provide better analysis.

Deep Neural Networks (DNNs) are algorithms that can recognize very complex patterns in data with multiple inputs, such as the information contained in written clinical documentation. Deep nets can find concepts in data that are virtually undetectable to humans, thanks to the numerous nodes that connect to synthesize information that can then be classified and applied to known medical protocols.

As some language experts would suggest, metaphors are not a decorative element in elaborated speech but instead an economical instrument to save words and effort by recycling old words into a new context. This is what DNNs are really good at: finding hidden relationships between “metaphors.” Mention “the king of rock and roll” or “the king of pop,” and the names Elvis Presley and Michael Jackson come to mind automatically — our brains are wired to relate these simple metaphors to a subject and an industry. Now imagine finding concepts among hundreds of thousands of clinical notes filled with countless medical metaphors — the size of that metaphorical web of medical conditions and their care becomes staggering.

What comes next?

As AI continues to gain support among medical experts, its value will without doubt continue to increase as new use cases are discovered and problems solved. And just as with any new disruptive technology, it is imperative that we prepare for its use appropriately. Though there are many advantages on one hand, there are also pitfalls we need to guard against. If we can successfully avoid these, we can expect huge medical discoveries and treatment innovations to continue to be made.