Artificial intelligence has applications within almost every field or industry. Moreover, AI’s power to significantly increase task processing efficiency makes it a valuable asset in any digital problem-solving tool kit.
For AI in healthcare, the overall productivity-boosting factor is pretty much the same. It can automate tasks, substitute more basic interactions, and effectively use data to plot the course of a decision or activity that an institution can do.
That being said, the benefits of AI within the medical industry also deepen our understanding of its fundamental sciences. Discoveries and new information that would otherwise have not been discovered without AI already exists, and in the near future, will most likely grow greater in number.
So What Can AI in Healthcare Exactly Do?
Applications of AI in healthcare can be divided into the following sub-categories:
In general, AI can analyze and come up with preliminary decisions more quickly, using vast amounts of digital information. For instance, when given a sufficiently detailed EHR data set, an AI tool can easily detect patterns and trends. This would then allow medical professionals to immediately or more accurately come up with a verdict on what to do with a certain medical emergency.
Perhaps more interestingly with this regard, AI in healthcare can also make decisions in the form of robotic interactions, through its integration into an established natural language processing system. For example, UK-based Babylon Health offers AI-based consultation by interfacing with chatbots at its first few service levels. With both an AI and RPA system used for the chatbots, the queries can be sifted and categorized automatically. No live human operator is required to redirect them to their respective consultation departments (actual doctors) after the initial potential illness assessment has been made.
As for support-related RPA applications, its integration with AI also brings other possibilities in digital healthcare. Rather than create the automated task step by step for the first time, smarter configurations could combine multiple steps in a row in order to speed up the program’s development period.
A simple example of this is using an RPA to provide automated billing, during which the AI would analyze related patient data to see if there are eligible processes, such as if insurance can be used to cover the expenses shown on the (digital) billing documents.
But the most profitable use for AI in healthcare at the moment is with any application that requires management. Analysis and decision-making may be the first steps, but allocating everything to the correct location, and deciding how other actions should interact, is something that might become a bit too much for a human to handle in today’s digitally saturated world.
Staff management AI software, while definitely usable for hospitals and the like, is not exactly healthcare-specific. CloudMedX Health may not be the best example, but it is the most balanced option when it comes to demonstrating the management prowess of a robust AI system. The company specializes in predictive healthcare models. As such, its AI tools benefit both the doctor and the patient when it comes to management. Its services offer a variety of data handling and assessment features that help streamline everyday clinical tasks.
An even more advanced application of management features of AI in healthcare is when attempting to predict demand and capacity at a hospital or similar institution. By analyzing scores of patient records, billing documents, activity logs, staff work hours, and even specific medical equipment use, an AI would be able to more or less accurately anticipate when the hospital would usually be overloaded with patients. In response, the facility could prepare well ahead to meet the demand and allocate human and supply resources accordingly.
The limitations of AI today, in any field or industry, primarily lie in the amount of information available, the hardware used, or the nature of its software’s coding. Machine learning systems are technically a step up for each of these. They can learn several lifetimes’ worth of data, they benefit from modern server-based computers, and. Especially for deep learning-based systems are designed to adapt independently.
This means that more advanced forms of analysis can be made, and the most promising aspect of healthcare is preliminary diagnosis. A sufficiently advanced AI would be able to collate every bit of data and measurement from a patient to develop an (ideally) accurate conclusion about their physiological state. Put simply, AI could eventually be made to know, on its own, what exactly is wrong with a patient’s body.
The concept of computers learning how to diagnose illnesses accurately has been researched and tested for several decades. However, at the moment, we have yet to see a perfectly reliable medical diagnostic AI system that can do this without any significant drawbacks.
Watson, the cancer diagnostic AI built by IBM, made popular through its participation in the game show Jeopardy!, wasn’t able to perform as originally intended. It still has glaring assessment issues regarding static medical literature and was eventually deemed too risky to use by some medical professionals.
Nonetheless, the march of progress continues. AI in healthcare is due to become far more advanced within the next few decades; even something that would even supplant Google’s AlphaMind might finally do the job it is designed to accomplish perfectly.
General Medical Care and Assistive Robotics
Lastly, AI in healthcare can be used directly in physical robotic applications. More specifically for healthcare, this usually encompasses any type of assistive machine, a therapeutic unit, or a medical procedure specialist that can react accordingly. So, while most robots are built with only pre-defined tasks, these AI-powered units can adapt, providing their own set of actions accordingly.
For example, Stevie is a “social robot” primarily designed by Akara Robotics to keep elderly citizens socially connected. It interacts with them, gives them entertainment, and is also programmed to play with available games typical for people of a particular age. Its AI reacts to what senior citizens do and could technically adapt by regularly logging its interactions and activity.
Extending beyond simple assistive applications, a few AI in healthcare robots are even made to help doctors conduct medical procedures that require high precision or visual recognition. SigTuple’s Shonit demonstrates this with the use of a smart robotic microscope.
When its system is given a blood sample, a set of tiny microscopes interface with AI to automatically move it around, scanning the sample for various readings and measurements. The data can either be saved on a cloud database for further automated analysis or viewed directly via a smartphone app.
So, much in the same manner as software-based robots of advanced automation, real physical robots can also replace human medical professionals in areas that are simple enough for a robot’s focus specialization attributes to be of significant operational benefit.
Would AI Be Beneficial for My Solo/Small Practice?
The answer is definitely yes. Indeed, it is easy to conceptualize AI as this big, multi-institutional machine that helps streamline and organize entire facilities and professional groups’ technical operations. However, in a nutshell, AI is just as its name says, a form of human-developed intelligence. Thus, it can still offer several advantages at the smallest practice scales, though finding equally smaller scale AI services would need to be locally available:
- It offers an extensive use network, rather than confining updates with manual internet browsing and interaction with other medical professionals.
- Immediately provides a rudimentary second opinion via analytical assessments.
- A simplified AI system could create another layer of communication with patients.
- You can have a “smart” referential database for past medical records that could formulate new medical suggestions that you can consider.
What Do Services from AI Companies Usually Offer?
Because AI applications in healthcare are vast, AI companies’ services will still depend on the particular field of expertise in which their team specializes. Here’s a sample list of a few notable AI-powered healthcare companies today:
BioSymetrics – specializes in predictive analytics using its signature Augusta platform. By promotive definition, this platform is optimized for pre-processing and analyzing huge sets of small, accumulated data, such as daily activity tracking via IoT medical devices.
The company aims to use Augusta as a flexible AI tool that can be introduced to any medicine-related company or organization, be it a small scientific research team, a larger hospital, or even an entire pharmaceutical factory.
OnCora Medical – much like BioSymetrics, the company uses a data analytics AI platform. But this time, it is designed exclusively for oncological care (cancer treatment). Data from the cancer patient is extracted from a digital database.
The AI software then attempts to outline the best operational procedures, measure the quality of potential care, and optimize treatment solutions to give the best possible chance of technical success (for such a potentially terminal illness). Their services primarily cater to patient care and healthcare institutions.
Babylon Health – as mentioned earlier, this company focuses on remote consultation services in the form of frontline AI-powered chatbots. What wasn’t explained earlier is that it uses a deep learning AI.
This allows the chatbots to adapt and provide users with specific information regarding the AI’s “experience” with similar health problems, with a personalized, seemingly human flair. Essentially, the more consultations it processes, the better it gets at analyzing people’s health queries.
Subtle Medical – this company offers a suite of different AI-powered technologies that mainly help enhance the quality and information of images in medical examinations, focusing on radiology. Its AI uses deep learning systems to scan a particular medical image (e.g., an X-ray sheet or an MRI slice).
Afterwards, it reveals several areas of interest by accurately clearing up what would usually be a fuzzier image on a physical slide or viewing screen. Elements that might not have been as noticeable as the original could be more clearly observed.
Atomwise – focuses on using neural network algorithms to significantly speed up and reduce the cost of developing potential new drugs. It accomplishes this using its AtomNet system, a proprietary technology capable of processing and handling millions of affinity measurements and protein structures.
Using its vast analytical prowess, Atomwise can provide (relatively) accurate predictions of molecular interactions with proteins, enhancing the efficiency of drug discovery procedures several times fold.
Lastly, we have to give a short shoutout to Google’s DeepMind team and IBM Watson Health for being one of the world’s leading organizations that developed prototypes for a future predictive diagnosis system.
They may not have been on par with the expected performance quality, but we did at least catch a glimpse of what these technologies could become several years further down the research line.
Will AI Ever Replace Doctors?
The age-old question of whether AI would eventually supplant human expertise and labor has been intensely debated ever since the concept was first introduced. This includes the medical industry and healthcare in general. But, as the world stands today, it is safe to assume that AI in healthcare will probably never replace humans completely.
In fact, according to an article published in Forbes just last year, the trend would most likely go towards the birth of new jobs, just as other industries have long predicted. Diagnosis, treatment, and prognosis may all be done efficiently by an advanced future AI. But the caretaker industry might evolve to focus more on the psychological therapy aspect of doctor-patient interactions.
Another opinion is that AI won’t simply replace humans but would instead work alongside us. This is already true today, though many more improvements must be made. For one thing, directly interfacing with AI hasn’t been exactly possible without the AI developing a platform first that we humans could understand.