The Growth of Artificial Intelligence (AI) in Healthcare
The use of artificial intelligence continues to grow across all industries, including healthcare. There are many applications of AI in healthcare, from diagnostics to research and treatment management. In a recent survey of 56 leading healthcare organizations, Tata Consultancy Services found that 86% of them have already adopted AI and plan to spend an average of $54 million on it by 2020.
This guide will provide an overview of the increased presence of AI in healthcare, its use, and what that means for healthcare workers in the future.
The Increased Presence of AI in Healthcare
AI and healthcare go hand in hand since the artificial intelligence platforms can serve as a database for healthcare professionals to access a wide variety of medical resources. They allow doctors to make accurate decisions quickly, which has led to an increased presence of AI in healthcare.
For example, a patient may have symptoms present in several health conditions, which may lead to difficulty diagnosing them. With artificial intelligence, practitioners can use quantitative and qualitative data to diagnose patients – leading to improved accuracy and earlier detection.
Since healthcare AI can learn from data, it can source information from the top medical research worldwide – in real-time. The data is continuously updated and ensures that doctors have access to the most up to date research, medical notes, and other essential information.
Types of AI Used in Healthcare
Artificial intelligence does not refer to one tool or piece of equipment but rather a collection of systems and algorithms that allow machines to process information similar to how the human brain does.
The following are some essential types of AI technology in the healthcare industry.
Machine learning refers to a technique that allows data models to learn by analyzing a preselected data set. It is one of the most common types of AI in healthcare – a study by Deloitte found that 63% of firms were already using machine learning to improve their business processes.
Machine learning in healthcare consists of processing large amounts of patient data and research so that the models can recognize underlying trends and patterns. Once the model is trained, the machine learning model can identify new patient symptoms and information.
Simply put, machine learning helps doctors diagnose and treat patients quickly and accurately.
Deep learning is a subset of machine learning which involves analyzing several layers of variables to predict outcomes. Deep learning is sometimes called a neural network because it works as the human brain does. It is used to determine the likelihood that someone will get a particular disease since the problem is viewed in terms of inputs and outputs. The algorithm also considers the weights of the features associated with these inputs and outputs.
Another application of deep learning in healthcare is within radiology, as it can be used to recognize cancerous lesions in images. These types of AI can detect features that are clinically relevant but may not be noticeable by the naked eye.
Natural Language Processing
Natural language processing is an approach within artificial intelligence in healthcare to recognize speech, analyze text, and translate outputs into plain English. Natural language processing, or NLP, can create and understand published research, transcribe clinical notes for patients, and even conduct conversations with patients virtually!
If you have ever worked with a healthcare provider that allows you to chat online with a system that narrows down your symptoms, you have interacted with healthcare artificial intelligence.
One of the most impressive results of combining AI and healthcare is developing robots that can perform highly skilled tasks. Surgical robots help surgeons see in areas that they usually would not be able to and reduce incisions and stitches’ invasiveness.
The human surgeon is still in charge and has to direct the robot, but the AI capabilities allow for greater precision and accuracy. Head and neck surgeries are often performed through robotic surgery, as extreme precision is required.
Robotic Process Automation
Performing surgery is not the only role that robots play when looking at healthcare AI. Robotic process automation, or RPA, allows administrative tasks to be performed automatically, based on a set of rules or predefined script.
These computer programs can complete repetitive tasks quickly and accurately so that staff members can focus on value-added processes instead. For instance, medical offices can use RPA to automate patient records, insurance authorizations, and even billing.
The more targeted the treatment plan and
diagnosis are, the faster
the patient can recover!
AI Machine Learning Allows for Targeted Diagnostics
As we mentioned earlier, targeted diagnostics is one of the primary benefits of AI in healthcare. There are so much data and research available that the artificial intelligence platform has to be able to sort through all of it and learn it – that way, it can make predictions when presented with new information.
Supervised machine learning involves using the physical traits of a patient in combination with the database of resources to provide a targeted diagnostic and treatment plan. It can help doctors narrow down diagnoses to one or two options and therefore allow them to make a more definitive decision.
Major Disease Areas and Artificial Intelligence
Major disease areas like cardiovascular and neurological disorders and cancer are some of the top causes of death around the world. As a result, diagnosing and treating these diseases as early as possible is essential in improving medical care.
Artificial intelligence allows these major disease areas to be detected early on, as the systems may pinpoint any additional risks that a patient may have.
There was one study conducted on patients who were at risk for stroke. The artificial intelligence tools measured their symptoms and genetic history to decide if they needed to be placed in an early detection stage. The early detection stage was based on abnormal physical movements that triggered alerts that urged patients to get CT scans done sooner than otherwise.
This early detection led to better predictions about which patients were at greater risk for future strokes!
Telehealth and Artificial Intelligence
Even though artificial intelligence applications in major disease areas are impressive, AI technology can also provide valuable solutions for everyday healthcare needs.
For instance, telehealth tools can be implemented in a patient’s home to help treat illnesses or prevent situations that could lead to hospitalization. This technology measures and documents a wide variety of patient metrics that can notify healthcare professionals if they fall out of acceptable ranges.
Telehealth benefits patients and healthcare workers – the patient gets better and more immediate care at a lower price, and fewer patients need to be readmitted to the hospital. Simply put, AI in healthcare helps make the process more efficient and effective for everyone involved!
Implications for the Healthcare Workforce
With the power of artificial intelligence in healthcare, there has been concern that this will lead to healthcare jobs being replaced by machines. However, research has shown that AI in healthcare will enhance decision-making and allow clinicians to focus on other tasks rather than replace jobs.
Even though a machine learning model can diagnose patients based on symptoms and research, it cannot replace treatments’ human and emotional aspects. Similarly, a medical professional must evaluate all medical images since the technology is not reliable enough to rely solely on its predictions.
While healthcare artificial intelligence may change the way doctors and nurses do their jobs, it will not replace them!
The Future of AI in Healthcare
AI in healthcare has a bright future. It has already played an essential role in healthcare, but this trend is only going to continue.We expect machine learning to be a standard component of diagnosing patients and providing treatment recommendations. AI is rapidly improving in the imaging analysis space. There will likely come a time when pathology and radiology images are examined by machines.
Natural language processing will continue to be used for speech recognition and record clinical notes on a broad scale. Systems will be highly standardized so that machine learning models can optimize processes.
The biggest challenge that AI needs to overcome is accessibility and adoption in daily practice. Widespread acceptance will require regulatory approval and integration with existing medical systems – a process that may take at least five to ten years.
Human healthcare workers will not be replaced by AI health but instead will serve as a tool to improve their efforts and allow them to focus on the social skills of empathy and persuasion. The goal is for healthcare workers to work alongside artificial intelligence!
The growth of artificial intelligence in healthcare will continue as the tools become more affordable and accessible. AI can empower doctors to diagnose patients more accurately and effectively while preventing errors with things like dosages.
There are many applications for artificial intelligence in telehealth and significant disease areas. Still, the goal is not to replace jobs – instead, AI will be a tool that can help healthcare workers do their jobs more efficiently and effectively.
There is serious potential here, but perfecting medical AI takes time.