With the rise of technological advancements and innovations across various industries, it comes as no surprise that the world that people live in is changing rapidly and continuously.
The same is true for the healthcare system.
Healthcare providers face enormous pressures as the population is aging and various public health crises emerge.
Now more than ever, there is a need to become more efficient, deliver higher-quality healthcare, and apply the latest technology in every treatment and drug therapy.
Since medical environments hold substantial amounts of data, it’s only right that they integrate artificial intelligence (AI) machines to make their workload more manageable, provide better patient experiences, and minimize potential patient safety risks.
The Different Types of Artificial Intelligence in Healthcare
When it comes to taking care of your health — especially in dire, life-and-death situations, AI can offer assistance in a way that can improve outcomes and provide the most ideal results.
Because of this, hospitals, clinics, laboratories, and other medical facilities have integrated various types of AI into their daily operations.
Below are four different categories of artificial intelligence in healthcare.
1. Machine Learning
The first type of artificial intelligence is known as machine learning. This is one of the most well-known examples of AI, especially in the medical field.
Machine learning uses deep learning that has modified the way healthcare systems provide medical diagnoses and treatments. Every AI machine uses algorithms to process extensive amounts of clinical data, identify notable patterns, and ultimately make well-informed predictions about potential outcomes with more accuracy than its manual counterpart.
From analyzing patient records and exploring various therapies, the science behind machine learning is enabling healthcare professionals to reduce costs while improving their services and treatments.
By leveraging machine learning for important tasks like drug development and disease diagnoses, medical practitioners can more precisely diagnose their patients and offer customized treatments to meet each patient’s diverse needs.
Further, using artificial intelligence — in particular, machine learning — allows doctors to unearth subtleties that the human eye may otherwise overlook.
2. Natural Language Processing
Another type of artificial intelligence in the medical spectrum is natural language processing. This is a type of technological advancement that enables machines to interpret and utilize the human language. In healthcare, it is primarily being used in data applications, like more accurately diagnosing health complications and improving patient care to streamlining clinical procedures and offering more personalized services.
Similar to machine learning, natural language processing can diagnose illnesses accurately by extracting vital information from health data. In addition, it can also predict potential health risks and determine relevant medication and treatments for patients using past data.
In terms of managing countless documents, this powerful tool can sieve through massive amounts of complex data and extract key information in a short amount of time.
3. Rule-based Expert Systems
While the previous two types of AI latch onto algorithms to predict outcomes, rule-based expert systems require human knowledge and expertise to construct a set of constrictions to fill out various “if-then” scenarios.
To make this system work, engineers need to incorporate simple rules that are easy to understand. Once the rules become more complicated, there’s a high chance that they will clash with one another and ultimately break down. To remedy this problem, engineers are exploring numerous approaches (such as machine learning). Doing so should make these systems run more smoothly regardless of the number of rules or how they change.
4. Robotics
There are two types of robotics in AI: physical robots and probiotic process automation.
On the one hand, physical robots are industrial robots that perform like humans. They have a pre-existing list of tasks (e.g., lifting, welding, repositioning, etc.) that they can accomplish, which can aid in the hospital’s daily operations. On top of their basic skills, some robots — in particular, surgical robots — have enhanced features to help surgeons in the operating room.
On the other hand, robotic process automation is used mainly for administrative work. They are easy to program and install on computers or servers to help make administrative work more efficient.
Unlike physical robots, robotic process automation is the only software that relies on business rules, workflow, and information systems that act like semi-intelligent users. Because of this, it can be applied to repetitive tasks, record updates, and billing. In addition, when combined with technologies such as image recognition, it can extract and input data into various transactional systems.
Integrating AI in Healthcare
The emergence and integration of artificial intelligence in the healthcare system have always been groundbreaking. It was able to reshape and revolutionize the way patients are diagnosed, treated, and monitored. By introducing such technology into daily routines, it was able to drastically improve the outcomes, produce more precise diagnoses, and enable a more personalized approach.
Based on the information above, here are the different ways AI has been incorporated into the healthcare system.
AI-supported Robotic Surgery
With a market value of $10.4 billion in 2021, the estimated value for global medical robots is only expected to grow in the period from 2023 to 2028. According to the forecast, their value may reach up to $34.6 billion in 2027, which is a 22.30 percent growth from the 2021 value.
Robots are an integral part of the medical field because they can analyze data using preoperative medical records and guide surgeons when conducting surgery. This can lead to a 21 percent decrease in the patient’s stay in the hospital.
In addition, robotic surgery is also considered a minimally-invasive operation, making it easier for patients to heal post-surgery. Through artificial intelligence, these robots can utilize previously collected data to inform new techniques and garner more positive results.
In 2018, a surgical robot known as Da Vinci was used to conduct eye surgery for the first time. Back then, it allowed doctors to execute complex procedures with much greater control and precision than previously-used conventional approaches. In the end, the surgery was a success and opened the doors for more surgeries with the help of robotics.
Nursing Virtual Assistants
Nurses play an important role in the healthcare setting. They interact with patients and give them care daily. However, with the increasing demand and the shortage of actual nurses, it’s become difficult to provide each patient the care that they need. To compensate for these issues, there has been a rise in the number of virtual assistants.
According to studies, virtual nursing assistants have the potential to save medical facilities $20 billion annually. Since they should be available 24/7, virtual nurses will have more opportunities to answer patient questions and monitor patients.
The main goal of this type of technology is to foster better and more regular communication between healthcare providers and patients. Even if the patient is in between office visits, they can still ask vital questions to their provider.
As a result, patients can cut down on their hospital visits, have immediate access to clear and concise answers, and benefit from regular wellness checks.
Clinical Diagnosis and Judgment
For the longest time, medical practitioners relied on their knowledge and expertise to provide a well-educated clinical judgment and diagnosis. But with recent developments, it’s now possible to use artificial intelligence to diagnose patients. Although this system is still in development there have been impressive case studies throughout the years.
In one study, a Danish AI software company examined its deep-learning program by allowing a computer to eavesdrop on emergency dispatcher calls. The computer’s algorithm was able to analyze what the dispatcher was saying, the tone of their voice, and the background noise. Based on this data, the computer was able to detect cardiac arrests with an outstanding 93 percent success as opposed to the 73 percent success rate from humans alone.
Another study conducted by Stanford University tested their AI algorithm to identify and detect skin cancers in comparison with 21 dermatologists. Both parties were shown a series of photographs and had to classify whether or not the patient had skin cancer and, if so, what type. At the end of the study, the algorithm performed just as well as the dermatologists, but more features can be updated to the algorithm to make it more sensitive. By altering its sensitivity, researchers can add more depth and complexity to what the algorithm can accomplish and, therefore, provide a better evaluation with regard to skin lesion photos.
Image Analysis
When done manually, image analysis can be extremely time-consuming for humans. Sometimes it can take hours to analyze an image and humans can miss minute details. That’s why an MIT research team designed and developed an AI algorithm that can examine 3D scans a thousand times faster than humans can.
This increase in efficiency and real-time assessment can provide significant input for operating surgeons. Likewise, it can help improve current radiology tools and make them more detailed and accurate enough to replace tissue samples.
Moreover, facilities in remote areas that don’t have access to a wide range of medical experts can use AI image analysis to provide better care for their patients. There are virtually countless possibilities of how this tool can be used and in every case, they make it better for all the people involved.
Automation of Administrative Tasks
Besides surgeries, treatments, and patient care, the healthcare sector also deals with a lot of administrative work. From filing reports, writing chart notes, prescribing medication, and so on, medical providers spend hours each day accomplishing administrative tasks which they could use for more important tasks.
For this reason, automation of administrative tasks has become essential. An example of this is voice-to-text transcription. With this tool, nurses can more efficiently order tests, write notes, and prescribe medication without spending as much time filing paperwork. The technology can do the majority of the work while the provider simply double-checks the accuracy of the information and relays it to the essential parties.
The Benefits of AI in Healthcare
Artificial intelligence has come a long way since its conception, and it has only brought forth a series of advantages in the medical field.
Some of the benefits of artificial intelligence in healthcare can be seen below.
Unifies Human and Machine Interaction
Using technology to communicate is not a new concept, but using direct interfaces between the human mind and technology is cutting-edge and offers significant applications in the field.
Through its machine learning and natural language processing capabilities, artificial intelligence can decode meaning, extract information, and analyze complex data in a matter of minutes — if not seconds. More than this, it can also predict outcomes and communicate them with humans in a clear and concrete manner.
Expands Access to Healthcare in Developing Areas
Shortages of healthcare providers and technicians have been an ongoing challenge in the field. Especially in developing regions, these trained professionals are hard to come by, making it extremely difficult to care for patients.
That’s where AI comes into play. Since artificial intelligence relies heavily on technology, it can mitigate the impact of the lack of qualified medical staff. Instead of humans, machines can take over some of the duties and provide essential services to jumpstart patient care.
For example, nursing virtual assistants, telemedicine, and image analysis can work seamlessly even in remote locations, making healthcare more accessible to a wide range of people.
Reduces the Burden of Manual Work
In a highly demanding field, manual work brings about a myriad of challenges with regard to endless documentation, cognitive overload, and burnout. By incorporating AI into daily operations, healthcare professionals can cut down the time they spend on manual work and can focus on more complex tasks.
An example of this is the electronic health record system (EHR). Before AI, clinical staff used to document data on EHR manually. Now, with the shift towards digitization, it’s much easier to fill out EHRs since many systems use intuitive interfaces that can automate routine processes.
Creates More Precise Analytics in Pathology
Among the different specializations in the medical field, pathology is one of the most crucial departments because it provides significant sources of diagnostic information. Around 70 to 75 percent of all the data in EHRs come from pathology results. The more accurate the data, the more precise the diagnosis will be, and the better the services will be — all of which can be delivered with the help of AI and digital pathology.
Using AI, pathologists can obtain the most detailed reports from images and identify even the most minute nuances. This, in turn, can give them the opportunity to provide the most comprehensive data to inform doctors regarding the right diagnosis and treatment for every patient.
Advances in Treatment and Medication
If there’s one thing to note in the medical field, it’s that every patient’s case is unique. Every patient has a different genetic makeup that allows their body to react differently to specific treatments. That’s why customizing medications and approaches is essential to ensure that each patient receives the best care.
Fortunately, this is a task that AI can accomplish. Through machine learning algorithms, the system can synthesize complex datasets and highlight nuances, which can then illuminate new treatment options that can meet every patient’s diverse needs. This feature is especially beneficial for cancer treatment.
Besides chemotherapy, there may be other options that can give better results for different individuals.
How Will AI Shape Healthcare in 2023 and Beyond?
So far, you have gotten a glimpse into the various applications and advantages of AI in healthcare. But what comes next? What is the future of AI in healthcare and what can you expect to see?
Personalization of treatment will likely have an even greater focus as more and more data is collected. That said, by analyzing the patient’s genetics, profile, lifestyle, and environment, doctors can provide a specific treatment that can optimize better outcomes.
You can also expect to see an improvement in regulations and legislation with regard to artificial intelligence in healthcare. Since the majority of data is stored digitally, it’s time to become more aggressive in protecting patients’ confidential information. In 2023, many medical institutions will strengthen their cybersecurity to protect the privacy and well-being of not only their organization but also the people within.
As technology continues to evolve, you can also expect to see a broader range of AI applications in healthcare. Innovations will emerge and make their way into the system to enhance daily operations, which can further enhance the capabilities of current technologies.
Final Words
Through this article, you can get a deeper understanding of how AI works and how it aids in the medical field. Automation is the name of the game, and artificial intelligence in healthcare is just getting started. There’s definitely more to come in the years to follow.