What is AI?
Artificial Intelligence is simulated human intelligence within computers- these computers are programmed to break down, analyse, and mirror the actions of their human counterparts. This specific terminology may also refer to any machine that exhibits human-like characteristics, such as constant learning and problem-solving. Machines such as this are extremely beneficial to society, especially within the healthcare sector, as it is competent, practical, and reduces the risk of human error. Practicality and competency are highly prevalent within AI because machines are fundamentally more efficient; especially in diagnosing medical problems. However, there are many advantages and disadvantages to Artificial Intelligence in Healthcare.
Here are some of the reasons as to why
As we can see from the table above, there are multiple pros and cons as to why AI may potentially be beneficial, but they do have their shortcomings. In order to provide a more in-depth and succinct breakdown of this table, we will explore the various types of AI utilized within the healthcare system, and systematically break down the various types of AI in healthcare.
Types of AI Utilized within Healthcare
Natural Language Processing: Though it sounds quite complex, Natural Language Processing (NLP) is actually quite simple in terms of functionality. Essentially, traditional NLP systems include the input of text analysis or speech recognition, then subsequently translate them to whichever form of output is required.
However, within the healthcare system, NLP has a more specific use: the classification, dissemination, and understanding of clinical documentation. Clinical NLP systems are designed in such a way so that they are able to analyze clinical notes, and subsequently provide detailed insight into any given patient. These insights may range anywhere from providing improvement of treatment methods to the betterment of a particular patient, or even alternative treatments- depending on that particular patient, their needs, and their healthcare records.
Machine Learning
Gaining more traction in recent years, machine learning has become a common sight for the utilization of AI within healthcare. As machine learning is a relatively generalized terminology, we will move further into the workings as to what machine learning within the healthcare industry really is.
As machine learning is an incredible asset in regards to AI in the healthcare industry, it has a substantial amount of beneficial uses, some of which may be seen above within our NLP segment. Other than NLP usage, machine learning has become an unprecedented force in the application towards precision medicine. Also known as supervised learning, AI are able to select which treatments and/or procedures have the highest chance of success for patients, based on the data inputted into the system. These data include the patients’ past medical history, their treatments, and their treatment framework, as well as their genetic make-up.
Rule-Based Expert Systems
During the 1980’s, these systems were at the forefront of AI technology. This ‘if-then’ rule collection and was used commercially during the 1980’s, as well as later periods. This system was also used decades ago for ‘clinical decision support’ and is still used worldwide due to its proven track record.
These require human expertise to create rules within a particular domain of knowledge. However, if there are too many rules, the rules may conflict with each other, causing a breakdown of fundamental logic and concepts. These systems are slowly being replaced in healthcare by newer approaches based on data, and machine learning algorithms.
Telemedicine
Another recent occurrence of AI usage within the healthcare industry has been telehealth. Telehealth is a relatively recent occurrence, especially with the ongoing crisis of COVID-19. Made even more accessible and efficient due to advancements in technology and AI, telehealth essentially allows for your clinical practitioner to provide you with care and support, without the need for a physical visit to the clinic or hospital. Telemedicine is heavily reliant on internet access, and options include (but are not limited to) the following:
- Speaking to your medical practitioner via the telephone or a live video call
- Messaging your medical practitioner via email, file exchanges, messages, and more
- Off-site monitoring so that your medical practitioner may monitor your vitals remotely
In conclusion, to advance AI in healthcare, we must first begin by more precisely determining clinical needs. We may not merely design in theory; we have to design where our products and services will be used: on the front lines of our healthcare systems, in medical centers, (e.g. hospitals and clinics), and even in our own homes.
Consider the fact that Artificial Intelligence is not a possible substitute for well-trained medical professionals, but merely a complement for trained medical professionals, providing a few additional pairs of eyes and unlimited memory and processing capabilities, thus expanding our collective detection and diagnostic capabilities.
