As per some research, AI acts better than humans when it is about diagnosing diseases. You can notice that AI-based technologies are outperforming radiologists at identifying malignant tumors during clinical trials.
After observing the excellent outcome, the clinicians believe that AI will replace human efforts in the medical field, but not immediately. In this article, you will get to know the potential of Artificial Intelligence and the significant obstacles to the swift execution of AI in healthcare.
Various Types Of AI For Healthcare
Machine Learning
Machine Learning is perhaps the most common AI application that enables the system to learn the data and improve the real-time experience without any programming. This app mainly focuses on computer programs to access and analyze the data automatically. The machine learning system diagnoses medical protocols to cure a patient based on his treatment procedure. The precision medicine applications and the great majority of machine learning need a training dataset outcome known as supervised learning.
The neural network is another technology that has been available for healthcare research work since the 1960s. It is the complex form of machine learning that determines if a patient will be affected by a particular disease in the future.
Natural Language Processing
Since the 1950s, AI researchers have been trying to make sense of human language use in applications. The NLP comes with some excellent applications that analyze texts, recognize human speech, and meet other goals related to this field. There are two primary sections of NLP: Statistical NLP and Semantic NLP. These applications work vigorously to understand, analyze and classify the published research work and clinical documentation. It is to mention that NLP systems processes unstructured clinical data about patients and transcribe their interactions, arrange reports, and run conversational AI.
Rule-based Expert Systems
Rule-based expert systems are a fantastic dominant technology for AI. In the 1980s, these systems were primarily installed and are in wide use in today’s world. It highlights that comprehensive EHR (Electronic Health Record) providers employ a set of rules with their AI systems. Rule-based expert systems require knowledgeable human experts or engineers to set a series of rules. The team may find it challenging and time-taking to change the rules if the knowledge domain changes. Furthermore, if the highlights rules conflict with each other, they may break down.
Physical Robots
Physical robots are perhaps the best invention of Artificial Intelligence. The robots do various types of pre-defined tasks like welding, lifting, assembling objects, repositioning goods in warehouses or factories. The robots also serve the medical sector by delivering supplies and conducting machines. Moreover, in recent times, robots are collaborating with patients. The devices are becoming more intelligent as AI specialists use AI technology to program the robot’s brain (operating system).
In 2000, the United States certified surgical robots with AI superpowers to enhance their functional skills. Although the human surgeons will take all the crucial decisions, the robots will perform the surgical procedures.
Robotic Process Automation
The Robotic Process Automation supports administrative purposes with structured digital tasks. When you compare with other AI applications. Robotic Process Automation is inexpensive and easy to program. However, as the name suggests, RPA doesn’t involve any physical robot; instead, it includes compute programs. RPA helps update patient records, prior authorization, get accurate test reports, and more to support the healthcare industry. Although AI comes with several different apps, gradually, the programs are being integrated and combined. For instance, physical robots have AI-based brans. On the other hand, Robotic Process Automation is being integrated with Image recognition.
Diagnosis And Treatment Applications
Before treating a disease, it is essential to diagnose the significant health issues of the patient. From the earlier days, rule-based systems have been performing well in diagnosing the problems and treating the health problems accurately. But then medical practitioners did not adopt this system as human diagnosticians were more efficient.
Recently Watson from IBM has got significant attention by helping the healthcare industry with cancer diagnoses, treatment, and precision medicine. Watson isn’t a single product but uses application programming interfaces to offer a series of cognitive services. This system employs Neuro-Linguistic Programming and a combination of machine learning capabilities to identify the cancer types.
In today’s world, AI-based diagnosis and treatment have been encouraging by multiple healthcare organizations. However, rule-based systems are widely used when incorporated within Electronic Health Records. Not surprisingly, the rule-based clinical decision support systems lack the accuracy of algorithmic systems related to machine learning. The experts find it challenging to maintain the explosion of data as it becomes an issue when the medical knowledge based on proteomic, genomic, metabolic, and others changes.
Patient Engagement Is Essential
It’ll be great if patients get familiar with their health status and treatment procedure. The more patients take part in the well-being process, the greater the outcome. Apart from the financial aspects, the sick person should get familiar with the health factors related to his condition. The patient has to undergo behavioral adjustments such as scheduling follow-up visits, joining weight loss programs, complying with treatment plans, and filling prescriptions. It is challenging to cure the disease when patients neither follow the guidelines and nor take the prescribed medicines.
According to a survey involving over 300 healthcare executives and clinicians, less than 50%of patients are familiar with the treatment procedure. Only 25% of the patients follow the proper guidelines while maintaining medical rules. In such a situation, it’ll be great if AI innovations can drag the patients to get engaged with the treatment procedure by using the applications.
In the end, it is needless to say that Artificial Intelligence has taken the medical sector by storm. The unique applications, machine learning processes have elevated the service quality to a great extent. And that’s why it is high time for medical officials to get the best result out of AI-based technologies.
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