When it comes to healthcare, Artificial Intelligence plays a significant role in elevating patient care services.

You cannot deny that AI and related technologies are doing wonders by transforming various aspects of the medical sector.

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.

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