Artificial Intelligence: A new way of communicating

Conversational AI and patients

robot holding frame

Photo: Brett Jordan

Without a doubt, AI is changing the way in which we communicate. Chatbots, omnichannel communications, and targeted marketing campaigns have all impacted communication between service providers and customers. This goes for healthcare systems around the world as well. With the exponential development of AI technologies, new and innovative ways of communication are possible between healthcare service providers and patients. Let's take a closer look at how a natural language processing engine AI has changed the way we communicate.

Using NLP engines to increase the efficiency of AI

To begin with, it is important to go over a few short descriptions of what an NLP really is. Basically. the branch of artificial intelligence or AI whose main goal is to give AI the ability to understand and communicate with humans naturally. Natural language statistical, machine learning, and deep learning models.

NLP AI can come in the form of voice-operated systems such as the ones used in GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. NLP plays a huge role in the development of many AI systems that focus on communication between humans and AI including the ones adopted for healthcare systems.

How does an NLP engine-powered AI achieve this? NLP engine powers conversational AI. As we all know, human language is filled with many ambiguities. Regular chatbots are not capable of maintaining a meaningful conversation and a person talking with a regular chatbot can relatively easily figure out that a chatbot is behind their conversation. These ambiguities of human language present quite a big challenge for the process of training and developing a conversation AI. When it comes down to human language, homonyms, homophones, sarcasm, idioms, metaphors, grammar, etc. all differ drastically from one language to the other. Programmers must teach natural language processing engines to make these distinctions just as a regular human being would.

The tasks of an NLP engine

To truly explain how a conversation AI that uses a natural language processing engine learns how to communicate and understand human language as well as how AI changes the way we communicate, we need to separate some of its main tasks and explain each of the individually. Let's take a closer look at each one.

Speech recognition

Speech-to-text is the task of reliably converting voice data into text data. Any application or service that actively listens to voice input and answers spoken questions, requires speech recognition.

Part of speech tagging

Grammatical tagging or part of speech tagging essentially determines the part of speech of a particular word or piece of text based on its use and context. For example, we have the verb make which can also be a noun as in the sentence: "What make of car do you own?".

Word sense disambiguation

Using the process of semantic analysis AI determines the word that makes the most sense in the given context.

Named entity recognition

NEM or named entity recognition basically helps AI distinguish words or phrases as useful entities. So, this way AI learns that Spain is a location and Carlos is a male name.

Additional task

There are many other additional tasks of NLP such as Co-reference resolution^identifying if and when two words refer to the same entity(identifying if and when two words refer to the same entity), Sentiment analysis(extract subjective qualities—attitudes, emotions, sarcasm, confusion, suspicion—from text), and Natural language generation(the task of putting structured information into human language). As you can see, there is plenty of work to be done when trying to train and enable an NLP engine to effectively and efficiently communicate with patients for example.

The benefits of using a conversational AI in healthcare

So, now that we have explained how an NLP engine functions and recognizes human speech and language, let's discuss some of the main benefits of using conversational AI in healthcare. The constantly-increasing number of patients has pushed health clinics to look for new and innovative solutions when it comes to maintaining and establishing communication with patients but also when offering services.

How can AI help? Well, firstly, call centers are becoming increasingly inefficient in communication with patients. Many long-term occurring problems such as long waiting times, constant agent transfer, missed information, and so on are still present in call centers. However, with AI things are now different. For example, AI functions 24/7 and can effectively communicate with multiple patients at once. Each patient will receive an accurate and quick response. The margin of error is significantly lower as well. Furthermore, health clinics have a wider outreach as they can engage with patients living in remote areas very easily. Through conversational AI, health clinics around the world have the ability to not only establish faster communication with patients but also better maintain this communication long-term.

Artificial intelligence is truly a new way of communicating and this is especially true for conversational AI built 100% to focus on healthcare interactions with patients. More personalized care can be given because AI can be trained to engage with each patient differently, according to their needs, likes, dislikes, response time, and so on.

AI can help clinics and patients by allowing for faster, digital communication that is no longer only fiction but our reality.

With a proprietary natural language processing engine and a team of dedicated experts behind it, both patients and health clinics can experience better communication that is further strengthened by many digital services and platforms powered by AI.

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