Because it is the future of digital health

Chief Technology Officer & Co-Founder of CLEAR. Proud advocate of mental health awareness and technology for good.

Have you ever heard of emotional artificial intelligence (AI)? Emotion AI is an area of ​​computer science that helps machines understand human emotions. The MIT Media Lab and Dr. Rosalind Picard are the leading innovators in this space. Through their work, they sparked the idea of ​​helping machines develop empathy.

Empathy is a complex concept with many threads attached to it, but at a basic level, it means understanding another person’s emotional states. In theory, if machines can have this level of understanding, they can serve us better. Particularly in areas like healthcare, applying AI empathy can have a very big impact.

How is emotion AI being used in healthcare?

There are different types of AI emotions. The first kind detects human emotions. In the field of mental health, this kind of technology has great potential in diagnosis. In terms of physical health conditions, they can be used to monitor resistance to conditions such as cancer. This is beneficial especially since the importance of holistic and integrated care is now widely recognized.

The next level of emotion AI not only detects human emotion but has the ability to respond accordingly. A great example of how it can be used is with the population living with dementia. People living with dementia may find it difficult to understand their own emotional state and even more so to communicate with their carers how they are feeling. This puts a lot of burden on caregivers to constantly read and decipher how they are feeling, which is difficult when you are already overwhelmed.

This opens up the opportunity for emotional AI to look at things like biometrics or psychometrics that are less dependent on self-assessment – such as facial expression, speech cues or behavior. Emotion AI allows us to predict what a person’s condition is with a level of skill that can be as good or even better than what a caregiver could tell us. In our use case at LUCID, we use this data to curate personalized music to help with the psychological symptoms of dementia.

This can increase compassion towards caregivers. Caregivers face increasing levels of burnout and may experience fatigue when doing this type of monitoring. Bringing AI in to help can provide better patient care and increase resilience for caregivers.

What are some disadvantages or concerns about emotional AI?

When artificial intelligence gets involved with human emotions, many alarms are understandably raised. There is a gut reaction (coming from TV and Hollywood) that if machines understood emotion, they could gain emotion and potentially manipulate our emotions. This is a valid concern, but at the same time, these machines are given a very limited playground to play in. Training the responsible AI is crucial, whereby they are given data to do good with that information. This is why we must push for responsible ethics in artificial intelligence.

Technology and IT develop faster than state law, so policy gaps may exist. That’s where foundations like AI For Good come in. These frameworks and institutions are important because they help develop professional ethics to promote a positive culture around artificial intelligence.

Bias is another concern for the AI ​​community. If the datasets are biased towards a certain type of population, the AI ​​will not be reliable when you extrapolate it to the larger population. Many of these data-gathering efforts trained the AI ​​on specific types of people—people who either volunteered for testing or could buy certain products. Would it reliably predict emotions for people outside of that population? This is a difficult problem for artificial intelligence in general, which professionals in this field are working very hard to overcome.

Fortunately, there are strategies to prevent AI emotion bias. It is important to collect active participant bodies and samples from people from all walks of life wherever possible. You should make an effort to distribute this data collection as widely as possible. Another solution to bias is to develop a truly driven product to train AI — one that is cheap, accessible, and globally distributed so that it can cover as many cultural representations as possible.

How are empathic machines being used in digital health today?

Technology has the advantage of being able to integrate into a patient’s life beyond what a doctor can. As we move towards a timeless, human-centric approach, this gap can begin to be filled through the use of artificial intelligence. With the rise of integrated care, many digital health businesses are now leveraging emotion AI.

Twill (formerly Happify) is an example of using emotional AI in mental health. The Intelligent Healing platform uses artificial intelligence to learn about someone’s health needs and recommend a course of action. Its health chatbot is trained to provide personalized care and support in an empathetic manner.

LUCID also uses an AI recommendation system to suggest music based on one’s mental states. It leverages biometrics and self-assessment data as inputs to classify a user’s emotional states. By learning about someone’s mood and response to music, the algorithm adjusts to help them better.

While empathic machines and emotion AI may sound scary, they help fill the gap in patient care that traditional health models sometimes fail to do. Patient follow-up and chronic care use a lot of human resources. One physician asserted, “Creating and maintaining an enduring, person-centered care plan is really hard work. It requires a lot of resources. No health care provider is going to do it if it costs them more to do the program than the benefit they get from it.”

The faster we can make machines more empathetic, the better our digital healthcare tools will become. It can open up many opportunities if, through technology, we can truly understand how people are feeling at any moment—and empathize. Emotion AI is one of the most important pillars of digital health, because if we understand better what is going on with the patient, we have a better way to treat them.

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