Emotional AI: Why is it the future of digital health?

Have you ever heard of emotional AI? Emotional AI is a field of computer science that helps machines understand human emotions. The MIT Media Lab and Dr. Rosalind Picard are key innovators in this field. Through their work, the idea of ​​helping machines develop empathy was sparked.

Empathy is a complex concept with a lot of strings attached, but at a basic level it means understanding another person’s emotional state. In theory, if machines could achieve this level of understanding, they could better serve people. Especially in fields such as healthcare, applying empathetic AI can have a very big impact.

How can emotional AI be used in healthcare?

There are various types of emotional AI. The first detects human emotions. In the field of mental health care, this technology has huge potential for diagnostics. In terms of physical health, it could be used to monitor resilience to diseases such as cancer. This is beneficial, not least because the importance of holistic and integrated care is now widely recognized.

Next-level emotional AI can not only detect human emotions, but also respond accordingly. A good example is how it is used with people with dementia. People with dementia may have difficulty understanding their emotional states and even more difficulty communicating their feelings to caregivers. This places a heavy burden on caregivers to constantly read and interpret their feelings, a difficult task especially when already overburdened.

This opens up the opportunity for emotional AI to study biometric or psychometric things that rely less on self-assessment, such as facial expressions, verbal cues, or behavior. Emotional AI can predict a person's state of competency at a level that may be as good as or better than what a caregiver can tell them. In LUCID’s use case, this data is used to curate personalized music to help treat the psychological symptoms of dementia.

This can increase empathy for caregivers. Caregivers face increasing levels of burnout and may experience fatigue while undergoing such monitoring. With the help of artificial intelligence, it is possible to provide better care to patients and increase the stamina of caregivers.

What are the pitfalls or concerns about emotional AI?

When artificial intelligence is associated with human emotions, it’s understandable that there will be a lot of warnings. There is a gut reaction (stemming from television and Hollywood) that if machines understood emotions, they would gain sentience and possibly manipulate human emotions. It's a legitimate concern, but at the same time, these machines have a very limited playing field. It is crucial to train responsible AI through which data can be obtained to make better use of this information. This is why responsible ethics must be promoted in the field of artificial intelligence.

Technology and computers are developing faster than government legislation, so there may be a gap in policy. This is where foundations like AI For Good come into play. These frameworks and institutions are important because they help develop professional ethics and promote a positive AI culture.

Bias is another concern of the artificial intelligence community. If a data set is skewed towards one type of population, the AI ​​will be unreliable when extrapolated to the larger population. Many data collection efforts train AI to target specific types of people—those who either volunteer to participate in trials or can afford certain products. Can it reliably predict the emotions of people who don’t belong to this group? This is a common conundrum faced by artificial intelligence, and one that professionals in the field work very hard to circumvent.

Fortunately, there are strategies to prevent bias in emotional AI. It is important to be as proactive as possible in collecting participant bodies and samples from people from all walks of life. Efforts must be made to distribute these data collections as widely as possible. Another way to address bias is to develop a truly motorized product for training AI—one that is cheap, easily accessible, and distributed globally so that it can cover as many cultural representations as possible.

How is emotional AI currently being used in digital health?

The advantage of the technology is that it can transcend the doctor’s ability to integrate itself into the patient’s life. As we move toward a vertical, human-centered approach, this gap can begin to be filled through the use of artificial intelligence. With the rise of integrated care, many digital health companies are now leveraging emotional AI.

Twill (formerly known as Happify) is an example of emotional AI in the field of mental health. Its smart treatment platform uses artificial intelligence to understand a person's health needs and recommend courses of action. Its health chatbot is trained to deliver personalized care and support in an empathetic way.

LUCID also uses an artificial intelligence recommendation system to recommend music based on people’s mental state. It uses biometrics and self-assessment data as input to classify users' emotional states. By understanding people's emotions and reactions to music, the algorithm adjusts to better help.

While emotional AI may sound intimidating, it is also helping to fill gaps in patient care that traditional medical models sometimes fail to do. Patient monitoring and longitudinal care require significant human resources. One physician claimed: "Building and maintaining a longitudinal, person-centered care plan is really hard work. It requires a lot of resources. Not if the cost to the healthcare provider exceeds the benefit it gets from it." Done.”

The sooner machines become more empathetic, the better digital health tools will become. If technology can truly understand and empathize with people at every moment, it will open up many opportunities. Emotional AI is one of the most important pillars of digital health, because if a patient's condition is better understood, better treatments may be possible.

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