Wearable devices like smartwatches, fitness bands, and smartphone sensors collect data all the time. This data includes heart rate, skin response, sleep patterns, and activity levels. These details give clues about a person’s health. When AI programs analyze this data, they can constantly check a person’s mental health. This helps find early signs of problems like depression, anxiety, or substance use issues.
At Dartmouth, researchers with a $20 million grant from the National Science Foundation are building AI assistants that work with these sensors to give mental health help that fits each person’s situation. The AI Research Institute on Interaction for AI Assistants (ARIA) aims to create systems that understand signals from the body and mind to spot mental health symptoms early. This can help care teams act before problems get worse.
Dartmouth’s CTBH created technologies like MoodCapture and MindScape. MoodCapture is an app that uses AI and facial recognition to spot signs of depression by looking at small changes in facial expressions during normal smartphone use. MindScape mixes behavior data with AI models like ChatGPT to give personalized mental health support. These tools show how AI and sensor data can come together to make care plans for individuals.
Using wearables with AI assistants can also lower the limits some people face when trying to get mental health help. Some may feel better talking to AI chatbots or digital coaches since they offer real-time support without judgment. This is especially helpful for people who don’t have easy access to mental health specialists.
One big benefit of AI assistants working with wearables is giving help right when it is needed. Mental health can change quickly, and small early signs often happen before a full episode. AI tools look at many kinds of information at once, like body signals and behavior changes, to notice these early warnings.
For example, Dartmouth researchers are making AI models that find patterns in body and brain signals that come before symptoms of major depression. They also study what leads to opioid relapse by looking at data about the environment and behavior. AI assistants use this information to give advice and alerts that doctors can use to adjust treatments or medicines.
In clinics across the U.S., mental health providers can link these AI systems to telehealth or electronic health records (EHRs) to get alerts about patient risks right away. These alerts can remind providers to reach out, change medicines, or set up urgent visits. This approach tries to prevent problems before a crisis starts.
AI-powered remote patient monitoring (RPM) systems are used more in hospitals and clinics to keep tracking patients all the time. Recent data shows that AI analyzing wearable data can find issues like irregular heartbeat, breathing problems, and mental health crises early. This helps lower hospital readmissions and improves patient care. For practice managers, using RPM tech can save money and resources by lowering emergency visits and hospital stays.
Mental health care can be hard because symptoms vary a lot between people. AI assistants help by combining many types of data to get a full picture of a patient’s condition. These include body signals, behavior data, medical history, patient feedback, and social factors.
By mixing wearable sensor data with electronic health records (EHRs) and what patients report, AI can make treatment plans that change as the patient’s condition changes. For example, if sensors show increased heart rate and poor sleep along with a patient saying they feel stressed, AI can suggest treatments like therapy, medication reviews, or lifestyle changes.
HealthSnap is a company that provides remote patient monitoring in the U.S. They connect with over 80 big EHR systems and use AI analytics plus cellular-connected devices to help manage chronic and mental health. Services like this let practices without virtual care in-house offer support early and actively.
AI assistants use Natural Language Processing (NLP) to study the words patients speak or write. This checks feelings and emotional tone. When this info is added to data from wearables, AI can make better mental health predictions and customize support.
Managing mental health work can be hard and time-consuming for clinic staff and managers. AI helps not only with patient assessment and treatment but also by automating many admin tasks.
Generative AI is now common in healthcare to reduce the paperwork burden on doctors and nurses. Research shows AI tools can cut the time spent on writing notes by as much as 74% and save nurses 95 to 134 hours a year. This gives clinicians more time to work directly with patients.
AI virtual assistants can also handle appointment scheduling, reminders, and follow-ups using natural conversation. This lowers no-shows and makes providers’ schedules more efficient. AI workflows can also analyze patient data to focus on those who need urgent care and help coordinate treatment better.
As more hospitals and clinics in the U.S. adopt AI and generative AI, practice owners and IT managers must think about challenges like making systems work together, following privacy laws (HIPAA), and using AI ethically. Platforms that follow standards like SMART on FHIR help data move smoothly between wearables, AI apps, and EHRs.
Training programs for healthcare workers, such as those by the ARIA institute, help clinical staff understand AI outputs well. This training helps maintain good clinical oversight, avoid bias in AI, and build trust in these tools.
Focusing on these points helps make sure AI-driven mental health support is safe and trusted.
The United States is investing a lot in AI for mental health care. Dartmouth College plays a big part, building on its long history in AI research. Their AIM HIGH Lab created the first fully AI-based psychotherapy chatbot that is now in clinical trials. This is a step toward proven AI digital therapy.
Dartmouth and other schools through ARIA focus on research that connects AI with mental health care. The institute also trains students and professionals to become future healthcare AI experts ready to use these tools.
In the future, AI assistants combined with wearables will keep improving by using new ways to mix data, model thinking, and personalize medicine. These tools may change mental health care in the U.S. by offering continuous and careful support tailored to each person.
By focusing on these steps, U.S. healthcare providers can use AI assistants and wearable sensors to deliver better personalized mental health care while running more efficiently.
This blending of AI-powered assistants with wearable sensors is an important step toward mental health care that is proactive and tailored. Using it in U.S. clinics can help patients get better care and make healthcare operations smoother. As the tools improve and use grows, paying close attention to clinical, ethical, and technical issues will be key to success in real-world use.
Dartmouth serves as a leading partner in ARIA, a national AI research institute focused on AI assistants for mental and behavioral health. It leads ‘use-inspired’ research integrating AI with devices and wearable sensors to provide personalized mental health assessment and intervention.
ARIA aims to develop AI-powered assistants capable of trustworthy, sensitive, and context-aware interactions addressing mental health and substance-use needs, advancing best practices through scientific evidence and personalized real-time feedback.
Dartmouth initially targets major depressive disorder and substance use disorders, specifically opioids, by studying physiological, behavioral, and neural data to produce timely interventions preventing symptom onset and relapse.
Lisa Marsch leads behavioral and mental health research; Andrew Campbell oversees AI mental health testbed and technology integration; Nicholas Jacobson directs AI psychotherapy chatbot development and digital biomarkers; Steven Frankland coordinates cognitive science research and computational modeling for adaptive AI.
Dartmouth developed the first FDA-authorized digital addiction treatment, pioneered use of behavioral sensing in clinical trajectories, and conducted longitudinal studies like StudentLife evaluating mental health through mobile and wearable tech.
By combining sensor and neuroimaging data with AI and cognitive neuroscience, Dartmouth creates models that interpret physiological and environmental cues, enabling real-time personalized interventions and predictive analytics for mental health management.
AIM HIGH develops generative AI psychotherapy chatbots undergoing clinical trials and works on digital biomarkers to predict individual mental health changes, ensuring AI systems are personalized, safe, and clinically valid.
ARIA provides training and curricula for middle, high school, undergraduate, and graduate students, alongside mentoring and interdisciplinary collaboration, preparing the next generation of AI scientists and mental health professionals.
ARIA organizes workshops, roundtables, and certificate programs to educate mental health practitioners, facilitating efficient clinical implementation of AI innovations and bridging research with practice.
It bridges human intelligence and AI, aiming to build systems with flexible and reliable understanding of users and environments, which is vital to developing effective AI interventions for complex mental health disorders.
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