S3E16 Building a Journaling Bot with Academics Daniel Lametti and Joanna Kuc
May 2, 2024
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Daniel Lametti and Joanna Kuc discuss creating an advanced chatbot for mental health journaling. They explore the use of conversational AI, linguistic biomarkers for mental health, and the challenges of participant engagement. The podcast highlights their innovative design process and findings in an engaging discussion.
Utilizing conversational AI in journaling enhances mental well-being by encouraging reflection and providing personalized summaries.
Analyzing linguistic markers with AI can aid in understanding emotional states and developing predictive models for mental health conditions.
Deep dives
Role of Conversational AI in Mental Health
Conversational AI's role in mental health is demonstrated through the utilization of large language models to enhance journaling experiences for users. By employing conversational AI in facilitating the journaling process, participants were encouraged to reflect on their daily experiences and emotions, potentially improving their mental well-being. The use of generative AI in providing personalized summaries of journal entries aimed to make journaling more engaging and effective, offering insights into individual mental health.
Exploring Language Biomarkers in Mental Health
The podcast delves into the significance of linguistic markers in mental health analysis. By examining acoustic markers and content from participant entries, unique elements in speech patterns were identified to potentially aid in understanding individuals' emotional states. The integration of conversational AI and linguistic markers may lead to more effective predictive models for mental health conditions, enabling early interventions based on subtle indicators.
Human-AI Collaboration in Mental Health Care
The discussion highlights the collaboration between AI and mental health care providers in monitoring and supporting patients' well-being. Utilizing conversational AI to collect and analyze data, the system can offer insights to therapists for informed decision-making during patient visits. The integration of linguistic biomarkers and personalized feedback not only enhances patient engagement but also provides valuable data for clinicians to track and address mental health concerns.
Daniel Lametti and Joanna Kuc join Robb and Josh to share the advanced chatbot they created for a research project in mental health. Dan Lametti is an Associate Professor of Cognitive Psychology at Acadia University in Nova Scotia, Canada. Dan taught and conducted research in experimental psychology at the University of Oxford, and is also the Director of Academic Fellowships at OneReach.ai. Joanna is a Data Scientist at Compass Pathways as well as a PhD Candidate in Experimental Psychology at University College London, where her work focuses on decoding language biomarkers relating to mental health. Using the OneReach.ai platform, Daniel and Joanna created a conversational app in Telegram that collects journal entries from participants as either written text or voice notes, and generates weekly summaries. Learn more about this unique design process and some of their findings in an engaging and practical episode of Invisible Machines.
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