Podcast Summary: Artificial Intelligence and Mental Health [Part 2]
The rise of artificial intelligence is transforming healthcare, influencing both the study of mental health and the delivery of care. In the first part of our two-part series on AI in mental health research, we examined what AI is and how it’s already impacting healthcare.
In part two of the series, Dr. Sagar Parikh and Dr. Frank Rudzicz focus on the practical applications of AI in mental health research. They highlight the innovative work being done with CAN-BIND and discuss the ethical considerations that arise when integrating AI into mental health care.
Joining Forces: AI Meets Mental Health Research
Dr. Rudzicz became involved with CAN-BIND after joining Dalhousie University, where he connected with psychiatrist Dr. Rudolf Uher and his data science team. While most of the AI work within CAN-BIND is still at the research stage, the long-term vision is clear: to develop safe, clinically useful tools.
Collaborations like CAN-BIND are especially valuable because they bring together technical experts and clinicians, combining technological innovation with real-world healthcare needs. Using AI, Dr. Rudzicz’s team can analyze speech patterns to detect symptoms and monitor changes in depression over time.
Their efforts extend beyond simply “listening for illness.” The team is also addressing technical challenges, such as separating overlapping voices in conversations and using AI to summarize lengthy clinical discussions so that clinicians can quickly access the most relevant information. The ultimate goal is to create tools that help clinicians make better decisions, faster. If successful, these tools could eventually be scaled beyond Canada.
“…a network like CAN-BIND is really great because it can combine people who are working on the tools, the technological people like myself and my students, and then the clinical people who have the domain expertise and the real world need for these tools. That’s a really great combination..” — Dr. Rudzicz
Challenges and Considerations
AI is not perfect. In systems like ChatGPT, hallucinations occur when the AI confidently provides information that isn’t accurate, such as fake references or incorrect facts. Techniques like retrieval-augmented generation, or RAG, help the AI base its answers on real information, reducing mistakes, though the problem is not fully solved.
Another concern is whether AI could reduce the skills of healthcare professionals. Dr. Rudzicz compares this to the introduction of calculators in schools. While students may become less practiced in manual calculations, they gain the ability to tackle more complex problems. Similarly, AI can take over routine tasks like filling out electronic records, allowing clinicians to spend more time with patients, focus on research, and make thoughtful, informed decisions.
“I think it’s not a matter of like turning a big switch in terms of overall skill. It’s just that doctors will focus on different things and hopefully they’ll be able to focus on things that matter more to them and to their patients.” — Dr. Rudzicz
Thinking About the Bigger Picture
AI isn’t just about efficiency… it also comes with responsibilities. One major concern is its environmental impact. Training AI models consumes enormous amounts of electricity, while day-to-day use, such as asking ChatGPT a question, uses far less. Still, widespread use can add up, highlighting the importance of smaller, task-specific models rather than massive systems designed to do everything.
Ethical concerns are also important to consider. AI can unintentionally reinforce existing biases in healthcare, putting certain groups at a disadvantage. Encouragingly, researchers are developing methods to identify and reduce these biases, making fairness a central focus in AI development.
Final Takeaway
While challenges remain, AI holds enormous potential in mental health care and research. By streamlining clinical workflows and detecting subtle signs of depression, it could transform how care is delivered if it is developed thoughtfully, ethically, and sustainably.
This summary has been adapted from Part 2 of our special two-part podcast series on artificial intelligence and mental health. Listen to the full episode here.