Causal inference is paramount for understanding the effects of interventions, yet extracting personalized insights from increasingly complex data remains a significant challenge for modern machine learning. This is the case, in particular, when considering functional outcomes observed over a continuous domain (e.g., time, or space). Estimation of heterogeneous treatment effects, known as CATE, has emerged as a crucial tool for personalized decision-making, but existing meta-learning frameworks are largely limited to scalar outcomes, failing to provide satisfying results in scientific applications that leverage the rich, continuous information encoded in functional data. Here, we introduce FOCaL (Functional Outcome Causal Learning), a novel, doubly robust meta-learner specifically engineered to estimate a functional heterogeneous treatment effect (F-CATE). FOCaL integrates advanced functional regression techniques for both outcome modeling and functional pseudo-outcome reconstruction, thereby enabling the direct and robust estimation of F-CATE. We provide a rigorous theoretical derivation of FOCaL, demonstrate its performance and robustness compared to existing non-robust functional methods through comprehensive simulation studies, and illustrate its practical utility on diverse real-world functional datasets. FOCaL advances the capabilities of machine intelligence to infer nuanced, individualized causal effects from complex data, paving the way for more precise and trustworthy AI systems in personalized medicine, adaptive policy design, and fundamental scientific discovery.

Telehealth is a valuable tool for primary health care (PHC), where depression is a common condition. PHC is the first point of contact for most people with depression, but about 25% of diagnoses made by PHC physicians are inaccurate. Many other barriers also hinder depression detection and treatment in PHC. Artificial intelligence (AI) may help reduce depression misdiagnosis in PHC and improve overall diagnosis and treatment outcomes. Telehealth consultations often have video issues, such as poor connectivity or dropped calls. Audio-only telehealth is often more practical for lower-income patients who may lack stable internet connections. Thus, our study focused on using audio data to predict depression risk. The objectives were to: 1) Collect audio data from 24 people (12 with depression and 12 without mental health or major health condition diagnoses); 2) Build a machine learning model to predict depression risk. TPOT, an autoML tool, was used to select the best machine learning algorithm, which was the K-nearest neighbors classifier. The selected model had high performance in classifying depression risk (Precision: 0.98, Recall: 0.93, F1-Score: 0.96). These findings may lead to a range of tools to help screen for and treat depression. By developing tools to detect depression risk, patients can be routed to AI-driven chatbots for initial screenings. Partnerships with a range of stakeholders are crucial to implementing these solutions. Moreover, ethical considerations, especially around data privacy and potential biases in AI models, need to be at the forefront of any AI-driven intervention in mental health care.

Whether we're ready or not, artificial intelligence (AI) already plays a role in many health care settings. However, cautiously developing, deploying, and even defining further AI advancements will determine its impact and efficacy in the years ahead, according to a new University of Western Ontario study. Interdisciplinary researchers from family medicine, computer science, and epidemiology have identified key issues regarding the use of AI tools in primary health care by connecting directly with family physicians, nurses, nurse practitioners and digital health stakeholders. Overwhelmingly, the responses show AI could have a positive impact in clinical practice, but many factors must be considered regarding its implementation. "We are ready for AI, but we must be thoughtful about how and when we use it," said Dan Lizotte, an associate professor in computer science and the Schulich School of Medicine & Dentistry and senior author on the study.

Brent is a PhD Candidate in the Department of Computer Science and a member of the Φ Lab and Insight Lab. He was previously the instructor for MMASc 9251A: Professional Computing for Applied Scientists and presently the Teaching Assistant for Unstructured Data. As of March 1st 2019, Brent will also be a Mitacs Accelerate Intern. This work is with the Parkwood Institute and IBM with the target of improving mental health resources for Canadian Veterans. His research interests are two-fold.