Artificial Intelligence in Clinical Practice: Mapping Applications Inside and Outside the Treatment Room
Method Stream: Literature Review
ECs: Only 14 EC (standard, no or limited own data collection. Applicable in case of a clinical internship)
Description:
Artificial intelligence (AI) is rapidly transforming mental health care across multiple levels. Outside the treatment room AI supports administration and documentation, enables large-scale analysis of intensive longitudinal data (e.g., EMA), informs diagnostic algorithms, and assists treatment planning tailored to individual trajectories. Inside the treatment room, AI can provide real-time support (speech assistants, clinician decision aids), augment therapeutic delivery (conversational agents, adaptive in-session interventions), and serve as direct therapeutic tools.
This project is a coordinated literature (scoping) review: two students will run a joint, pre-specified search strategy and screen studies together, ensuring a shared evidence base. Each student will then focus on one complementary subtopic:
· Thesis A - AI outside the treatment room: administrative and documentation tools; large-scale and intensive data analysis (EMA, sensor data, digital phenotyping); diagnostic and prognostic algorithms; treatment planning based on patient data.
· Thesis B - AI inside the treatment room: real-time clinician support and speech assistants; AI-mediated therapy delivery (chatbots, conversational agents); adaptive in-session interventions and clinician-AI collaboration.
Together the two theses will map the breadth of AI in clinical practice and provide in-depth syntheses of distinct application domains, methodological approaches, evidence of feasibility/effectiveness, and ethical/practical considerations.
What are we looking for?
We are looking for motivated students with an interest in the intersection of technology and clinical psychology, who enjoy working systematically with academic literature and collaborating in a team.
Possible guiding research questions:
- What types of AI applications are currently used in clinical practice?
- What evidence is available regarding feasibility, acceptability, and effectiveness?
- How are ethical, legal, and practical considerations addressed in the literature?
- What gaps and future directions are identified by researchers and practitioners?
Who are we looking for?
Students with strong analytical skills, curiosity about digital innovations in mental health, and readiness to collaborate closely with a peer.
What do we offer?
Supervision in conducting a systematic literature review, guidance on structuring and writing the thesis, and expertise in digital interventions and clinical practice.