A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions (2025)

Abstract: Mental health is increasingly critical in contemporary healthcare, with psychotherapy demanding dynamic, context-sensitive interactions that traditional NLP methods struggle to capture. Large Language Models (LLMs) offer significant potential for addressing this gap due to their ability to handle extensive context and multi-turn reasoning. This review introduces a conceptual taxonomy dividing psychotherapy into interconnected stages–assessment, diagnosis, and treatment–to systematically examine LLM advancements and challenges. Our comprehensive analysis reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. We identify critical challenges including capturing dynamic symptom fluctuations, overcoming linguistic and cultural biases, and ensuring diagnostic reliability. Highlighting future directions, we advocate for continuous multi-stage modeling, real-time adaptive systems grounded in psychological theory, and diversified research covering broader mental disorders and therapeutic approaches, aiming toward more holistic and clinically integrated psychotherapy LLMs systems.

## introduction mental health plays an increasingly critical role in current healthcare and social well-being. the high prevalence of common psychological disorders, such as depression and anxiety, has led to a growing demand for accessible and effective psychotherapy. the core of psychotherapy resides in dynamic, contextual interpersonal interactions-therapists should continuously assess and adjust their intervention strategies @xcite based on patients' emotional fluctuations, verbal expressions, and social backgrounds, fostering a strong therapeutic alliance @xcite figure 1 : the dynamic and interrelated network among assessment, diagnosis, and treatment in psychotherapy. large language models (llms) offer a new perspective to addressing this challenge. by leveraging their capability to model extensive context and perform multi-turn reasoning @xcite , llms can capture rich semantics and emotional signals in dialogues @xcite , enabling end-to-end language understanding and generation @xcite . in assessment, llms can extract potential symptom cues from vague and fragmented expressions @xcite . during diagnosis, they integrate subjective and objective patient information across multiple utterances @xcite . in therapeutic interventions, they adapt conversational strategies based on patients' real-time feedback, enabling more flexible and human-like interactions compared to traditional scripted systems @xcite . as a result, llms have the potential to surpass the conventional "discrete label recognition" paradigm, evolving toward a model of continuous, progressive clinical reasoning, enabling seamless connections across assessment, diagnosis, and treatment, aligning more closely with therapists' cognitive process and interaction flow. however, existing research on applying llms in this field remains somewhat fragmented. many studies have utilized llms for isolated tasks, such as depression detection @xcite or diagnosis @xcite , regarding them as superior feature extractors. another research line has focused on developing mental health counseling chatbots @xcite ; however, these systems remain limited to partial assistance due to insufficient integration with clinical workflows. in other words, although llms hold the potential to span the entire continuum from assessment to intervention, they remain limited by the fragmented paradigms of traditional nlp, preventing them from fully leveraging their dynamic, contextual capabilities. to address these gaps, we introduce the first conceptual taxonomy that divides the psychotherapy process into three interconnected dimensions: assessment, diagnosis, and treatment, and systematically review recent advancements and critical challenges of applying llms at each stage. we provide an extensive analysis of the current landscape from multiple perspectives, including the distribution of research across different psychotherapy stages, the coverage of mental disorders, the diversity of linguistic resources, and the incorporation of psychotherapy theories. moreover, we critically evaluate the fragmented nature of existing approaches, highlighting the inadequacies in capturing dynamic symptom representations, the inherent limitations due to linguistic resource biases and problematic translations, and the diagnostic risks affecting clinical acceptance. building on these findings, we outline essential future directions, emphasizing the need for continuous multistage modeling for coherent patient state tracking, real-time adaptability grounded explicitly in psychological theory, and a broadened scope of mental disorders and therapeutic frameworks. through this comprehensive review, we aim to offer detailed methodological insights, guiding future research efforts and facilitating the practical, continuous, and theoretically-grounded integration of llms across the full spectrum of psychotherapy. organization of this survey. we present the first comprehensive survey of recent advancements in applying llms to psychotherapy. we introduce a conceptual taxonomy that organizes psychotherapy into three core components-assessment, diagnosis, and treatment-and details their dynamic interrelations (section §2). we review how llms are applied within these components, highlighting their roles in facilitating assessments, refining diagnostic processes, and enhancing treatment strategies (section §3). we examine current research trends, including symptom and language coverage as well as the distribution of various models and techniques (section §4). finally, we discuss open challenges and outline promising directions for future work (section §5). ## conceptual taxonomy to establish a standardized framework for understanding psychotherapy, we propose a hierarchical taxonomy aligned with the american psychological association (apa)'s tripartite model of psychotherapeutic processes foot_0 . as illustrated in figure 1 , this taxonomy organizes psychotherapy into three core components: (1) assessment, (2) diagnosis, and (3) treatment, with dynamic interconnections foot_1 . each component is detailed below. ## conclusion llms hold significant promise for revolutionizing psychotherapy by enhancing assessment, diagnosis, and treatment. however, the current landscape reveals critical limitations: research is often fragmented across these stages, exhibits notable biases in linguistic and disorder coverage, and underutilizes diverse psychotherapeutic theories. to overcome these challenges, future work must focus on creating continuous, multi-stage models that are grounded in psychological theory and capable of real-time adaptation to evolving patient states. expanding the scope of addressed disorders and therapeutic modalities will be crucial for developing llm-driven psychotherapy tools.