From ensembles to knowledge-augmented reasoning: Information fusion for depression detection in the generative AI era

Abstract

Computational depression detection has reached a saturation point with standard end-to-end deep learning paradigms. To navigate the rapid proliferation of highly complex models, this systematic scoping review analyzes 87 primary post-LLM studies, spanning both “in-the-wild” social media and structured clinical interactions, published primarily between January 2024 and April 2026. Evaluated alongside 7 foundational 2023 baselines (totaling 94 core methodology papers out of 111 cited references), these works mark a distinct methodological transition from isolated feature engineering to multimodal and architectural synthesis. We introduce a novel taxonomy that categorizes these emerging approaches into four information fusion paradigms: (1) Architecture Fusion, which hybridizes heterogeneous neural components to capture complementary spatiotemporal dependencies; (2) Knowledge Fusion, which injects structured external clinical knowledge (e.g., DSM-5 ontologies) into neural pipelines to bridge the semantic gap; (3) Generative Fusion, which integrates in-context learning (ICL), retrieval-augmented generation (RAG), and parameter-efficient fine-tuning (PEFT); and (4) Data-Level Fusion, which addresses multimodal alignment, dynamic fusion timing, and temporal modeling. Our critical analysis reveals that while zero-shot large language models (LLMs) offer broad semantic reasoning capabilities, fine-tuned encoders and hybrid architectures integrated with domain-specific knowledge currently provide a more rigorously validated balance of reliability, explainability, and performance. We conclude by exposing the “illusion of accuracy” prevalent in proxy-labeled benchmarks and outlining strategic directions for developing clinically valid, privacy-preserving detection systems suitable for real-world clinical workflows.