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Haifang Li

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Review Open access Jul 2026

ACCELERATE-BASSO: early experiences and emerging best practices for ontology development in behavioral and social science research.

BACKGROUND Behavioral and social science research (BSSR) is essential for understanding human behavior and informing interventions, policy, and health outcomes; however, such research faces persistent challenges related to fragmented knowledge, imprecise and inconsistent terminology, and limited interoperability across studies. Ontologies provide a promising solution by enabling standardized, machine-readable representations of concepts and relationships to support data integration, knowledge synthesis, and reproducibility. RESULTS We present an initial overview of the ACCELERATE-BASSO Research Network, an NIH-supported consortium established to advance BSSR through ontology-driven approaches. Specifically, we describe the role of the Dissemination and Coordination Center (DCC) and the Best Practices Working Group (BPWG) in supporting a network of projects focusing on accelerating BSSR through ontology development and use. Based on the consortium's early experiences, we summarize emerging ontology-driven practices, current interoperability efforts, and common methodological considerations identified across the participating projects. Rather than proposing a formal consensus framework, this work provides an initial consortium perspective on current ontology development activities and shared lessons learned. We also discuss the potential role of ontologies in supporting AI-related applications in BSSR. CONCLUSIONS This paper provides an initial consortium perspective on ontology-driven approaches for advancing BSSR. By synthesizing early experiences, emerging practices, and shared challenges across multiple projects, it offers insights to inform future ontology development, interoperability, and community consensus while laying the foundation for more systematic evaluation and broader adoption within the BSSR community.

Yue Yu, Lu Kang, Susan Michie et al. · 0 citations
#large language models Review Open access Sep 2026

Generative large language models in medicine: a scoping review of recent methodological advances

Generative large language models (LLMs) are rapidly transforming medicine, demonstrating unprecedented capability across a broad spectrum of clinical and biomedical tasks. While prior literature has extensively investigated their applications, the methodological foundation underpinning these models remains comparatively underexamined. In this review, we provide a mechanically grounded analysis of recent methodological advances shaping the development and deployment of generative LLMs in healthcare. We categorize the technical landscape into three principal pillars: pretraining, fine-tuning, and prompt engineering, and examine their key architectures, subtypes, and adaptation strategies based on literature published between 2023 and 2025. We further discuss the emerging directions, including efficient model infrastructures and LLMs-powered multi-agent systems, alongside critical challenges related to bias, generalization, and evaluation. By tracing the evolutionary trajectories of these methodologies, this scoping review provides a mechanism-centered framework to inform responsible model development and deployment in medical settings, tailored to task complexity, data characteristics, and resource constraints.

Fang Li, Jianfu Li, Weiguo Cao et al. · 0 citations

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