Objective To evaluate clinical outcomes, patient satisfaction, and maintenance after treatment from mandibular implant-supported overdentures with different attachment types. Methods We searched six electronic databases: the Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library, Issue 4, 2005), Current Controlled Trials, MEDLINE (1966 to Sept. 2005), EMBASE (1984 to Sept. 2005), and Chinese biomedical database disk (1978 to Sept. 2005). Eleven Chinese professional journals in oral health were also handsearched from their first published issues. Three authors screened and selected the studies, appraised their methodological quality and extracted data from the studies. The results were presented narratively by meta-analysis. Results After strict screening, 12 trials involving 282 patients were included. Two of the trials were included in a meta-analysis with 27 patients in bar-clip group, 29 patients in ball-spring group, and the other trials were described thoroughly. The findings seemed to indicate that the bar-clip group had the highest retention but more oral mucosa complications, while the ball-spring group had good retention and less oral mucosa complications but needed more aftercare treatments, and the magnetic group had less retention but better peri-implant outcomes. Conclusions There is inadequate evidence to prove which is the best choice for mandibular implant-supported overdentures among bar-clip, ball-spring and magnetic attachments. More controlled clinical trials are required to guide clinicians on the choice of the type of attachment in mandibular implant-supported overdenture.
Systematic reviews and meta-analyses are essential methods in evidence-based medicine for integrating research evidence and guiding clinical decision-making. However, with the rapid expansion of medical research data, traditional approaches face significant challenges in terms of efficiency, accuracy, and reliability. In recent years, the rapid advancement of artificial intelligence (AI) technologies, particularly in natural language processing (NLP), machine learning (ML), and large language models (LLMs), has provided robust support for automating and intelligentizing systematic reviews and meta-analyses. This paper systematically reviews the progress of AI applications in these fields, tracing the evolution from traditional tools to intelligent platforms, and analyzes the functional characteristics, application scenarios, and limitations of existing AI-driven tools. Furthermore, it explores the challenges posed by AI in terms of adaptation to the medical field, multimodal data processing, and ethical transparency, while offering potential solutions and optimization strategies. Looking ahead, with the continuous optimization of technology, enhanced data sharing, and the establishment of industry standards, AI is expected to significantly improve the efficiency and quality of systematic reviews and meta-analyses, driving the transition from "tool-driven" to "intelligent collaboration." The deep integration of AI not only injects innovative momentum into evidence-based medicine but also reshapes its methodological foundation, laying a solid basis for a more intelligent, equitable, and efficient future.
