Health Information Technology Commons^™

Memoryart: Enhancing Llms Via Multi-Memory Models With Adaptive Resonance Theory For Healthcare Agents, Renke Dai, Hebin Hu, Jiahui Zhang, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Though promising in healthcare consultation applications, large language models (LLMs) face critical limitations in retaining and utilizing long-term memory across multiturn interactions. In particular, existing memory enhancing paradigms are constrained by limited context windows and embedding-based retrieval, often failing to maintain task relevance and still suffering from memory prototype collapse in multi-turn healthcare consultation. To address these challenges, we propose a cognitively-inspired memory framework named MemoryART, which is grounded in Adaptive Resonance Theory (ART)—a cognitive and learning theory of how humans and animals adapt to dynamic environments. MemoryART employs three memory modules—working memory, episodic memory, and semantic memory to support …

Robust And Interpretable Medical Image Segmentation Via Retrieval And Mechanistic Analysis, Salma Ahmed

Theses and Dissertations (Comprehensive)

Medical image segmentation is a critical component of clinical decision-making, yet deep learning based segmentation models face persistent challenges. These models typically require large volumes of densely annotated data, exhibit strong sensitivity to domain shifts across scanners and patient populations, and lack explicit anatomical priors. Furthermore, despite continual architectural advances beyond fully convolutional networks, most segmentation models remain opaque, where they lack the granularity required for component-level analysis. As a result, when segmentation models fail, it is difficult to identify which internal representations are responsible, limiting developers’ ability to diagnose errors, improve robustness, or establish trust in clinical settings. Existing …

Adjunctive Technologies To Electronic Fetal Monitoring: Promise, Pitfalls, And Lessons Learned, Rebecca Horgan, George Saade

Department of Obstetrics & Gynecology Faculty Publications

Despite widespread use, electronic fetal monitoring (EFM) has not demonstrably reduced perinatal morbidity or mortality, and likely contributed to the rise in cesarean delivery. To improve its utility, a number of adjunctive approaches have been introduced over the past three decades, including fetal pulse oximetry, ST segment analysis (STAN), and maternal oxygen supplementation. Each intervention was supported by strong physiologic rationale, yet none achieved widespread clinical adoption due to inconsistent evidence of benefit and, in some cases, potential harm. This review critically evaluates landmark studies on EFM adjuncts, highlights the limitations that have impeded progress, and outlines emerging innovations, including …

January 2026, Swosu Rural Health Center

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