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Research Paper#Artificial Intelligence in Healthcare, Large Language Models, Clinical Diagnosis🔬 ResearchAnalyzed: Jan 3, 2026 15:48

MedKGI: Improving LLMs for Clinical Diagnosis

Published:Dec 30, 2025 12:31
1 min read
ArXiv

Analysis

This paper addresses the limitations of Large Language Models (LLMs) in clinical diagnosis by proposing MedKGI. It tackles issues like hallucination, inefficient questioning, and lack of coherence in multi-turn dialogues. The integration of a medical knowledge graph, information-gain-based question selection, and a structured state for evidence tracking are key innovations. The paper's significance lies in its potential to improve the accuracy and efficiency of AI-driven diagnostic tools, making them more aligned with real-world clinical practices.
Reference

“MedKGI improves dialogue efficiency by 30% on average while maintaining state-of-the-art accuracy.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 28, 2025 08:31

Recreating Palantir's "Ontology" with Python

Published:Dec 28, 2025 08:20
1 min read
Qiita LLM

Analysis

This article discusses the implementation of an ontology, similar to Palantir Foundry's, using Python. It addresses the practical application of the ontological concepts previously discussed, moving beyond theoretical understanding to actual implementation. The article likely provides code examples and demonstrates the output of such an implementation. The value lies in bridging the gap between understanding the concept of an ontology and knowing how to build one in a practical setting. It caters to readers who are interested in the hands-on aspects of AI data infrastructure and want to explore how to leverage Python for building ontologies.
Reference

“「概念はわかった。で、どう実装して、どんなアウトプットになるの？」”

PermalinkQiita LLM
Paper#AI in Healthcare🔬 ResearchAnalyzed: Jan 3, 2026 16:36

MMCTOP: Multimodal AI for Clinical Trial Outcome Prediction

Published:Dec 26, 2025 06:56
1 min read
ArXiv

Analysis

This paper introduces MMCTOP, a novel framework for predicting clinical trial outcomes by integrating diverse biomedical data types. The use of schema-guided textualization, modality-aware representation learning, and a Mixture-of-Experts (SMoE) architecture is a significant contribution to the field. The focus on interpretability and calibrated probabilities is crucial for real-world applications in healthcare. The consistent performance improvements over baselines and the ablation studies demonstrating the impact of key components highlight the framework's effectiveness.
Reference

“MMCTOP achieves consistent improvements in precision, F1, and AUC over unimodal and multimodal baselines on benchmark datasets, and ablations show that schema-guided textualization and selective expert routing contribute materially to performance and stability.”

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Research#Ontology🔬 ResearchAnalyzed: Jan 10, 2026 11:34

Leveraging Wikidata's Structure: A Multi-Axial Approach to Ontology Design

Published:Dec 13, 2025 09:59
1 min read
ArXiv

Analysis

This ArXiv article explores the lessons learned from Wikidata's polyhierarchical structure for designing ontologies, emphasizing a multi-axial mindset. This approach could significantly improve the flexibility and expressiveness of knowledge representation in AI.
Reference

“The article analyzes Wikidata's polyhierarchical structure.”

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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:59

Model management to support systems engineering workflows using ontology-based knowledge graphs

Published:Dec 10, 2025 12:45
1 min read
ArXiv

Analysis

This article likely discusses the application of knowledge graphs and ontologies to improve the management and efficiency of systems engineering processes. The focus is on how these technologies can be used to model and manage complex systems, potentially improving collaboration, traceability, and overall system design.
Reference

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Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 13:07

Improving Clinical Note Generation with GPT-4: Leveraging ICD-10, Knowledge Graphs, and Chain-of-Thought

Published:Dec 4, 2025 21:12
1 min read
ArXiv

Analysis

This research explores a practical application of GPT-4 in healthcare, focusing on the crucial task of clinical note generation. The integration of ICD-10 codes, clinical ontologies, and chain-of-thought prompting offers a promising approach to enhance accuracy and informativeness.
Reference

“The research leverages ICD-10 codes, clinical ontologies, and chain-of-thought prompting.”

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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:13

Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models

Published:Nov 29, 2025 18:44
1 min read
ArXiv

Analysis

This article introduces Wikontic, a method for building knowledge graphs that are aligned with Wikidata and aware of ontologies, using Large Language Models (LLMs). The focus is on integrating LLMs to improve the construction process and ensure semantic consistency with existing knowledge bases. The research likely explores the challenges and benefits of using LLMs for this specific task, such as handling complex relationships and ensuring data accuracy.
Reference

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PermalinkArXiv