Paper List
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Discovery of a Hematopoietic Manifold in scGPT Yields a Method for Extracting Performant Algorithms from Biological Foundation Model Internals
This work addresses the core challenge of extracting reusable, interpretable, and high-performance biological algorithms from the opaque internal repr...
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MS2MetGAN: Latent-space adversarial training for metabolite–spectrum matching in MS/MS database search
This paper addresses the critical bottleneck in metabolite identification: the generation of high-quality negative training samples that are structura...
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Toward Robust, Reproducible, and Widely Accessible Intracranial Language Brain-Computer Interfaces: A Comprehensive Review of Neural Mechanisms, Hardware, Algorithms, Evaluation, Clinical Pathways and Future Directions
This review addresses the core challenge of fragmented and heterogeneous evidence that hinders the clinical translation of intracranial language BCIs,...
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Less Is More in Chemotherapy of Breast Cancer
通过纳入细胞周期时滞和竞争项，解决了现有肿瘤-免疫模型的过度简化问题，以定量比较化疗方案。
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Fold-CP: A Context Parallelism Framework for Biomolecular Modeling
This paper addresses the critical bottleneck of GPU memory limitations that restrict AlphaFold 3-like models to processing only a few thousand residue...
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Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database
This paper addresses the core pain point of fragmented biomedical data by constructing and federating large-scale, open knowledge graphs to enable sea...
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Predictive Analytics for Foot Ulcers Using Time-Series Temperature and Pressure Data
This paper addresses the critical need for continuous, real-time monitoring of diabetic foot health by developing an unsupervised anomaly detection fr...
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Hypothesis-Based Particle Detection for Accurate Nanoparticle Counting and Digital Diagnostics
This paper addresses the core challenge of achieving accurate, interpretable, and training-free nanoparticle counting in digital diagnostic assays, wh...
An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
Georgia Institute of Technology, Atlanta, GA, USA | Shriners Hospitals for Children, Tampa, FL, USA
30秒速读
IN SHORT: This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for assessing and improving healthcare data quality using trustworthy AI principles.
核心创新
- Methodology Developed a Python-based extension of OHDSI's Data Quality Dashboard (DQD) that integrates the METRIC framework for trustworthy AI assessment, addressing informative missingness, timeliness, and distribution consistency.
- Methodology Implemented a real-world case study modernizing a large pediatric healthcare system's Research Data Warehouse from OMOP CDM v5.1/5.2 to v5.4 within Microsoft Fabric, achieving 4% improvement in data quality test success rate (84.78% to 88.88%).
- Biology Demonstrated that data harmonization using OMOP CDM concept codes does not significantly impact AI model performance (mean AUROC: 71.3% with source codes vs. 70.0% with OMOP codes) while increasing interoperability for Craniofacial Microsomia case study.
主要结论
- Modernizing SC's OMOP CDM database from v5.1/5.2 to v5.4 improved overall data quality by 4% (84.78% to 88.88% success rate) and conformance by 8% (80.73% to 88.09%).
- Data harmonization using OMOP CDM concept codes maintained comparable AI model performance (mean AUROC difference: 1.3%) while enabling better interoperability across healthcare systems.
- Only 50% of ICD-9 codes shared common mappings with ICD-10 codes, revealing significant vocabulary transition challenges that could degrade AI model performance when encountering mixed coding systems.
摘要: The rapid growth of Artificial Intelligence (AI) in healthcare has sparked interest in Trustworthy AI and AI Implementation Science, both of which are essential for accelerating clinical adoption. Yet, barriers such as strict regulations, gaps between research and clinical settings, and challenges in evaluating AI systems hinder real-world implementation. This study presents an AI implementation case study within Shriners Children’s (SC), a large multisite pediatric system, showcasing the modernization of SC’s Research Data Warehouse (RDW) to OMOP CDM v5.4 within a secure Microsoft Fabric environment. We introduce a Python-based data quality assessment tool compatible with SC’s infrastructure, an extension of OHDSI’s R/Java-based Data Quality Dashboard (DQD) that integrates Trustworthy AI principles using the METRIC framework. This extension enhances data quality evaluation by addressing informative missingness, redundancy, timeliness, and distributional consistency. We also compare systematic and case-specific AI implementation strategies for Craniofacial Microsomia (CFM) using the FHIR standard. Our contributions include a real-world evaluation of AI implementations, integration of Trustworthy AI in data quality assessment, and evidence-based insights into hybrid implementation strategies, highlighting the need to blend systematic infrastructure with use-case-driven approaches to advance AI in healthcare.