From the 2 of 7 papers with an AI index.
18 citations
- Himanshu Gupta3 profiles3 · h 10
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- University College LondonGB3 papers
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- German Research Centre for Artificial IntelligenceDE2 papers
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- Imperial College LondonGB2 papers
- Queen Mary University of LondonGB2 papers
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- Universitat Pompeu FabraES2 papers
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- University of ChicagoUS2 papers
7 papers
Artificial intelligence in deep brain stimulation for movement disorders: a systematic review and technology readiness assessment
Zohra Souei, Muhammad Mushhood Ur Rehman, Harith Akram +14
The paper reviews 239 studies on using artificial intelligence for deep brain stimulation in movement disorders, evaluating how mature the technologies are and identifying validati…
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The paper introduces the TopCoW Challenge, a benchmark for automatically segmenting the Circle of Willis in CT and MR angiography using deep learning, and provides a new annotated…
Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework
Sarah de Boer, Hartmut Häntze, Kiran Vaidhya Venkadesh +9
Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an importa…
Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes
Johann Frei, Nils Feldhus, Lisa Raithel +3
For clinical data integration and healthcare services, the HL7 FHIR standard has established itself as a desirable format for interoperability between complex health data. Previous…
Treatment Effect Estimation in Causal Survival Analysis: Practical Recommendations
Charlotte Voinot, Clément Berenfeld, Imke Mayer +2
The restricted mean survival time (RMST) difference offers an interpretable causal contrast to estimate the treatment effect for time-to-event outcomes, yet a wide range of availab…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1144
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…