7.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Mayo Clinic in FloridaUS31 papers
- Mayo ClinicUS27 papers
- Arizona State UniversityUS16 papers
- WinnMedUS10 papers
- University of Illinois Urbana-ChampaignUS9 papers
- Emory UniversityUS8 papers
- University of PennsylvaniaUS8 papers
- Harvard UniversityUS7 papers
- Cornell UniversityUS6 papers
- National Institutes of HealthUS6 papers
- Yale UniversityUS6 papers
- Indiana University – Purdue University IndianapolisUS5 papers
86 papers
SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
Xinrui He, Mengting Ai, Junting Wang +2
Clinical care often relies on key laboratory indicators, yet real-world patient visits are sparse and tests are ordered irregularly, leading to pervasive missingness. While many im…
Accuracy, Repeatability, and Reproducibility of a Radiographic Technique to Assess Spinal Cord Stimulation Lead Position: A Validation Study
Andrew Thoreson, Katrina Fernandez, Cesar Lopez +4
Spinal cord stimulation with implantable leads is a valuable therapy used to treat a variety of chronic pain conditions. However, lead migration is a common complication causing lo…
Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes
Matthew W. Kenaston, Umair Ayub, Mihir Parmar +14
Despite high performance on clinical benchmarks, large language models may reach correct conclusions through faulty reasoning, a failure mode with safety implications for oncology…
Dynamicasome: a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations
Naeyma N Islam, Mathew A Coban, Jessica M Fuller +6
Advances in genomic medicine accelerate the identi cation of mutations in disease-associated genes, but the pathogenicity of many mutations remains unknown, hindering their use in…
Geometric and Dosimetric Validation of Deformable Image Registration for Prostate MR-guided Adaptive Radiotherapy
Victor N. Malkov, Iymad R. Mansour, Vickie Kong +8
Objective: Quantify geometric and dosimetric accuracy of a novel prostate MR-to-MR deformable image registration (DIR) approach to support MR-guided adaptive radiation therapy dose…
Launching Insights: A Pilot Study on Leveraging Real-World Observational Data from the Mayo Clinic Platform to Advance Clinical Research
Yue Yu, Xinyue Hu, Sivaraman Rajaganapathy +15
Backgrounds: Artificial intelligence (AI) is transforming healthcare, yet translating AI models from theoretical frameworks to real-world clinical applications remains challenging.…
Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes
Saman Khamesian, Asiful Arefeen, Maria Adela Grando +2
Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels and avoid dysglycemia, including hyperglycemia and hypoglycem…
Novel Dark Matter Signatures
A. Argiriou, G. Cantatore, S. A. Cetin +16
Celestial observations often exhibit inexplicable planetary dependencies when the timing of an observable is projected onto planetary heliocentric positions. This is possible only…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
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…
Validation of Subject-Specific Knee Models from In Vivo Measurements
Thor E. Andreassen, Donald R. Hume, Landon D. Hamilton +3
Calibration to experimental data is vital when developing subject-specific models towards developing digital twins. Yet, to date, subject-specific models are largely based on cadav…
A recent evaluation on the performance of LLMs on radiation oncology physics using questions of randomly shuffled options
Peilong Wang, Jason Holmes, Zhengliang Liu +4
Purpose: We present an updated study evaluating the performance of large language models (LLMs) in answering radiation oncology physics questions, focusing on the recently released…
LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation
Xinrui He, Yikun Ban, Jiaru Zou +3
Missing data imputation is a critical challenge in various domains, such as healthcare and finance, where data completeness is vital for accurate analysis. Large language models (L…
Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease
Shovito Barua Soumma, Daniel Peterson, Shyamal Mehta +1
Parkinson's disease (PD) is a progressive neurological disorder that impacts the quality of life significantly, making in-home monitoring of motor symptoms such as Freezing of Gait…
Environment Scan of Generative AI Infrastructure for Clinical and Translational Science
Betina Idnay, Zihan Xu, William G. Adams +54
This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 instituti…
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +77
Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%.…
MNuFFT: A Computationally Efficient Suboptimal Power Spectrum Estimator for Fast Exploration of Nonuniformly Sampled Time Series
Jie Cui, Benjamin H. Brinkmann, Gregory A. Worrell
Nonuniformly sampled signals are prevalent in real-world applications. However, estimating their power spectra from finite samples poses a significant challenge. The optimal soluti…
A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images
Peilong Wang, Timothy L. Kline, Andy D. Missert +6
Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose of this study is to explore the feasibility…
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Dominic LaBella, Ujjwal Baid, Omaditya Khanna +119
We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in…
Accurate Patient Alignment without Unnecessary Imaging Dose via Synthesizing Patient-specific 3D CT Images from 2D kV Images
Yuzhen Ding, Jason M. Holmes, Hongying Feng +8
In radiotherapy, 2D orthogonally projected kV images are used for patient alignment when 3D-on-board imaging(OBI) unavailable. But tumor visibility is constrained due to the projec…
Analysis and Validation of Image Search Engines in Histopathology
Isaiah Lahr, Saghir Alfasly, Peyman Nejat +16
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and d…
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha +3
Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of t…
Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities
Ricardo Gonzalez, Ashirbani Saha, Clinton J. V. Campbell +3
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Syst…
MedYOLO: A Medical Image Object Detection Framework
Joseph Sobek, Jose R. Medina Inojosa, Betsy J. Medina Inojosa +4
Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to…
A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templates
Jingru Fu, Daniel Ferreira, Örjan Smedby +1
Alzheimer's Disease and normal aging are both characterized by brain atrophy. The question of whether AD-related brain atrophy represents accelerated aging or a neurodegeneration p…