Posts Tagged ‘Machine Vision’
Local large language models for privacy-preserving accelerated review of historic echocardiogram reports.
Link: https://doi.org/10.1093/jamia/ocae085 Authors: Vaid, Akhil; Duong, Son Q; Lampert, Joshua; Kovatch, Patricia; Freeman, Robert; Argulian, Edgar; Croft, Lori; Lerakis, Stamatios; Goldman, Martin; Khera, Rohan; Nadkarni, Girish N Abstract: The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient’s entire echocardiogram report history. This…
Read MoreExtracting social support and social isolation information from clinical psychiatry notes: comparing a rule-based natural language processing system and a large language model.
Link: https://doi.org/10.1093/jamia/ocae260 Authors: Patra, Braja Gopal; Lepow, Lauren A; Kasi Reddy Jagadeesh Kumar, Praneet; Vekaria, Veer; Sharma, Mohit Manoj; Adekkanattu, Prakash; Fennessy, Brian; Hynes, Gavin; Landi, Isotta; Sanchez-Ruiz, Jorge A; Ryu, Euijung; Biernacka, Joanna M; Nadkarni, Girish N; Talati, Ardesheer; Weissman, Myrna; Olfson, Mark; Mann, J John; Zhang, Yiye; Charney, Alexander W; Pathak, Jyotishman Abstract:…
Read MoreDeep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease.
Link: https://doi.org/S0735-1097(24)07676-9 Authors: Mayourian, Joshua; Gearhart, Addison; La Cava, William G; Vaid, Akhil; Nadkarni, Girish N; Triedman, John K; Powell, Andrew J; Wald, Rachel M; Valente, Anne Marie; Geva, Tal; Duong, Son Q; Ghelani, Sunil J Abstract: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital…
Read MoreArtificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study.
Link: https://doi.org/S2589-7500(24)00249-8 Authors: Oikonomou, Evangelos K; Vaid, Akhil; Holste, Gregory; Coppi, Andreas; McNamara, Robert L; Baloescu, Cristiana; Krumholz, Harlan M; Wang, Zhangyang; Apakama, Donald J; Nadkarni, Girish N; Khera, Rohan Abstract: Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We aimed…
Read MoreThe role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.
Link: https://doi.org/10.1007/s00330-024-11261-x Authors: Omar, Mahmud; Watad, Abdulla; McGonagle, Dennis; Soffer, Shelly; Glicksberg, Benjamin S; Nadkarni, Girish N; Klang, Eyal Abstract: Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA…
Read MoreDeep Learning for Contrast Enhanced Mammography – A Systematic Review.
Link: https://doi.org/10.1016/j.acra.2024.11.035 Authors: Sorin, Vera; Sklair-Levy, Miri; Glicksberg, Benjamin S; Konen, Eli; Nadkarni, Girish N; Klang, Eyal Abstract: Contrast-enhanced mammography (CEM) is a relatively novel imaging technique that enables both anatomical and functional breast imaging, with improved diagnostic performance compared to standard 2D mammography. The aim of this study is to systematically review the literature…
Read MoreExtracting International Classification of Diseases Codes from Clinical Documentation using Large Language Models.
Link: https://doi.org/10.1055/a-2491-3872 Authors: Simmons, Ashley; Takkavatakarn, Kullaya; McDougal, Megan; Dilcher, Brian; Pincavitch, Jami; Meadows, Lukas; Kauffman, Justin; Klang, Eyal; Wig, Rebecca; Smith, Gordon Stephen; Soroush, Ali; Freeman, Robert; Apakama, Donald; Charney, Alexander; Kohli-Seth, Roopa; Nadkarni, Girish; Sakhuja, Ankit Abstract: Large language models (LLMs) have shown promise in various professional fields, including medicine and law. However,…
Read MoreBenchmarking Large Language Models for Extraction of International Classification of Diseases Codes from Clinical Documentation.
Link: https://doi.org/10.1101/2024.04.29.24306573 Authors: Simmons, Ashley; Takkavatakarn, Kullaya; McDougal, Megan; Dilcher, Brian; Pincavitch, Jami; Meadows, Lukas; Kauffman, Justin; Klang, Eyal; Wig, Rebecca; Smith, Gordon; Soroush, Ali; Freeman, Robert; Apakama, Donald J; Charney, Alexander W; Kohli-Seth, Roopa; Nadkarni, Girish N; Sakhuja, Ankit Abstract: Healthcare reimbursement and coding is dependent on accurate extraction of International Classification of Diseases-tenth…
Read MoreDetection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.
Link: https://doi.org/10.1016/j.eclinm.2024.102919 Authors: Gleason, Alec; Richter, Florian; Beller, Nathalia; Arivazhagan, Naveen; Feng, Rui; Holmes, Emma; Glicksberg, Benjamin S; Morton, Sarah U; La Vega-Talbott, Maite; Fields, Madeline; Guttmann, Katherine; Nadkarni, Girish N; Richter, Felix Abstract: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective.…
Read MoreLeveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review.
Link: https://doi.org/10.2196/57124 Authors: Abbott, Ethan E; Apakama, Donald; Richardson, Lynne D; Chan, Lili; Nadkarni, Girish N Abstract: Social determinants of health (SDOH) are critical drivers of health disparities and patient outcomes. However, accessing and collecting patient-level SDOH data can be operationally challenging in the emergency department (ED) clinical setting, requiring innovative approaches. This scoping review…
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