To optimize in-context learning in biomedical natural language processing by improving example selection.
Author(s): Zhan, Zaifu, Wang, Jun, Zhou, Shuang, Deng, Jiawen, Zhang, Rui
DOI: 10.1093/jamia/ocaf128
This article evaluates the privacy policies of Artificial Intelligence (AI)-powered mHealth apps, focusing on their availability, readability, transparency, and scope.
Author(s): Javed, Yousra, Bhojanam, Saaketh
DOI: 10.1093/jamia/ocaf130
This quality improvement study implemented and prospectively examined user engagement with an artificial intelligence (AI)-powered clinical trial knowledge management application at an NCI-designated comprehensive cancer center.
Author(s): Hung, Tony K W, Mao, Jun J, Ho, Alan L, Sherman, Eric J, Robson, Mark, Park, Jae, Stein, Eytan M, Kuperman, Gilad J, Pfister, David G
DOI: 10.1093/jamia/ocaf129
Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for prognostication and informing targeted interventions. Clinical notes often contain details about SBDH in unstructured format. Conventional extraction methods for these data tend to be labor intensive, inaccurate, and/or unscalable. In this study, we aim to develop and validate a large language model (LLM)-powered method to extract structured SBDH data from clinical notes through prompt engineering.
Author(s): Gu, Zifan, He, Lesi, Naeem, Awais, Chan, Pui Man, Mohamed, Asim, Khalil, Hafsa, Guo, Yujia, Huang, Jingwei, Villanueva-Miranda, Ismael, Ding, Ying, Shi, Wenqi, Dupre, Matthew E, Xiao, Guanghua, Peterson, Eric D, Xie, Yang, Navar, Ann Marie, Yang, Donghan M
DOI: 10.1093/jamia/ocaf124
The success of artificial intelligence (AI) and machine learning (ML) approaches in biomedical research depends on the quality of the underlying data. The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Data Centric Challenge was designed to address the challenge of making raw clinical research data AI ready, with a focus on type 1 diabetes studies available in the NIDDK Central Repository (NIDDK-CR). This paper aims to present [...]
Author(s): Domagalski, Marcin J, Lu, Yin, Pilozzi, Alexander, Williamson, Alicia, Chilappagari, Padmini, Luker, Emma, Shelley, Courtney D, Dabic, Anya, Keller, Michael A, Rodriguez, Rebecca M, Lawlor, Sharon, Thangudu, Ratna R
DOI: 10.1093/jamia/ocaf114
To conduct an independent external validation of an implemented vendor-developed emergency department (ED) pediatric sepsis predictive model.
Author(s): Kandaswamy, Swaminathan, Orenstein, Evan W, Muthu, Naveen, McCarter, Andrea, Braykov, Nikolay, Beus, Jonathan M, Ray, Edwin, Senior, Tal, Brown, Sara P, Carter, Rebekah, Gleeson, MaryBeth, Thummel, Hannah, Cheng, John, Bui, Thuy, Blanco, Reena, Hebbar, Kiran, Fortenberry, James, Iyer, Srikant B, Mai, Mark V
DOI: 10.1093/jamia/ocaf105
To assess the influence of an implemented artificial intelligence model predicting pediatric sepsis (defined by IPSO-Improving Pediatric Sepsis Outcomes collaborative) in the emergency department (ED) on human performance measures.
Author(s): Kandaswamy, Swaminathan, Muthu, Naveen, Braykov, Nikolay, Carter, Rebekah, Blanco, Reena, Bui, Thuy, Orenstein, Evan, Mai, Mark
DOI: 10.1093/jamia/ocaf106
This work incorporates the Digital Imaging Communications in Medicine (DICOM) Standard into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) to standardize and accurately represent imaging studies, such as acquisition parameters, in multimodal research studies.
Author(s): Park, Woo Yeon, Sippel Schmidt, Teri, Salvador, Gabriel, O'Donnell, Kevin, Genereaux, Brad, Jeon, Kyulee, You, Seng Chan, Dewey, Blake E, Nagy, Paul, ,
DOI: 10.1093/jamia/ocaf091
This review paper comprehensively summarizes healthcare provider (HCP) evaluation of explanations produced by explainable artificial intelligence methods to support point-of-care, patient-specific, clinical decision-making (CDM) within medical settings. It highlights the critical need to incorporate human-centered (HCP) evaluation approaches based on their CDM needs, processes, and goals.
Author(s): Bauer, Jenny M, Michalowski, Martin
DOI: 10.1093/jamia/ocaf110
This paper presents the results from a competition challenging participants to develop entity linking models using a subset of annotated MIMIC-IV-Note data and the SNOMED CT Terminology.
Author(s): Davidson, Rory, Hardman, Will, Amit, Guy, Bilu, Yonatan, Della Mea, Vincenzo, Galaida, Aleksandr, Girshovitz, Irena, Kulyabin, Mikhail, Horia Popescu, Mihai, Roitero, Kevin, Sokolov, Gleb, Yanover, Chen
DOI: 10.1093/jamia/ocaf104