Correction to: Evaluating the ChatGPT family of models for biomedical reasoning and classification.
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DOI: 10.1093/jamia/ocae083
Author(s):
DOI: 10.1093/jamia/ocae083
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DOI: 10.1093/jamia/ocae048
We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for clinical decision support. We describe the pre-implementation evaluation process that considered model performance, fairness, and clinical appropriateness.
Author(s): Liu, Yifan, Joly, Rochelle, Reading Turchioe, Meghan, Benda, Natalie, Hermann, Alison, Beecy, Ashley, Pathak, Jyotishman, Zhang, Yiye
DOI: 10.1093/jamia/ocae056
Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications.
Author(s): Javidi, Hamed, Mariam, Arshiya, Alkhaled, Lina, Pantalone, Kevin M, Rotroff, Daniel M
DOI: 10.1093/jamia/ocae049
Genomic kidney conditions often have a long lag between onset of symptoms and diagnosis. To design a real time genetic diagnosis process that meets the needs of nephrologists, we need to understand the current state, barriers, and facilitators nephrologists and other clinicians who treat kidney conditions experience, and identify areas of opportunity for improvement and innovation.
Author(s): Romagnoli, Katrina M, Salvati, Zachary M, Johnson, Darren K, Ramey, Heather M, Chang, Alexander R, Williams, Marc S
DOI: 10.1093/jamia/ocae053
Large-language models (LLMs) can potentially revolutionize health care delivery and research, but risk propagating existing biases or introducing new ones. In epilepsy, social determinants of health are associated with disparities in care access, but their impact on seizure outcomes among those with access remains unclear. Here we (1) evaluated our validated, epilepsy-specific LLM for intrinsic bias, and (2) used LLM-extracted seizure outcomes to determine if different demographic groups have different [...]
Author(s): Xie, Kevin, Ojemann, William K S, Gallagher, Ryan S, Shinohara, Russell T, Lucas, Alfredo, Hill, Chloé E, Hamilton, Roy H, Johnson, Kevin B, Roth, Dan, Litt, Brian, Ellis, Colin A
DOI: 10.1093/jamia/ocae047
This article aims to examine how generative artificial intelligence (AI) can be adopted with the most value in health systems, in response to the Executive Order on AI.
Author(s): Jindal, Jenelle A, Lungren, Matthew P, Shah, Nigam H
DOI: 10.1093/jamia/ocae043
To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts.
Author(s): Liu, Siru, McCoy, Allison B, Wright, Aileen P, Nelson, Scott D, Huang, Sean S, Ahmad, Hasan B, Carro, Sabrina E, Franklin, Jacob, Brogan, James, Wright, Adam
DOI: 10.1093/jamia/ocae041
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocae077
This article presents the National Healthcare Safety Network (NHSN)'s approach to automation for public health surveillance using digital quality measures (dQMs) via an open-source tool (NHSNLink) and piloting of this approach using real-world data in a newly established collaborative program (NHSNCoLab). The approach leverages Health Level Seven Fast Healthcare Interoperability Resources (FHIR) application programming interfaces to improve data collection and reporting for public health and patient safety beginning with common [...]
Author(s): Shehab, Nadine, Alschuler, Liora, McILvenna, Sean, Gonzaga, Zabrina, Laing, Andrew, deRoode, David, Dantes, Raymund B, Betz, Kristina, Zheng, Shuai, Abner, Sheila, Stutler, Elizabeth, Geimer, Rick, Benin, Andrea L
DOI: 10.1093/jamia/ocae064