ArticleNature medicine2019
Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence.
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Article in Nature medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 247 papers, 5 of them syntheses that pooled it.
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247citing papers in PubMed, 5 pooled it
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
247 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.Acta neurochirurgica. Supplement · 2022Pooled it
- Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Does Artificial Intelligence Outperform Natural Intelligence in Interpreting Musculoskeletal Radiological Studies? A Systematic Review.Clinical orthopaedics and related research · 2020Pooled it
- Development and validation of a machine learning-driven framework for differentiating pediatric bronchopneumonia from lobar pneumonia: a multicenter investigation.European journal of medical research · 2025Trial
- Prediction of maternal and infant outcomes from longitudinal electronic health records with a mother-child AI agent.Nature medicine · 2026Article
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges.Methods and protocols · 2026Article
- Empowering AI data scientists using a multi-agent LLM framework with self-evolving capabilities for autonomous, tool-aware biomedical data analyses.Nature biomedical engineering · 2026Article
- AI in pediatric surgery: a narrative review.Translational pediatrics · 2026Review
- AI-powered surveillance of bronchiolitis in the Nirsevimab era: comparative performance of machine learning, deep learning, and large language models on free-text ED records.BMC emergency medicine · 2026Article
- The Use of Artificial Intelligence in Improving Diagnostic Modalities in Rheumatoid Arthritis: A Narrative Review.Cureus · 2026Review
- First-line risk stratification with machine learning models facilitates rapid triage for non-ST-elevation myocardial infarction.PLOS digital health · 2026Article
- Large Language Models for Psychiatric Diagnosis Based on Multicenter Real-World Clinical Records: Comparative Study.JMIR medical informatics · 2026Article
- The Evolving Role of Artificial Intelligence in Pediatric Asthma Management: Opportunities and Challenges for Modern Healthcare.Journal of personalized medicine · 2026Review
- Harnessing multi-omics and machine learning for predicting immune checkpoint blockade responses: Advances, challenges, and future directions.Fundamental research · 2026Review
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Promoting Responsible Use of AI in African Healthcare: Strengthening Patients' Moral Agency.Asian bioethics review · 2026Article
- Application of artificial intelligence to measure and predict patient values and preferences: a scoping review.NPJ digital medicine · 2025Article
187 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
71 authors.
Huiying LiangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Brian Y TsuiInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0001-8017-5895
Hao NiHangzhou YITU Healthcare Technology Co. Ltd, Hangzhou, China.
Carolina C S ValentimDepartment of Thoracic Surgery/Oncology, First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory and National Clinical Research Center for Respiratory Disease, Guangzhou, China.
Sally L BaxterInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0002-5271-7690
Guangjian LiuGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Wenjia CaiInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0003-2398-1449
Daniel S KermanyGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Xin SunGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Jiancong ChenInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Liya HeGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Jie ZhuGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Pin TianInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Hua ShaoInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Lianghong ZhengGuangzhou Kangrui Co. Ltd, Guangzhou, China.
Rui HouGuangzhou Kangrui Co. Ltd, Guangzhou, China.
Sierra HewettGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Gen LiGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Ping LiangHangzhou YITU Healthcare Technology Co. Ltd, Hangzhou, China.
Xuan ZangHangzhou YITU Healthcare Technology Co. Ltd, Hangzhou, China.
Zhiqi ZhangHangzhou YITU Healthcare Technology Co. Ltd, Hangzhou, China.
Liyan PanGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Huimin CaiGuangzhou Kangrui Co. Ltd, Guangzhou, China.
Rujuan LingGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Shuhua LiGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Yongwang CuiGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Shusheng TangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Hong YeGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Xiaoyan HuangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Waner HeGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Wenqing LiangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Qing ZhangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Jianmin JiangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Wei YuGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Jianqun GaoGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Wanxing OuGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Yingmin DengGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Qiaozhen HouGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Bei WangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Cuichan YaoGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Yan LiangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Shu ZhangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Yaou DuanInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Runze ZhangInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Sarah GibsonInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Charlotte L ZhangInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Oulan LiInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Edward D ZhangInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Gabriel KarinInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Nathan NguyenInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Xiaokang WuGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Cindy WenInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Jie XuInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Wenqin XuInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Bochu WangInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Winston WangInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Jing LiGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Bianca PizzatoInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Caroline BaoInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Daoman XiangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Wanting HeGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Suiqin HeInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Yugui ZhouGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Weldon HawInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Michael GoldbaumInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Adriana TremouletInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Chun-Nan HsuInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0002-5240-4707
Hannah CarterInstitute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
Long ZhuHangzhou YITU Healthcare Technology Co. Ltd, Hangzhou, China.
Kang ZhangGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China. kang.zhang@gmail.com.ORCID http://orcid.org/0000-0002-4549-1697
Huimin XiaGuangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China. xiahumin@hotmail.com.ORCID http://orcid.org/0000-0002-3714-3764
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal.
Indexed as
Deep LearningDiagnosis, Computer-AssistedElectronic Health RecordsNatural Language ProcessingPediatricsAdolescentArtificial IntelligenceChildChild, PreschoolChinaFemaleHumansInfantInfant, NewbornMachine LearningMale
Identifiers
PMID
30742121What OpenQuestion holds
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.