Luo, Yuan

Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers

Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023) · npj Digital Medicine

Academic integrity and plagiarismAcademic Writing and Publishing

Cited by 1,099

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022) · npj Digital Medicine

Academic integrity and plagiarismAcademic Writing and Publishing

Cited by 1,099

Deep generative molecular design reshapes drug discovery

Zeng, X., Wang, F., Luo, Y., Kang, S.-G., Tang, J., Lightstone, F. C., Fang, E. F., Cornell, W., Nussinov, R., & Cheng, F. (2022) · Cell Reports Medicine

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 295

An exploratory survey about using ChatGPT in education, healthcare, and research

Hosseini, M., Gao, C. A., Liebovitz, D. M., Carvalho, A. M., Ahmad, F. S., Luo, Y., MacDonald, N., Holmes, K. L., & Kho, A. (2023) · PLOS ONE

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Education

Cited by 238

An exploratory survey about using ChatGPT in education, healthcare, and research

Hosseini, M., Gao, C. A., Liebovitz, D. M., Carvalho, A. M., Ahmad, F. S., Luo, Y., MacDonald, N., Holmes, K. L., & Kho, A. (2023) · PLoS ONE

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Education

Cited by 238

Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review

Luo, Y., Thompson, W. K., Herr, T. M., Zeng, Z., Berendsen, M. A., Jonnalagadda, S. R., Carson, M. B., & Starren, J. (2017) · Drug Safety

Pharmacovigilance and Adverse Drug ReactionsBiomedical Text Mining and Ontologies

Cited by 200

Statistical and machine learning methods for spatially resolved transcriptomics data analysis

Zeng, Z., Li, Y., Li, Y., & Luo, Y. (2022) · Genome Biology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 186

Using Machine Learning to Predict Laboratory Test Results

Luo, Y., Szolovits, P., Dighe, A. S., & Baron, J. M. (2016) · American Journal of Clinical Pathology

Artificial Intelligence in HealthcareClinical Laboratory Practices and Quality Control

Cited by 162

Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer’s disease

Xu, J., Mao, C., Hou, Y., Luo, Y., Binder, J. L., Zhou, Y., Bekris, L. M., Shin, J., Hu, M., Wang, F., et al. (2022) · Cell Reports

Alzheimer's disease research and treatmentsBioinformatics and Genomic Networks

Cited by 115

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Kohane, I. S., Aronow, B. J., Avillach, P., Beaulieu-Jones, B. K., Bellazzi, R., Bradford, R. L., Brat, G. A., Cannataro, M., Cimino, J. J., García-Barrio, N., et al. (2021) · Journal of Medical Internet Research

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 113

Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements

Zimmerman, L. P., Reyfman, P. A., Smith, A. D. R., Zeng, Z., Kho, A., Sanchez-Pinto, L. N., & Luo, Y. (2019) · BMC Medical Informatics and Decision Making

Acute Kidney Injury ResearchMachine Learning in Healthcare

Cited by 100

A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports

Li, Y., Wang, H., & Luo, Y. (2020) · 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 81

A multidimensional precision medicine approach identifies an autism subtype characterized by dyslipidemia

Luo, Y., Eran, A., Palmer, N., Avillach, P., Levy-Moonshine, A., Szolovits, P., & Kohane, I. S. (2020) · Nature Medicine

Autism Spectrum Disorder ResearchGenetics and Neurodevelopmental Disorders

Cited by 78

MedGCN: Medication recommendation and lab test imputation via graph convolutional networks

Mao, C., Yao, L., & Luo, Y. (2022) · Journal of Biomedical Informatics

Machine Learning in HealthcareAdvanced Graph Neural Networks

Cited by 70

AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease

Mao, C., Xu, J., Rasmussen, L., Li, Y., Adekkanattu, P., Pacheco, J., Bonakdarpour, B., Vassar, R., Shen, L., Jiang, G., et al. (2023) · Journal of Biomedical Informatics

Machine Learning in HealthcareDementia and Cognitive Impairment Research

Cited by 67

Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction

Ahmad, F. S., Luo, Y., Wehbe, R. M., Thomas, J. D., & Shah, S. J. (2022) · Heart Failure Clinics

Artificial Intelligence in HealthcareHeart Failure Treatment and Management

Cited by 34

Spatiotemporal localization of proteins in mycobacteria

Zhu, J., Wolf, I. D., Dulberger, C. L., Won, H. I., Kester, J. C., Judd, J. A., Wirth, S. E., Clark, R. R., Li, Y., Luo, Y., et al. (2021) · Cell Reports

Bacterial Genetics and BiotechnologyCell Image Analysis Techniques

Cited by 32

Proactive vs Reactive Machine Learning in Health Care: Lessons From the COVID-19 Pandemic

Luo, Y., Wunderink, R. G., & Lloyd-Jones, D. (2022) · JAMA

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 24

Moving Beyond the Model

Jain, S. S., Mortazavi, B. J., Chan You, S., Yao, X., Lam, C. S. P., Elias, P., Poterucha, T. J., Avram, R., Tison, G. H., Pirruccello, J., et al. (2025) · JACC

Clinical practice guidelines implementationHealth and Medical Research Impacts

Cited by 13

Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs

Adekkanattu, P., Furmanchuk, A., Wu, Y., Pathak, A., Patra, B. G., Bost, S., Morrow, D., Wang, G. H.-M., Yang, Y., Forrest, N. J., et al. (2024) · npj Digital Medicine

Machine Learning in HealthcareMental Health via Writing

Cited by 12

Developing a computable phenotype for identifying children, adolescents, and young adults with diabetes using electronic health records in the DiCAYA Network.

Shao, H., Thorpe, L. E., Islam, S., Bian, J., Guo, Y., Li, P., Bost, S., Dabelea, D., Conway, R., Crume, T., et al. (2025) · Diabetes Care

Machine Learning in HealthcareMedical Coding and Health Information

Cited by 8

ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

Chen, C., Yu, J., Chen, S., Liu, C., Wan, Z., Zhou, S., Luo, Y., Zhang, R., Bitterman, D., Wang, F., et al. (2024) · arXiv

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 5

Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis

Li, Y., Wu, X., Yang, P., Jiang, G., & Luo, Y. (2022) · arXiv

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 2