Aerts, Hugo J. W. L.

Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients

Busch, F., Hoffmann, L., Xu, L., Zhang, L. J., Hu, B., García-Juárez, I., Toapanta-Yanchapaxi, L. N., Gorelik, N., Gorelik, V., Rodriguez-Granillo, G. A., et al. (2025) · JAMA Network Open

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 124

Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model

Lu, M. T., Raghu, V. K., Mayrhofer, T., Aerts, H. J. W. L., & Hoffmann, U. (2020) · Annals of Internal Medicine

Lung Cancer Diagnosis and TreatmentAI in cancer detection

Cited by 123

A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen

Rios Velazquez, E., Aerts, H. J. W. L., Gu, Y., Goldgof, D. B., De Ruysscher, D., Dekker, A., Korn, R., Gillies, R. J., & Lambin, P. (2012) · Radiotherapy and Oncology

Medical Image Segmentation TechniquesLung Cancer Diagnosis and Treatment

Cited by 120

When helpfulness backfires: LLMs and the risk of false medical information due to sycophantic behavior

Chen, S., Gao, M., Sasse, K., Hartvigsen, T., Anthony, B., Fan, L., Aerts, H., Gallifant, J., & Bitterman, D. S. (2025) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 112

Approaching autonomy in medical artificial intelligence

Bitterman, D. S., Aerts, H. J. W. L., & Mak, R. H. (2020) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 107

Deep Learning to Estimate Biological Age From Chest Radiographs

Raghu, V. K., Weiss, J., Hoffmann, U., Aerts, H. J. W. L., & Lu, M. T. (2021) · JACC: Cardiovascular Imaging

Radiomics and Machine Learning in Medical ImagingForensic Anthropology and Bioarchaeology Studies

Cited by 101

Evaluating the ChatGPT family of models for biomedical reasoning and classification

Chen, S., Li, Y., Lu, S., Van, H., Aerts, H. J. W. L., Savova, G. K., & Bitterman, D. S. (2024) · Journal of the American Medical Informatics Association

Topic ModelingBiomedical Text Mining and Ontologies

Cited by 91

Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification

Chen, S., Li, Y., Lu, S., Van, H., Aerts, H. J., Savova, G. K., & Bitterman, D. S. (2023) · arXiv

Topic ModelingBiomedical Text Mining and Ontologies

Cited by 91

Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs

Weiss, J., Raghu, V. K., Paruchuri, K., Zinzuwadia, A., Natarajan, P., Aerts, H. J. W. L., & Lu, M. T. (2024) · Annals of Internal Medicine

Health Promotion and Cardiovascular PreventionCardiac Imaging and Diagnostics

Cited by 50

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice

Bakas, S., Vollmuth, P., Galldiks, N., Booth, T. C., Aerts, H. J. W. L., Bi, W. L., Wiestler, B., Tiwari, P., Pati, S., Baid, U., et al. (2024) · The Lancet Oncology

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Cited by 36

National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence

Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., et al. (2023) · Radiographics

Artificial Intelligence in Healthcare and EducationAI in cancer detection

Cited by 35

Natural Language Processing to Automatically Extract the Presence and Severity of Esophagitis in Notes of Patients Undergoing Radiotherapy

Chen, S., Guevara, M., Ramirez, N., Murray, A., Warner, J. L., Aerts, H. J. W. L., Miller, T. A., Savova, G. K., Mak, R. H., & Bitterman, D. S. (2023) · JCO Clinical Cancer Informatics

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 20

Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks

Gallifant, J., Chen, S., Moreira, P. J. F., Munch, N., Gao, M., Pond, J., Celi, L. A., Aerts, H., Hartvigsen, T., & Bitterman, D. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024

Topic ModelingArtificial Intelligence in Healthcare and Education

Cited by 19

Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks

Gallifant, J., Chen, S., Moreira, P., Munch, N., Gao, M., Pond, J., Celi, L. A., Aerts, H., Hartvigsen, T., & Bitterman, D. (2024) · arXiv

Topic ModelingArtificial Intelligence in Healthcare and Education

Cited by 19

LongHealth: A Question Answering Benchmark with Long Clinical Documents

Adams, L., Busch, F., Han, T., Excoffier, J.-B., Ortala, M., Löser, A., Aerts, H. J., Kather, J. N., Truhn, D., & Bressem, K. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 10

MedBrowseComp: Benchmarking Medical Deep Research and Computer Use

Chen, S., Moreira, P., Xiao, Y., Schmidgall, S., Warner, J., Aerts, H., Hartvigsen, T., Gallifant, J., & Bitterman, D. S. (2025) · arXiv

Artificial Intelligence in Healthcare and EducationInformation Retrieval and Search Behavior

Cited by 0