Gichoya, Judy Wawira

The TRIPOD-LLM reporting guideline for studies using large language models

Gallifant, J., Afshar, M., Ameen, S., Aphinyanaphongs, Y., Chen, S., Cacciamani, G., Demner-Fushman, D., Dligach, D., Daneshjou, R., Fernandes, C., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 615

AI recognition of patient race in medical imaging: a modelling study

Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L.-C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.-C., et al. (2022) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 611

Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study

Zack, T., Lehman, E., Suzgun, M., Rodriguez, J. A., Celi, L. A., Gichoya, J., Jurafsky, D., Szolovits, P., Bates, D. W., Abdulnour, R.-E. E., et al. (2024) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 510

Ethics of large language models in medicine and medical research

Li, H., Moon, J. T., Purkayastha, S., Celi, L. A., Trivedi, H., & Gichoya, J. W. (2023) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 286

The limits of fair medical imaging AI in real-world generalization

Yang, Y., Zhang, H., Gichoya, J. W., Katabi, D., & Ghassemi, M. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 285

AI pitfalls and what not to do: mitigating bias in AI

Gichoya, J. W., Thomas, K., Celi, L. A., Safdar, N., Banerjee, I., Banja, J. D., Seyyed-Kalantari, L., Trivedi, H., & Purkayastha, S. (2023) · The British Journal of Radiology

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 277

Multi-institutional validation of a mammography-based breast cancer risk model

Yala, A., Mikhael, P. G., Strand, F., Lin, G., Satuluru, S., Kim, T., Banerjee, I., Gichoya, J., Trivedi, H., Lehman, C. D., et al. (2022) · Journal of Clinical Oncology

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 204

AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence

Angus, D. C., Khera, R., Lieu, T., Liu, V., Ahmad, F. S., Anderson, B., Bhavani, S. V., Bindman, A., Brennan, T., Celi, L. A., et al. (2025) · JAMA

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 195

Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.

Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 149

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

Geis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E., Jaremko, J. L., Langer, S. G., Kitts, A. B., Birch, J., Shields, W. F., et al. (2019) · Canadian Association of Radiologists Journal

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 140

Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports

Li, H., Moon, J. T., Iyer, D., Balthazar, P., Krupinski, E. A., Bercu, Z. L., Newsome, J. M., Banerjee, I., Gichoya, J. W., & Trivedi, H. M. (2023) · Clinical Imaging

Artificial Intelligence in Healthcare and EducationRadiology practices and education

Cited by 118

Optimizing risk-based breast cancer screening policies with reinforcement learning

Yala, A., Mikhael, P. G., Lehman, C., Lin, G., Strand, F., Wan, Y.-L., Hughes, K., Satuluru, S., Kim, T., Banerjee, I., et al. (2022) · Nature Medicine

AI in cancer detectionGlobal Cancer Incidence and Screening

Cited by 103

The EMory BrEast imaging Dataset (EMBED): a racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images

Jeong, J. J., Vey, B. L., Bhimireddy, A., Kim, T., Santos, T., Correa, R., Dutt, R., Mosunjac, M., Oprea-Ilies, G., Smith, G., et al. (2023) · Radiology Artificial Intelligence

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 92

Performance of intensive care unit severity scoring systems across different Ethnicities in the USA: a retrospective observational study

Sarkar, R., Martin, C., Mattie, H., Gichoya, J. W., Stone, D. J., & Celi, L. A. (2021) · The Lancet Digital Health

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 85

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Hanneman, K., Playford, D., Dey, D., van Assen, M., Mastrodicasa, D., Cook, T. S., Gichoya, J. W., Williamson, E. E., Rubin, G. D., & on behalf of the American Heart Association Council on Cardiovascular Radiology and Intervention; and Council on Lifelong Congenital Heart Disease and Heart Health in the Young (2024) · Circulation

Artificial Intelligence in Healthcare and EducationCardiac Imaging and Diagnostics

Cited by 72

Current Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting Evidence

Tariq, A., Purkayastha, S., Padmanaban, G. P., Krupinski, E., Trivedi, H., Banerjee, I., & Gichoya, J. W. (2020) · Journal of the American College of Radiology

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

Cited by 63

Evaluation of Federated learning variations for COVID-19 diagnosis using chest Radiographs from 42 US and European hospitals

Peng, L., Luo, G., Walker, A., Zaiman, Z., Jones, E. K., Gupta, H., Kersten, K., Burns, J. L., Harle, C. A., Magoc, T., et al. (2022) · Journal of the American Medical Informatics Association

Privacy-Preserving Technologies in DataCOVID-19 diagnosis using AI

Cited by 36

Implications of predicting race variables from medical images

Zou, J., Gichoya, J. W., Ho, D. E., & Obermeyer, Z. (2023) · Science

Race, Genetics, and SocietyArtificial Intelligence in Healthcare and Education

Cited by 30

Imaging Artificial Intelligence: A Framework for Radiologists to Address Health Equity, From the AJR Special Series on DEI

Davis, M. A., Lim, N., Jordan, J., Yee, J., Gichoya, J. W., & Lee, R. (2023) · American Journal of Roentgenology

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 19

A scoping review of silent trials for medical artificial intelligence

Tikhomirov, L., Semmler, C., Prizant, N., Bhasin, S., Kenyon, G., van der Vegt, A., Erdman, L., Kurian, N. C., Thompson, H., Palmer, L. J., et al. (2026) · Nature Health

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 17

AI in action: a road map from the radiology AI council for effective model evaluation and deployment

Trivedi, H., Khosravi, B., Gichoya, J., Benson, L., Dyckman, D., Galt, J., Howard, B. M., Kikano, E. G., Kunjummen, J., Lall, N., et al. (2025) · Journal of the American College of Radiology

Artificial Intelligence in Healthcare and EducationRadiology practices and education

Cited by 15

Detecting racial/ethnic health disparities using deep learning from frontal chest radiography

Pyrros, A., Rodríguez-Fernández, J. M., Borstelmann, S. M., Gichoya, J. W., Horowitz, J. M., Fornelli, B., Siddiqui, N., Velichko, Y., Koyejo, O., & Galanter, W. (2022) · Journal of the American College of Radiology

COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and Education

Cited by 15

MedShift: identifying shift data for medical dataset curation

Guo, X., Gichoya, J. W., Trivedi, H., Purkayastha, S., & Banerjee, I. (2021) · arXiv

Anomaly Detection Techniques and ApplicationsMachine Learning and Data Classification

Cited by 2