Cited by 117Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.

Advancing health equity with artificial intelligence

Open access

Thomasian, Nicole M., Eickhoff, Carsten, Adashi, Eli Y.·2021·Journal of Public Health Policy

DOI 10.1057/s41271-021-00319-5PMC PMC8607970

Open this paper in CoChat — free account Open PDF

Abstract

Population and public health are in the midst of an artificial intelligence revolution capable of radically altering existing models of care delivery and practice. Just as AI seeks to mirror human cognition through its data-driven analytics, it can also reflect the biases present in our collective conscience. In this Viewpoint, we use past and counterfactual examples to illustrate the sequelae of unmitigated bias in healthcare artificial intelligence. Past examples indicate that if the benefits of emerging AI technologies are to be realized, consensus around the regulation of algorithmic bias at the policy level is needed to ensure their ethical integration into the health system. This paper puts forth regulatory strategies for uprooting bias in healthcare AI that can inform ongoing efforts to establish a framework for federal oversight. We highlight three overarching oversight principles in bias mitigation that maps to each phase of the algorithm life cycle.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1057/s41271-021-00319-5

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 6×
“Failure to account for race in contexts where known disparities are relevant to the algorithm’s intended use case has been repeatedly shown to entrench bias [3, 21, 22].”
BackgroundMethodResultInfluential
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312
Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501
Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
Vyas, D. A., Eisenstein, L. G., & Jones, D. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmms2004740 · Cited by 1,731
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · cited 1×
“Finally, collaborative training techniques such as federated learning and cyclic weight transfer are just two examples of options to boost data availability, as they can allow for model training across multiple institutions without transfer of patient data (See Box 1) [18, 19].”
Background
Federated Learning for Healthcare Informatics
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00082-4 · Cited by 1,542 · cited 1×
“examples of options to boost data availability, as they can allow for model training across multiple institutions without transfer of patient data (See Box 1) [18, 19].”
Background
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers
Mongan, J., Moy, L., & Kahn, C. E. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020200029 · Cited by 1,443 · cited 1×
“Regulators can look towards AI-reporting recommendations in academia as a reference when formulating validation criteria, as the research community has recently developed a number of checklists designed to quantify the robustness of artificial intelligence studies [24, 25].”
Background
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421

Show all 35 references

Research works printed but not linked: 1

Stiglic G, Kocbek P, Fijacko N, Zitnik M, Verbert K, Cilar L. Interpretability of machine learning-based prediction models in healthcare. Wiley Interdiscip Rev: Data Min Knowl Discov. 2020;10(5):e1379.

Sorted by citations

Bias in medical AI: Implications for clinical decision-making
Cross, J. L., Choma, M. A., & Onofrey, J. A. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000651 · Cited by 607
Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment
Khalighi, S., Reddy, K. M., Midya, A., Pandav, K. B., Madabhushi, A., & Abedalthagafi, M. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00575-0 · Cited by 364
A Scoping Review of Reporting Gaps in FDA-Approved AI Medical Devices
Muralidharan, V., Adewale, B. A., Huang, C. J., Nta, M. T., Ademiju, P. O., Pathmarajah, P., Hang, M. K., Adesanya, O., Abdullateef, R. O., Babatunde, A. O., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01270-x · Cited by 201
Ethical considerations on artificial intelligence in dentistry: A framework and checklist
Rokhshad, R., Ducret, M., Chaurasia, A., Karteva, T., Radenković, M., Roganović, J., Hamdan, M. H., Mohammad‐Rahimi, H., Krois, J., Lahoud, P., et al. (2023) · Journal of Dentistry · doi:10.1016/j.jdent.2023.104593 · Cited by 149
A survey of recent methods for addressing AI fairness and bias in biomedicine
Yang, Y., Lin, M., Zhao, H., Peng, Y., Huang, F., & Lu, Z. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104646 · Cited by 122
Patient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review
Moy, S., Irannejad, M., Manning, S. J., Farahani, M., Ahmed, Y., Gao, E., Prabhune, R., Lorenz, S., Mirza, R. M., & Klinger, C. A. (2024) · Journal of patient-centered research and reviews · doi:10.17294/2330-0698.2029 · Cited by 113
Advancing AI in healthcare: A comprehensive review of best practices
Polevikov, S. (2023) · Clinica Chimica Acta · doi:10.1016/j.cca.2023.117519 · Cited by 121
Equity within AI systems: What can health leaders expect?
Gurevich, E., Hassan, B. E., & Morr, C. E. (2022) · Healthcare Management Forum · doi:10.1177/08404704221125368 · Cited by 108
Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence
Paladugu, P., Ong, J., Nelson, N. G., Kamran, S. A., Waisberg, E., Zaman, N., Kumar, R., DIAS, R. D., Lee, A. G., & Tavakkoli, A. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03304-z · Cited by 98
Guiding principles for the responsible development of artificial intelligence tools for healthcare
Badal, K., Lee, C. M., & Esserman, L. J. (2023) · Communications Medicine · doi:10.1038/s43856-023-00279-9 · Cited by 77
Nbias: A natural language processing framework for BIAS identification in text
Raza, S., Garg, M., Reji, D. J., Bashir, S. R., & Ding, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121542 · Cited by 76
Strategies to improve the impact of artificial intelligence on health equity: scoping review
Berdahl, C. T., Baker, L., Mann, S., Osoba, O., & Girosi, F. (2023) · JMIR AI · doi:10.2196/42936 · Cited by 68
Accelerating health disparities research with artificial intelligence
Green, B. L., Murphy, A., & Robinson, E. J. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1330160 · Cited by 73
Developing, Implementing, and Evaluating an Artificial Intelligence-Guided Mental Health Resource Navigation Chatbot for Health Care Workers and Their Families During and Following the COVID-19 Pandemic: Protocol for a Cross-sectional Study
Noble, J. M., Zamani, A., Gharaat, M., Merrick, D., Maeda, N., Lambe Foster, A., Nikolaidis, I., Goud, R., Stroulia, E., Agyapong, V. I. O., et al. (2022) · JMIR Research Protocols · doi:10.2196/33717 · Cited by 53
Transforming Public Health Practice With Generative Artificial Intelligence
Bharel, M., Auerbach, J. M., Nguyen, V., & DeSalvo, K. B. (2024) · Health Affairs · doi:10.1377/hlthaff.2024.00050 · Cited by 60
Implications of Large Language Models for Quality and Efficiency of Neurologic Care
Moura, L. M. V. R., Jones, D. T., Sheikh, I., Murphy, S. N., Kalfin, M., Kummer, B. R., Weathers, A. L., Grinspan, Z. M., Silsbee, H. M., Jones, L. K., et al. (2024) · Neurology · doi:10.1212/wnl.0000000000209497 · Cited by 53
The promise of data science for health research in Africa
Adebamowo, C., Callier, S., Akintola, S., Maduka, O. C., Jegede, A. S., Arima, C. G., Ogundiran, T. O., & Adebamowo, S. N. (2023) · Nature Communications · doi:10.1038/s41467-023-41809-2 · Cited by 49
Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV
Kuo, N. I.-H., García, F., Sönnerborg, A. B., Böhm, M., Kaiser, R., Zazzi, M., Polizzotto, M. N., Jorm, L. R., & Barbieri, S. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104436 · Cited by 54
Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare
Kleinberg, G., Diaz, M. J., Batchu, S., & Lucke-Wold, B. P. (2022) · Journal of Biomed Research · doi:10.46439/biomedres.3.025 · Cited by 45
Integrating health equity in artificial intelligence for public health in Canada: a rapid narrative review
Ghanem, S., Moraleja, M., Gravesande, D., & Rooney, J. A. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1524616 · Cited by 39
Ethical and social issues related to AI in healthcare
Mondal, H., & Mondal, S. (2024) · Methods in microbiology · doi:10.1016/bs.mim.2024.05.009 · Cited by 33
Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways
Gao, Q., Chen, L., & Huang, Z. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1748797 · Cited by 39
Community perspectives on AI/ML and health equity: AIM-AHEAD nationwide stakeholder listening sessions
Vishwanatha, J. K., Christian, A. H., Sambamoorthi, U., Thompson, E. L., Stinson, K., & Syed, T. A. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000288 · Cited by 29
Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model
Channa, R., Wolf, R. M., Abràmoff, M. D., & Lehmann, H. P. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00785-z · Cited by 32
State-of-the-Art in Responsible, Explainable, and Fair AI for Medical Image Analysis
Amirian, S., Gao, F., Littlefield, N. G., Hill, J. H., Yates, A. J., Plate, J. F., Pantanowitz, L., Rashidi, H. H., & Tafti, A. P. (2025) · IEEE Access · doi:10.1109/access.2025.3555543 · Cited by 34
Exploring bias and prediction metrics to characterise the fairness of machine learning for equity-centered public health decision-making: a narrative review
Raza, S., Shaban-Nejad, A., Dolatabadi, E., & Mamiya, H. (2024) · IEEE Access · doi:10.1109/access.2024.3509353 · Cited by 25
Building an ethical and trustworthy biomedical AI ecosystem for the translational and clinical integration of foundation models
Sankar, B. S., Gilliland, D., Rincon, J., Hermjakob, H., Yan, Y., Adam, I., Lemaster, G., Wang, D., Watson, K., Bui, A., et al. (2024) · Bioengineering · doi:10.3390/bioengineering11100984 · Cited by 26
Bridging Health Disparities in the Data-Driven World of Artificial Intelligence: A Narrative Review
Murphy, A., Bowen, K., Naqa, I. M. E., Yoga, B., & Green, B. L. (2024) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-024-02057-2 · Cited by 27
Early childhood caries risk prediction using machine learning approaches in Bangladesh
Hasan, F., Tantawi, M. E., Haque, F., Foláyan, M. O., & Virtanen, J. I. (2025) · BMC Oral Health · doi:10.1186/s12903-025-05419-2 · Cited by 27
Achieving health equity through healthcare technology: Perspective from India
Gupta, P., Choudhury, R. K., & Kotwal, A. (2023) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_321_23 · Cited by 21
Developing an Ethical Regulatory Framework for Artificial Intelligence: Integrating Systematic Review, Thematic Analysis, and Multidisciplinary Theories
Wang, J., Huo, Y., Mahe, J., Ge, Z., Liu, Z., Wang, W., & Zhang, L. (2024) · IEEE Access · doi:10.1109/access.2024.3501332 · Cited by 19
Integrating enterprise risk management to address AI‐related risks in healthcare: Strategies for effective risk mitigation and implementation
Palma, G. D., Scendoni, R., Tambone, V., Alloni, R., & Micco, F. D. (2025) · Journal of Healthcare Risk Management · doi:10.1002/jhrm.70000 · Cited by 24
Ethics Principles for Artificial Intelligence–Based Telemedicine for Public Health
Tiribelli, S., Monnot, A., Shah, S. F. H., Arora, A., Toong, P. J., & Kong, S. (2023) · American Journal of Public Health · doi:10.2105/ajph.2023.307225 · Cited by 19
Artificial intelligence and global health equity
Dychiao, R. G., Nazer, L. H., Mlombwa, D., & Celi, L. A. (2024) · BMJ · doi:10.1136/bmj.q2194 · Cited by 21
Ethical challenges of using artificial intelligence in healthcare delivery: a thematic analysis of a systematic review of reviews
Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024) · Journal of Public Health · doi:10.1007/s10389-024-02219-w · Cited by 20
Digital pathology, deep learning, and cancer: a narrative review
Williams, D. K. A., Graifman, G., Hussain, N., Amiel, M., Priscilla, T., Reddy, A., Haider, A., Kavitesh, B. K., Li, A., Alishahian, L., et al. (2024) · Translational Cancer Research · doi:10.21037/tcr-23-964 · Cited by 20
Trust and AI in healthcare: a systematic review
Astobiza, A. M., Fernández, M. A., & Lozano, R. O. (2025) · Bioethics News · doi:10.1007/s40592-025-00272-z · Cited by 22
Role of artificial intelligence in early identification and risk evaluation of non-communicable diseases: a bibliometric analysis of global research trends
Al-Dekah, A. M., & Sweileh, W. M. (2025) · BMJ Open · doi:10.1136/bmjopen-2025-101169 · Cited by 19
Reimagining healthcare education through nurturing AI-driven innovation
Mendes, J. M. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08254-9 · Cited by 18
How AI is advancing asthma management? Insights into economic and clinical aspects
Meslamani, A. Z. A. (2023) · Journal of Medical Economics · doi:10.1080/13696998.2023.2277072 · Cited by 16
Clinical Algorithms and the Legacy of Race-Based Correction: Historical Errors, Contemporary Revisions and Equity-Oriented Methodologies for Epidemiologists
Horsfall, L., Bondaronek, P., Ive, J., & Poduval, S. (2025) · Clinical Epidemiology · doi:10.2147/clep.s527000 · Cited by 15
Systematic Review and Meta-Analysis: Effectiveness of Stand-Alone Digital Suicide Preventive Interventions for the Self-Management of Suicidality
Sütöri, S., Hadlaczky, G., Eliasson, E. T., Wasserman, D., & Carli, V. (2023) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-023-00374-7 · Cited by 16
Artificial Intelligence to Promote Racial and Ethnic Cardiovascular Health Equity
Amponsah, D., Thamman, R., Brandt, E. J., James, C. A., Spector‐Bagdady, K., & Yong, C. M. (2024) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-024-00745-6 · Cited by 15
The perception of artificial intelligence and infertility care among patients undergoing fertility treatment
Cromack, S. C., Lew, A. M., Bazzetta, S. E., Xu, S., & Walter, J. R. (2025) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-024-03382-5 · Cited by 13
Towards abundant intelligences: Considerations for Indigenous perspectives in adopting artificial intelligence technology
Silano, J. A. (2024) · Healthcare Management Forum · doi:10.1177/08404704241257144 · Cited by 12
Big Data/AI in Neurocritical Care: Maybe/Summary
Suárez, J. I. (2021) · Neurocritical Care · doi:10.1007/s12028-021-01422-x · Cited by 12
The AI-Powered Healthcare Ecosystem: Bridging the Chasm Between Technical Validation and Systemic Integration—A Systematic Review
Rahamtalla, B. M., Medani, I. E., Abdelhag, M. E., Eltigani, S. A., Rajan, S. K., Falgy, E., Hassan, N. M., Fadailu, M. E., Khudhayr, H. A., & Abdalla, A. (2025) · Future Internet · doi:10.3390/fi17120550 · Cited by 18
Improving Clinical Decision Making With a Two-Stage Recommender System
Raza, S., & Ding, C. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3318209 · Cited by 16
Fairness in Machine Learning Meets with Equity in Healthcare
Raza, S., Pour, P. O., & Bashir, S. R. (2023) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v1i1.27493 · Cited by 14
A Competency Framework for Medical AI Education: Mixed Methods Study
Cai, C., Zhu, G., Zhou, S., Ng, O., Duell, J., Ho, W. K., Chen, D. M., Lee, B. T. K., Li, F., Liu, S., et al. (2026) · JMIR Medical Education · doi:10.2196/91116 · Cited by 9
Connecting Fairness in Machine Learning with Public Health Equity
Raza, S. (2023) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi57859.2023.00127 · Cited by 9
Fairframe: a fairness framework for bias detection and mitigation in news
Sallami, D., & Aı̈meur, E. (2024) · AI and Ethics · doi:10.1007/s43681-024-00568-6 · Cited by 15
Computational Approaches for Predicting Preterm Birth and Newborn Outcomes
Seong, D., Espinosa, C. A., & Aghaeepour, N. (2024) · Clinics in Perinatology · doi:10.1016/j.clp.2024.02.005 · Cited by 9
Equality, diversity, and inclusion in artificial intelligence-driven healthcare chatbots: addressing challenges and shaping strategies
Küçükkaya, A., Aktaş, E., Moons, P., & Göktaş, P. (2025) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvaf104 · Cited by 9
Health for whom? Intersectionality and biases in the use of artificial intelligence in clinical diagnosis
Amaya-Santos, S., Jiménez-Pernett, J., & Bermúdez‐Tamayo, C. (2024) · Anales del Sistema Sanitario de Navarra · doi:10.23938/assn.1077 · Cited by 9
Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease
Perez, S. A., Thandra, S., Mellah, I., Kraemer, L. S., & Ross, E. (2024) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-024-00752-7 · Cited by 9
Advancing AI Literacy in Medical Education: A Medical AI Competency Framework Development
Cai, C., Duell, J., Chen, D. M., Ho, W. K., Lee, B. T. K., Li, F., Liu, S., Ng, O., Sudarshan, V. K., Zhou, S., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98462-4_15 · Cited by 16
Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis
Teeple, S., Chivers, C., Linn, K. A., Halpern, S. D., Eneanya, N. D., Draugelis, M. E., & Courtright, K. R. (2023) · BMJ Quality & Safety · doi:10.1136/bmjqs-2022-015173 · Cited by 7
Ethical Principles and Considerations concerning the Use of Artificial Intelligence in Healthcare*
Gieop, M. O. O. N., Yang, J. H., Son, Y., Choi, E. K., & Lee, I. (2023) · Korean Journal of Medical Ethics · doi:10.35301/ksme.2023.26.2.103 · Cited by 7
Artificial intelligence in the identification and prediction of adverse transfusion reactions(ATRs) and implications for clinical management: a systematic review of models and applications
ShojaeiBaghini, M., Ghaemi, M. M., & Ahmadipour, A. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03232-z · Cited by 6
Radiomic Features as Artificial Intelligence Prognostic Models in Glioblastoma: A Systematic Review and Meta-Analysis
Wardhana, D. P. W., Maliawan, S., Mahadewa, T. G. B., Rosyidi, R. M., & Wiranata, S. (2024) · Diagnostics · doi:10.3390/diagnostics14212354 · Cited by 6
Sağlık Sektöründe Karar Destek Araçları: İş Zekâsı, Makine Öğrenmesi, Derin Öğrenme ve Yapay Zeka Uygulamaları
Damar, M. (2024) · İzmir Sosyal Bilimler Dergisi · doi:10.47899/ijss.1591168 · Cited by 6
Artificial intelligence in biology and medicine, and radioprotection research: perspectives from Jerusalem
Socol, Y., Richardson, A., Garali, I., Grison, S., Varès, G., & Klokov, D. Y. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1291136 · Cited by 6
Advancing healthcare systems: A tri‐tier architecture by using data communication, AI data generative and regulation and compliance standards
Qureshi, K. N., Nafea, H., & Kim, P. W. (2024) · Expert Systems · doi:10.1111/exsy.13742 · Cited by 5
Evaluating the normative implications of national and international artificial intelligence policies for Sustainable Development Goal 3: good health and well-being
Mazzi, F. (2025) · Health Affairs Scholar · doi:10.1093/haschl/qxaf108 · Cited by 7
Artificial Intelligence And Women's Health: Innovations, Challenges, And Ethical Considerations
Korytnikova, E. (2023) · Advances In Clinical And Medical Research · doi:10.52793/acmr.2023.4(3)-59 · Cited by 5
Auditing ICU Readmission Rates in an Clinical Database: An Analysis of Risk Factors and Clinical Outcomes
Raza, S., & Bashir, S. R. (2023) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi57859.2023.00132 · Cited by 5
Intent and Entity Detection with Data Augmentation for a Mental Health Virtual Assistant Chatbot
Zamani, A., Reeson, M., Marshall, T., Gharaat, M. A., Foster, A. L., Noble, J. M., & Zai͏̈ane, O. R. (2023) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3570945.3607324 · Cited by 6
Artificial Intelligence and Public Health Context: What We Should Know?
Madli, F., Janin, Y., Gulabdin, S. B., Lada, S., Yun, W. S., Adis, A.-A. A., & Jafar, A. (2024) · Journal of Advanced Research in Applied Sciences and Engineering Technology · doi:10.37934/araset.39.2.96109 · Cited by 7
Towards better Data Science to address racial bias and health equity
Nsoesie, E. O., & Galea, S. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac120 · Cited by 6
Identifying and Characterizing Bias at Scale in Clinical Notes Using Large Language ModelsPreprint
Apakama, D. U., Nguyen, K.-A.-N., Hyppolite, D., Soffer, S., Mudrik, A., Ling, E. J., Moses, A., Temnycky, I., Glasser, A., Anderson, R., et al. (2024) · medRxiv · doi:10.1101/2024.10.24.24316073 · Cited by 5
Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review
Amaya-Santos, S., Vargas, C., & Bermúdez-Tamayo, C. (2026) · Public Health in Practice · doi:10.1016/j.puhip.2025.100713 · Cited by 4
“Integration of gendered and intersectional health analysis : Embed frameworks like Gendered Health Analysis (GHA) into the development process to address how intersecting identities influence healthcare outcomes [50].”
Multiple approaches to advance health equity in nursing science: Recruitment, data, and dissemination
Wu, B., & Zhu, Z. (2024) · Nursing Outlook · doi:10.1016/j.outlook.2024.102343 · Cited by 4
Post-processing methods for mitigating algorithmic bias in healthcare classification models: An extended umbrella review
Mackin, S., Major, V. J., Chunara, R., & Newton-Dame, R. (2025) · BMC Digital Health · doi:10.1186/s44247-025-00166-4 · Cited by 5
Remote health monitoring using artificial intelligence
Jain, P., & Palei, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13816-4.00008-5 · Cited by 4
Health Disparities and Reporting Gaps in Artificial Intelligence (AI) Enabled Medical Devices: A Scoping Review of 692 U.S. Food and Drug Administration (FDA) 510k ApprovalsPreprint
Muralidharan, V., Adewale, B. A., Huang, C. J., Nta, M. T., Ademiju, P. O., Pathmarajah, P., Hang, M. K., Adesanya, O. A., Abdullateef, R. O., Babatunde, A. O., et al. (2024) · medRxiv · doi:10.1101/2024.05.20.24307582 · Cited by 4
Ethical Considerations and Bias in AI Applications in Healthcare
Ali, A. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-84404-1_7 · Cited by 4
Ethical Issues Raised by Artificial Intelligence and Big Data in Population Health: A Scoping Review
Couture, V., Roy, M.-C., Dez, E., Tremblay, F., & Bélisle-Pipon, J.-C. (2025) · Frontiers in Sociology · doi:10.3389/fsoc.2025.1536389 · Cited by 3
Advancing Public Health Surveillance With Artificial Intelligence: A Systematic Review of Real‐Time Data Analytics and Disease Prediction
Islam, M. A., Hosen, A., Rony, M. K. K., Vanu, N., Bhuiyan, M. F. H., Tasnim, A. F., Tiwari, A., Yeasmin, S., & Manik, M. M. T. G. (2025) · Advances in Public Health · doi:10.1155/adph/2190224 · Cited by 6
Teleneurology and Artificial Intelligence in Clinical Practice
Jones, E., Kummer, B. R., & Wilkinson, J. R. (2024) · CONTINUUM Lifelong Learning in Neurology · doi:10.1212/con.0000000000001430 · Cited by 3
The Impact of Artificial Intelligence on Health Equity in Dermatology
Rinderknecht, F.-A. B., Nwandu, L., Daneshjou, R., & Lester, J. (2024) · Current Dermatology Reports · doi:10.1007/s13671-024-00436-w · Cited by 4
Transformative potential of artificial intelligence in US CDC HIV interventions: balancing innovation with health privacy
Kamitani, E., Koenig, L. J., & Sullivan, P. S. (2025) · AIDS · doi:10.1097/qad.0000000000004220 · Cited by 4
The implications of artificial intelligence in clinical healthcare: a scoping review on regulatory challenges up to 2023
Penteado, B. E., Fornazin, M., Castro, L., Rachid, R., Gonçalves, L. H. D. N., & Falcão, M. Z. (2025) · Health and Technology · doi:10.1007/s12553-025-01015-0 · Cited by 4
Addressing Health Disparities through Community Engagement in Artificial Intelligence-Driven Prevention Science
Haroz, E. E., Goklish, N. A., Dillard, A. Y., Adams, R., Bull, S. S., Gonzalez-Fisher, R. F., Valenza, P., Manson, S. M., & Thorpe, R. J. (2026) · Prevention Science · doi:10.1007/s11121-025-01863-2 · Cited by 2
Context-Aware adaptive normalization LSTM (CAAN-LSTM) for immunotherapy decision support in cancer clinical data analysis
Balafkhar, R. M., Baalawi, Y. S., Almashhor, A. M., Alasheq, A. K., Alhebshi, O. H., & Mohaini, M. A. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105006 · Cited by 2
Framing AI in public innovation: the engagement-manipulation nexus
Adebajo, A., Okpara, N., & Kolawole, A. (2026) · Public Management Review · doi:10.1080/14719037.2026.2659262 · Cited by 2
Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation
Bashir, S., Salad, H. A., Abdullahi, Y. B., Abdi, Y. H., Abdi, M. S., Ahmed, N., Musa, S. S., Elehamer, N. M. K., Musa, M. K., Bolarinwa, O. A., et al. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1841706 · Cited by 4
AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data Science Training Platform to Increase Researcher Diversity and Advance Health Equity
Ahmed, T., Hoyal, A., Stinson, K., Johnson, J., Latif, Z., Burge, L., Libin, A., Gao, G. G., Shara, N. M., & Vishwanatha, J. K. (2023) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi57859.2023.00114 · Cited by 2
Appropriate artificial intelligence algorithms will ultimately contribute to health equity
Kalina, J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-21598-8.00008-7 · Cited by 2
Data journey map: a process for co-creating data requirements for health care artificial intelligence
Charles, C., Tulloch, C., McNaughton, M. L., Hosein, P. A., & Hambleton, I. R. (2024) · Revista Panamericana de Salud Pública · doi:10.26633/rpsp.2024.107 · Cited by 2
Ambient technology in epilepsy clinical practice
Kakwan, H., Rousseau, J. F., Moura, L. M. V. R., & Sheikh, I. (2025) · Epilepsia Open · doi:10.1002/epi4.70066 · Cited by 3
Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health
Were, M. C., Li, A., Malin, B., Yin, Z., Coco, J., Collins, B. X., Clayton, E. W., Novak, L. L., Hendricks‐Sturrup, R., Oluyomi, A., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/73996 · Cited by 4
Endometriyum Kanserinin Bakım ve Yönetiminde Yapay Zeka
Kavlak, O., & Altıntaş, R. Y. (2024) · Artuklu Health · doi:10.58252/artukluhealth.1497539 · Cited by 1
Agenda setting for health equity assessment through the lenses of social determinants of health using machine learning approach: a framework and preliminary pilot study
Ramezani, M., Mobinizadeh, M., Bakhtiari, A., Rabiee, H. R., Ramezani, M., Mostafavi, H., Olyaeemanesh, A., Fazaeli, A. A., Atashi, A., Sazgarnejad, S., et al. (2025) · BioData Mining · doi:10.1186/s13040-025-00428-x · Cited by 2
Structural inequalities and chronic disease health outcomes: Impact of AI on health equity
Linde, S., Walker, R. J., Campbell, J. A., & Egede, L. E. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23750-8.00029-1 · Cited by 1
Ethical challenges and opportunities for integrating predictive analytics in community-based overdose prevention
Allen, B., Urmanche, A. A., Curtis, B., & Fisher, C. (2025) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2025.101345 · Cited by 1
Problems with a one-size-fits-all approach: a systematic literature review on solutions to AI bias in healthcare and implications for engineering biology
Harms, R. J., Ankeny, R. A., Carter, L., Mankad, A., & Scully, J. L. (2026) · AI and Ethics · doi:10.1007/s43681-026-01088-1 · Cited by 1
Improving Clinical Decision Making with a Two-Stage Recommender System: A Case Study on MIMIC-III DatasetPreprint
Raza, S. (2023) · medRxiv · doi:10.1101/2023.02.21.23286247 · Cited by 1
Deep Learning for Natural Language Processing of Neuro-Oncology Imaging ReportsPreprint
Thomasian, N. M., Imami, M. R., Xun, X., Guo, Y., He, E. W., Teferi, M., Vogt, B., Waddell, E. M., Saini, D., Scaringi, J. A., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4167846
Self-citation
Data access, data bias, data equity
Shung, D. L., & Laine, L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95068-8.00002-9

Authors: Thomasian, N. M., Eickhoff, C., & Adashi, E. Y.
Affiliations: Harvard University (US)
Brown University (US)
Corresponding author: Thomasian, Nicole M.
ORCID: Thomasian, Nicole M.
Eickhoff, Carsten
Adashi, Eli Y.
Venue: Journal of Public Health Policy
Publisher: Springer Science and Business Media LLC
Published: 2021-11-22
Volume: 42
Issue: 4
Pages: 602-611
ISSN: 0197-5897 (ISSN-L)
1745-655X
DOI: 10.1057/s41271-021-00319-5
PMC: PMC8607970
OpenAlex: W3215517669
Cited by: 117
References: 35
Citations by year: 2026: 15 · 2025: 34 · 2024: 37 · 2023: 25 · 2022: 5 · 2021: 1
FWCI: 3.18
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Journal of Public Health Policy (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords: artificial intelligence in healthcare, algorithmic bias, health equity, algorithmic fairness, bias mitigation, AI regulation, algorithmic oversight, ethical AI, federal oversight, health policy
MeSH terms: Health Equity, Artificial Intelligence, Delivery of Health Care, Humans, Policy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Advancing health equity with artificial intelligence

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks