Cited by 124Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.

Ensuring that biomedical AI benefits diverse populations

Open access

Zou, James, Schiebinger, Londa·2021·EBioMedicine

DOI 10.1016/j.ebiom.2021.103358PMC PMC8176083

Open this paper in CoChat — free account Open PDF

Abstract

Artificial Intelligence (AI) can potentially impact many aspects of human health, from basic research discovery to individual health assessment. It is critical that these advances in technology broadly benefit diverse populations from around the world. This can be challenging because AI algorithms are often developed on non-representative samples and evaluated based on narrow metrics. Here we outline key challenges to biomedical AI in outcome design, data collection and technology evaluation, and use examples from precision health to illustrate how bias and health disparity may arise in each stage. We then suggest both short term approaches-more diverse data collection and AI monitoring-and longer term structural changes in funding, publications, and education to address these challenges.

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 · 10.1016/j.ebiom.2021.103358

Cites

61 of 61 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.

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 1×
“After experiments testing how label choice affected both predictive performance and racial bias, the team recommended using an index variable that combined health prediction with cost prediction [22].”
Method
Untitled work
(2018) · Cited by 3,510
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019) · Nature Medicine · doi:10.1038/s41591-018-0268-3 · Cited by 3,148
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
Ensuring fairness in machine learning to advance health equity.
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018) · Annals of Internal Medicine · doi:10.7326/m18-1990 · Cited by 1,341
Racial bias in pulse oximetry measurement
Sjoding, M. W., Dickson, R. P., Iwashyna, T. J., Gay, S. E., & Valley, T. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2029240 · Cited by 1,114
Genetic analyses of diverse populations improves discovery for complex traits
Wojcik, G. L., Graff, M., Nishimura, K. K., Tao, R., Haessler, J., Gignoux, C. R., Highland, H. M., Patel, Y. M., Sorokin, E. P., Avery, C. L., et al. (2019) · Nature · doi:10.1038/s41586-019-1310-4 · Cited by 1,033
Geography, Ethnicity or Subsistence-Specific Variations in Human Microbiome Composition and Diversity
Gupta, V. K., Paul, S., & Dutta, C. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01162 · Cited by 1,019
Call to Action: Structural Racism as a Fundamental Driver of Health Disparities: A Presidential Advisory From the American Heart Association
Churchwell, K., Elkind, M. S. V., Benjamin, R. M., Carson, A. P., Chang, E. K., Lawrence, W., Mills, A., Odom, T. M., Rodriguez, C. J., Rodriguez, F., et al. (2020) · Circulation · doi:10.1161/cir.0000000000000936 · Cited by 984
Bad Blood: The Tuskegee Syphilis Experiment
Kiple, K. F., & Jones, J. H. (1982) · The American Historical Review · doi:10.2307/1870318 · Cited by 817

Show all 60 references

Research works printed but not linked: 6

Moran-Thomas, 2020, How a popular medical device encodes racial bias, Boston Review, 157
American Medical Assocation, Aubmented Intelligence in Health Care H-480.940. https://policysearch.ama-assn.org/policyfinder/detail/augmented%20intelligence?uri=%2FAMADoc%2FHOD.xml-H-480.940.xml. Accessed 16 March 2021.
Both authors participated in this process. A review is in progress.
NeurIPS, Thirty-fourth conference on neural information processing systems, Call for papers, 2020 https://nips.cc/Conferences/2020/CallForPapers. Accessed 23 October 2020.
The Lancet. Advancing racial equality. 2020. https://www.thelancet.com/racial-equality. Accessed 16 Mar 2021.
2020, Technology can't fix this, Nature Mach Intell, 363

Sorted by citations

Multimodal biomedical AI
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
The value of standards for health datasets in artificial intelligence-based applications
Arora, A., Alderman, J. E., Palmer, J., Ganapathi, S., Laws, E., McCradden, M. D., Oakden‐Rayner, L., Pfohl, S. R., Ghassemi, M., McKay, F., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02608-w · Cited by 341
Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers
Solanki, P., Grundy, J., & Hussain, W. (2023) · AI and Ethics · doi:10.1007/s43681-022-00195-z · Cited by 157
AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis
Alum, E. U. (2025) · Discover Oncology · doi:10.1007/s12672-025-02064-7 · Cited by 159
Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate
Amini, M. M., Jesus, M., Sheikholeslami, D. F., Alves, P. J. P., Benam, A. H., & Hariri, F. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5030053 · Cited by 152
A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning
McCradden, M. D., Anderson, J. A., Stephenson, E. A., Drysdale, E., Erdman, L., Goldenberg, A., & Shaul, R. Z. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2021.2013977 · Cited by 123
Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review
Marko, J. G. O., Neagu, D. C., & Anand, P. B. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02884-1 · Cited by 139
Smart data processing for energy harvesting systems using artificial intelligence
Divya, S., Panda, S., Hajra, S., Jeyaraj, R., Paul, A., Park, S. H., Kim, H. J., & Oh, T. H. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.108084 · 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
A comprehensive review on triboelectric nanogenerators based on Real-Time applications in energy harvesting and Self-Powered sensing
Munirathinam, P., Mathew, A. A., Shanmugasundaram, V., Vivekananthan, V., Purusothaman, Y., Kim, S., & Chandrasekhar, A. (2023) · Materials Science and Engineering B · doi:10.1016/j.mseb.2023.116762 · Cited by 100
Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review
Gastounioti, A., Desai, S., Ahluwalia, V. S., Conant, E. F., & Despina Kontos, B. (2022) · Breast Cancer Research · doi:10.1186/s13058-022-01509-z · Cited by 103
Influential Factors, Enablers, and Barriers to Adopting Smart Technology in Rural Regions: A Literature Review
Alabdali, S. A., Pileggi, S. F., & Çetindamar, D. (2023) · Sustainability · doi:10.3390/su15107908 · Cited by 116
Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis
Adler‐Milstein, J., Aggarwal, N., Ahmed, M. K., Castner, J., Incorporated, C., Evans, B. J., Gonzalez, A. A., James, C. A., Lin, S., Mandl, K. D., et al. (2022) · NAM Perspectives · doi:10.31478/202209c · Cited by 96
A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data
Nguyen, T., Dakka, M. A., Diakiw, S. M., VerMilyea, M. D., Perugini, M., Hall, J. M. M., & Perugini, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-12833-x · Cited by 86
Bias in algorithms of AI systems developed for COVID-19: A scoping review
Delgado, J., de Manuel, A., Parra, I., Moyano, C., Rueda, J., Guersenzvaig, A., Ausin, T., Cruz, M., Casacuberta, D., & Puyol, A. (2022) · Journal of Bioethical Inquiry · doi:10.1007/s11673-022-10200-z · Cited by 70
Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine
Lambin, P., Woodruff, H. C., Mali, S. A., Zhong, X., Kuang, S., Lavrova, E., Khan, H., Lekadir, K., Zwanenburg, A., Deasy, J. O., et al. (2025) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01067-1 · Cited by 72
Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning
Koçak, B., Cuocolo, R., Santos, D. P. D., Stanzione, A., & Ugga, L. (2023) · Balkan Medical Journal · doi:10.4274/balkanmedj.galenos.2022.2022-11-51 · Cited by 64
Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities
Hasani, N., Farhadi, F., Morris, M. A., Nikpanah, M., Rahmim, A., Xu, Y., Pariser, A., Collins, M. T., Summers, R. M., Jones, E. C., et al. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.09.009 · Cited by 66
The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19
Galetsi⁠, P., Katsaliaki, K., & Kumar, S. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114973 · Cited by 55
Examining the Role of AI in Changing the Role of Nurses in Patient Care: Systematic Review
Khatib, I. A., & Ndiaye, M. (2024) · JMIR Nursing · doi:10.2196/63335 · Cited by 65
Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions
Spanakis, M., Tzamali, E., Tzedakis, G., Koumpouzi, C., Pediaditis, M., Tsatsakis, A. M., & Sakkalis, V. (2025) · Pharmaceuticals · doi:10.3390/ph18030282 · Cited by 54
Advances in the Application of AI Robots in Critical Care: Scoping Review
Li, Y., Wang, M., Wang, L., Cao, Y. Y., Liu, Y., Zhao, Y., Rui, Y., Yang, M., Lu, S., Sun, Z., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/54095 · Cited by 59
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
Education Data Science: Past, Present, Future
McFarland, D. A., Khanna, S., Domingue, B. W., & Pardos, Z. A. (2021) · AERA Open · doi:10.1177/23328584211052055 · Cited by 45
Towards equitable AI in oncology
Viswanathan, V. S., Parmar, V. V., & Madabhushi, A. (2024) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-024-00909-8 · Cited by 45
The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities – a scoping review
Hussain, S. A., Bresnahan, M., & Zhuang, J. (2024) · Ethnicity and Health · doi:10.1080/13557858.2024.2422848 · Cited by 50
Synthetic Generation of Face Videos with Plethysmograph Physiology
Wang, Z., Ba, Y., Chari, P., Bozkurt, O. D., Brown, G., Patwa, P., Vaddi, N., Jalilian, L., & Kadambi, A. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01993 · Cited by 41
Trends in Graphene-Based E-Skin and Artificial Intelligence for Biomedical Applications—A Review
Mudhulu, S., Manjunatha, C., Balaji, S., Khosla, A., & Sekhar, P. K. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3294297 · Cited by 40
Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach
Hendricks‐Sturrup, R., Simmons, M., Anders, S. H., Aneni, K., Clayton, E. W., Coco, J., Collins, B. X., Heitman, E., Hussain, S., Joshi, K. P., et al. (2023) · JMIR AI · doi:10.2196/52888 · Cited by 45
Objective metrics for ethical AI: a systematic literature review
Palumbo, G., Carneiro, D. R., & Alves, V. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00541-w · Cited by 52
Prospects for AI clinical summarization to reduce the burden of patient chart review
Lee, C., Vogt, K. A., & Kumar, S. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1475092 · Cited by 49
Machine learning applications in precision medicine: Overcoming challenges and unlocking potential
Nilius, H., Tsouka, S., Nagler, M., & Masoodi, M. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117872 · Cited by 40
AI-Driven Real-Time Monitoring of Cardiovascular Conditions With Wearable Devices: Scoping Review
Abedi, A., Verma, A., Jain, D., Kaetheeswaran, J., Chui, C., Lankarany, M., & Khan, S. S. (2025) · JMIR mHealth and uHealth · doi:10.2196/73846 · Cited by 33
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
External Validation of a Mammography-Derived AI-Based Risk Model in a U.S. Breast Cancer Screening Cohort of White and Black Women
Gastounioti, A., Eriksson, M., Cohen, E. A., Mankowski, W. C., Pantalone, L., Ehsan, S., McCarthy, A. M., Despina Kontos, B., Hall, P. N., & Conant, E. F. (2022) · Cancers · doi:10.3390/cancers14194803 · Cited by 31
Artificial intelligence and sexual reproductive health and rights: a technological leap towards achieving sustainable development goal target 3.7
Gbagbo, F. Y., Ameyaw, E. K., & Yaya, S. (2024) · Reproductive Health · doi:10.1186/s12978-024-01924-9 · Cited by 39
Technological myopia
Crowther, D. (2022) · Technological Sustainability · doi:10.1108/techs-10-2022-0039 · Cited by 26
Latest developments of generative artificial intelligence and applications in ophthalmology
Feng, X., Xu, K., Luo, M., Chen, H., Yang, Y., He, Q., Song, C., Li, R., Wu, Y., Wang, H., et al. (2024) · Asia-Pacific Journal of Ophthalmology · doi:10.1016/j.apjo.2024.100090 · Cited by 33
Artificial intelligence in polycystic ovarian syndrome management: past, present, and future
Wang, J., Chen, R., Long, H., He, J., Tang, M., Su, M., Deng, R., Chen, Y., Ni, R., Zhao, S., et al. (2025) · La radiologia medica · doi:10.1007/s11547-025-02032-9 · Cited by 26
Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19
Li, M., Arun, N., Aggarwal, M., Gupta, S., Singh, P., Little, B. P., Mendoza, D. P., Corradi, G. C. A., Takahashi, M. S., Ferraciolli, S. F., et al. (2022) · Medicine · doi:10.1097/md.0000000000029587 · Cited by 25
Evolving intellectual property landscape for AI-driven innovations in the biomedical sector: opportunities in stable IP regime for shared success
Poddar, A., & Rao, S. R. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1372161 · Cited by 39
Future horizons in diabetes: integrating AI and personalized care
Zhang, K., Qi, Y. P., Wang, W., Tian, X., Wang, J., Xu, L., & Zhai, X. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1583227 · Cited by 29
Artificial intelligence in global health: An unfair future for health in Sub-Saharan Africa?
Victor, A. (2025) · Health Affairs Scholar · doi:10.1093/haschl/qxaf023 · Cited by 22
The Need to Prioritize Model-Updating Processes in Clinical Artificial Intelligence (AI) Models: Protocol for a Scoping Review
Otokiti, A. U., Ozoude, M. M., Williams, K. S., Sadiq-Onilenla, R. A., Ojo, S. A., Warsame, L., Walsh, S., & Edomwande, M. (2022) · JMIR Research Protocols · doi:10.2196/37685 · Cited by 26
Rethinking global digital health and AI-for-health innovation challenges
Farlow, A. W. K., Hoffmann, A., Tadesse, G. A., Mzurikwao, D., Beyer, R., Akogo, D., Weicken, E., Matika, T., Nweje, M. I., Wamae, W., et al. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0001844 · Cited by 23
Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review
Yousefi, F., Naye, F., Ouellet, S., Yameogo, A. R., Sasseville, M., Bergeron, F., Ozkan, M., Cousineau, M., Amil, S., Rhéaume, C., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/70381 · Cited by 28
OpenAI's Sora and Google's Veo 2 in Action: A Narrative Review of Artificial Intelligence-driven Video Generation Models Transforming Healthcare
Temsah, M.-H. A., Nazer, R. I., Altamimi, I., Aldekhyyel, R. N., Jamal, A. A., Almansour, M., Aljamaan, F., Alhasan, K. A., Temsah, A. A., Al-Eyadhy, A. A., et al. (2025) · Cureus · doi:10.7759/cureus.77593 · Cited by 23
How Generalizable Are Foundation Models When Applied to Different Demographic Groups and Settings?
Xiong, Z., Wang, X., Zhou, Y., Keane, P. A., Tham, Y. C., Wang, Y. X., & Wong, T. Y. (2024) · NEJM AI · doi:10.1056/aics2400497 · Cited by 18
Nanotechnology and Artificial Intelligence in Dyslipidemia Management—Cardiovascular Disease: Advances, Challenges, and Future Perspectives
Młynarska, E., Bojdo, K., Frankenstein, H., Kustosik, N., Mstowska, W., Przybylak, A., Rysz, J., & Franczyk, B. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14030887 · Cited by 20
Personalized 3D printed scaffolds: The ethical aspects
van Daal, M., de Kanter, A.-F. J., Bredenoord, A. L., & de Graeff, N. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.10.006 · Cited by 18
Easing the cognitive load of general practitioners: AI design principles for future-ready healthcare
Hor, T., Fong, L., Wynne, K., & Verhoeven, B. (2025) · Technovation · doi:10.1016/j.technovation.2025.103208 · Cited by 17
A scoping review of the landscape of health-related open datasets in Latin America
Restrepo, D. S., Quion, J., Vásquez-Venegas, C., Villanueva, C., Celi, L. A., & Nakayama, L. F. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000368 · Cited by 20
Participatory AI Considerations for Advancing Racial Health Equity
Parker, A. G., Vardoulakis, L., Alla, J., & Harrington, C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713165 · Cited by 20
Use of AI as a Research Recruitment Tool: Ethical Elements, Considerations, and Recommendations
Rentzepis, P. J., Snyder, D. L., Baker, T. J., & Bierer, B. E. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2649234 · Cited by 14
Artificial intelligence and pediatric surgery: where are we?
Miyake, Y., Retrosi, G., & Keijzer, R. (2024) · Pediatric Surgery International · doi:10.1007/s00383-024-05921-8 · Cited by 14
Sexual health in the era of artificial intelligence: a scoping review of the literature
Chawareb, E. A., Im, B. H., Lu, S., Hammad, M. A. M., Huang, T. R., Chen, H., & Yafi, F. A. (2025) · Sexual Medicine Reviews · doi:10.1093/sxmrev/qeaf009 · Cited by 16
Public sentiment analysis of data center energy consumption using social media data and large language models
Wang, H., Hua, W., Peng, J., & Hu, M. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115802 · Cited by 13
Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review
Wang, T., Emami, E., Jafarpour, D., Tolentino, R., Gore, G. C., & Rahimi, S. A. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000941 · Cited by 15
Health Disparities Through Generative AI Models: A Comparison Study Using a Domain Specific Large Language Model
Bautista, Y. J. P., Theran, C., Aló, R., & Lima, V. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-47454-5_17 · Cited by 15
Mitigating Bias in Machine Learning Models with Ethics-Based Initiatives: The Case of Sepsis
Banja, J. D., Xie, Y., Smith, J. R., Rana, S., & Holder, A. L. (2025) · The American Journal of Bioethics · doi:10.1080/15265161.2025.2497971 · Cited by 15
Linguistic Diversity and Mental Well-Being: Co-Designing Custom AI Chatbots with Multilingual Mothers
Elwahsh, S., Stern, N., Singh, A., & Ayobi, A. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3736615 · Cited by 18
Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism
Zhou, J., Wang, X., Li, Y., Yang, Y., & Shi, J. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02543-x · Cited by 12
Artificial intelligence and science of patient input: a perspective from people with multiple sclerosis
Helme, A. E., Kalra, D., Brichetto, G., Peryer, G., Vermersch, P., Weiland, H., White, A., & Zaratin, P. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1487709 · Cited by 12
Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners
Masawi, T. J., Miller, E. I., Rees, D. J., & Thomas, R. A. (2025) · Journal of Decision System · doi:10.1080/12460125.2025.2458874 · Cited by 15
Navigating sensor-skin coupling challenges in magnetic-based blood pressure monitoring: Innovations and clinical implications for hypertension and aortovascular disease management
Khan, W. U., Alissa, M., Allemailem, K. S., Alrumaihi, F., Alharbi, H. O., Almansour, N. M., Aldaiji, L. A., Albalawi, M. J., Abouzied, A. S., Almousa, S., et al. (2024) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2024.102964 · Cited by 6
Representation of Demographics in Otolaryngology by Artificial Intelligence Text‐to‐Image Platforms
Shaari, A. L., Saad, A. M., Patel, A. M., & Filimonov, A. (2025) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.70152 · Cited by 8
Enhancing body fat prediction with WGAN-GP data augmentation and XGBoost algorithm
Wang, X., & Chang, S. (2025) · Science Progress · doi:10.1177/00368504251366850 · Cited by 6
Digital Advancements in Retinal Models of Care in the Post–COVID-19 Lockdown Era
Ong, J., Tan, G. S. W., Ang, M., & Chhablani, J. K. (2022) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000533 · Cited by 6
Adoption and perception of LLM-based chatbots in health care: an exploratory cross-sectional survey of individuals with rheumatic diseases
Wang, E., Smith, J. D., Katz, S. J., Bishay, M., Dissanayake, T., Jones, N., Reddy, S., SHOLTER, D. E., Soo, J., & Ye, C. (2025) · Rheumatology Advances in Practice · doi:10.1093/rap/rkaf083 · Cited by 5
Health Data Sciences and Cardiovascular Disease in Africa: Needs and the Way Forward
Inam, M., Sheikh, S. S., Khoja, A. A., Abubakar, A. A., Shah, R., Samad, Z. Z., Ngugi, A. K., Alarakhiya, F., Waljee, A. K., & Virani, S. S. (2024) · Current Atherosclerosis Reports · doi:10.1007/s11883-024-01235-1 · Cited by 5
Sex, gender, and intersectional puzzles in health and biomedicine research
Schiebinger, L. L. (2022) · Med · doi:10.1016/j.medj.2022.04.003 · Cited by 5
Self-citation
Genetic Prediction of Eye, Hair, and Skin Color: Forensic Applications and Challenges in Latin American Populations
Flores-López, B. A., López-Ceballos, A. G., López-Aguilar, C., Rico-Méndez, M. A., Acosta-Ramírez, K., Cano-Ravell, A., Gembe-Olivarez, G., López-Quintero, A., Aguilar-Velázquez, J., Ramírez-de-Arellano-Sánchez, J. A., et al. (2025) · Genes · doi:10.3390/genes16101227 · Cited by 4
Artificial Intelligence, Innovation, and Safety in Biotechnology Research
Oyeniran, K. A., Oyeniran, O. H., & Oladunmoye, K. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9301-7.ch004 · Cited by 3
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
Explainable deep learning approaches and clinical insights for cancer biomarker identification
Srivastava, K., & Srivastava, R. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1810793 · Cited by 3
Automatic Wound Detection System for Multi-ethnic Populations Using YOLO
Jacob, N. V., Sowmya, V., Gopalakrishnan, E. A., Menon, R. R., & Pillai, A. V. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-85908-3_23 · Cited by 3
Artificial intelligence for laryngoscopy in vocal fold diseases: a review of dataset, technology, and ethics
Dao, T. T. P., Nguyen, T.-C., Huynh, V.-T., Bui, X., Le, T.-N., & Tran, M. (2024) · Machine Learning · doi:10.1007/s10994-024-06602-2 · Cited by 3
The role of digital libraries and emerging technologies in enhancing social work education: a comprehensive analysis
Neeraj, M. S., & Nirmala, B. P. (2025) · Social Work Education · doi:10.1080/02615479.2024.2445740 · Cited by 6
Pulse oximeters market analysis during the COVID-19 pandemic: Kyiv pharmacies’ offers and survey of pharmacy faculty students
Brytanova, T. S., Малецький, М. М., Лисянська, Г. П., & Antypenko, L. (2022) · Journal of Pharmaceutical Health Services Research · doi:10.1093/jphsr/rmac007 · Cited by 3
Analyzing Ethical Dilemmas in AI-Assisted Diagnostics and Treatment Decisions for Patient Safety
Agarwal, V., Priyanka, B., Reddy, P., Sidhu, A., Gupta, S. G., & Rastogi, S. (2024) · Seminars in Medical Writing and Education · doi:10.56294/mw2024497 · Cited by 3
Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring
Smith, E., Wagman, J., Barnes, C. M., Rees, P. C. T., & Johnson, G. E. (2025) · Mutagenesis · doi:10.1093/mutage/geaf026 · Cited by 2
Ethical and equitable approaches in AI for vector-borne disease management
Williams, J. J., Angelidou, I. A., Cholvi-Simó, M., Kadriaj, P., Martinou, A. F., Nadejda, M., Ong, S., Sadak, F., Skuhrovec, J., Velo, E., et al. (2025) · AI and Ethics · doi:10.1007/s43681-025-00933-z · Cited by 4
Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing
Zielińska, A., Fornalik, M., Szczepaniak, M., Gimła, M., Lemanska, A., Cielecka‐Piontek, J., & Souto, E. B. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag203 · Cited by 3
Artificial intelligence in clinical metagenomic pathogen detection: A critical review of pipeline integrations, challenges, and future directions
Dai, J., Tan, X., & Ma, J. (2026) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2026.107592 · Cited by 3
AI in Genomics: From Variant Calling to Multi‐Omics Integration
Sultana, H., Mohanty, S., Solomon, A. D., Iqbal, M. Y., Wani, A. K., Kumar, V., & Khattri, A. (2026) · BioEssays · doi:10.1002/bies.70160 · Cited by 2
Real-world evaluation of RetCAD deep-learning system for the detection of referable diabetic retinopathy and age-related macular degeneration
Taylor, J. R., Drinkwater, J. J., Sousa, D. C., Shah, V., & Turner, A. W. (2024) · Clinical and Experimental Optometry · doi:10.1080/08164622.2024.2385565 · Cited by 3
Optimal Selection of Left and Right Hand Multi-channel Pulse Features Based on Neighbourhood Component Analysis
Fan, L., Li, Y., Wang, J., Zhang, R., & Yao, R. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-20738-9_41 · Cited by 2
Integrating Sex, Gender, and Intersectional Analysis into Bioengineering
Schiebinger, L. L. (2022) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2022.100388 · Cited by 3
Self-citation
Hybrid AI Models for Rare Disease Diagnosis
Gowtham, T. (2025) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2025.v07i02.41436 · Cited by 1
From complexity to clarity: Role of AI in making ERAS protocols for colorectal cancer surgery accessible to patients and providers
Zha, H., Hu, Y., & Zhang, W. (2025) · Asian Journal of Surgery · doi:10.1016/j.asjsur.2025.04.335 · Cited by 1
Race in Medicine
Diamond-Hunter, M. A. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197625835.013.0016 · Cited by 1
Ethical Considerations in the Application of Artificial Intelligence in the Pharmaceutical Industry
Alshehade, S. A., Zarzour, R. H. A., Bitar, A. N., Alshehadeh, A. R., Tamilwanan, S., & Safni, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-96631-6_32 · Cited by 2
Racial Disparities Following Reconstructive Flap Procedures
Chwa, E. S., Applebaum, S. A., Khazanchi, R., Wester, J. R., & Gosain, A. K. (2023) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000009595 · Cited by 1
A Novel Privacy-Centric Training Routine for Maintaining Accuracy in Traditional Machine Learning Systems
Haritas, H. K., Haritas, C. K., & Kallimani, J. S. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-3982-4_22 · Cited by 1
Engaging Stakeholders With Professional or Lived Experience to Improve Firearm Violence Lexicon Development
Cook, N., Biel, F. M., Bet, K. A., Sills, M. R., Bataineh, A. A., Rivera, P., Templeton, A. R., & Cartwright, N. (2025) · JMIR Formative Research · doi:10.2196/68105 · Cited by 1
Ensuring Informed Consent and Fair Access to Artificial Intelligence Enhanced Healthcare Services
Zahra, M. M. A., Nema, K., Nair, R., Dash, B. B., Chowdhury, S., & Patra, S. S. (2025) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet64471.2025.11140180 · Cited by 1
External validation of an AI-driven breast cancer risk prediction model in a racially diverse cohort of women undergoing mammographic screening
Gastounioti, A., Eriksson, M., Cohen, E. A., Mankowski, W. C., Pantalone, L., McCarthy, A. M., Despina Kontos, B., Hall, P. N., & Conant, E. F. (2022) · International Workshop on Breast Imaging (IWBI) · doi:10.1117/12.2627140 · Cited by 1
Risk of Bias in Machine Learning and Statistical Models to Predict Height or Weight: A Systematic Review in Fetal and Paediatric MedicinePreprint
Lawrence, N., Bacila, I. A., Tonge, J. J., Tucker, A., Dawson, J., Lang, Z., Krone, N. P., Dhiman, P., & Collins, P. G. S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4788669 · Cited by 1
AI-Driven Innovations in Diagnostics, Remote Monitoring, and Clinical Decision Support Systems: A Systematic Review (Preprint)Preprint
Elgazzar, K., Wadie, P., Eissa, C., Alsbakhi, A., & Alhejaily, A.-M. (2025) · doi:10.2196/preprints.80928 · Cited by 1
WITHDRAWN: Integrating Sex, Gender, and Intersectional Analysis into Bioengineering
Schiebinger, L. L. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100347
Self-citation
Retracted

Authors: Zou, J., & Schiebinger, L.
Affiliations: Stanford University (US)
Corresponding author: Schiebinger, Londa
ORCID: Zou, James
Schiebinger, Londa
Venue: EBioMedicine
Publisher: Elsevier BV
Published: 2021-05
Volume: 67
Pages: 103358
ISSN: 2352-3964 (ISSN-L)
DOI: 10.1016/j.ebiom.2021.103358
PMC: PMC8176083
OpenAlex: W3158393586
Cited by: 124
References: 60
Citations by year: 2026: 16 · 2025: 43 · 2024: 23 · 2023: 17 · 2022: 22 · 2021: 3
FWCI: 1.32
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: EBioMedicine (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Science Foundation (1942926)
National Institutes of Health (P30AG059307, U01MH098953)
National Science Foundation Directorate for Engineering
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: algorithmic bias, health disparities, underrepresented groups, precision health, data collection, AI evaluation metrics, AI monitoring, research funding
MeSH terms: Artificial Intelligence, Health Policy, Humans, Medical Informatics, Health Status Disparities

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

Ensuring that biomedical AI benefits diverse populations

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