Unmasking bias in artificial intelligence: A systematic review of bias detection and mitigation strategies in electronic health record-based models
Open accessAbstract
Objectives: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data. Materials and Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 01, 2010 and December 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development, and analyzed metrics for bias assessment. Results: Of the 450 articles retrieved, 20 met our criteria, revealing 6 major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks, yet none have been deployed in real-world healthcare settings. Five studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Fifteen studies proposed strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling and reweighting. Discussion: This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation. Such measures are essential for gauging models’ practical impact and fostering ethical AI that ensures fairness and equity in healthcare.
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
Cites
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?
ChatGPT's responses can reflect subjective leanings originated from its data and embedded in its training, potentially skewing responses in subtle yet significant ways.
“Thus, its responses can reflect the subjective leanings originated from its data and embedded in its training, poten tially skewing responses in subtle yet significant ways (Chen et al., 2024).”
From If ChatGPT can do it, where is my creativity? generative AI boosts performance but diminishes experience in creative writing (2025) · cites this paper as Chen et al., 2024
The Types of bias section says that preexisting bias in data can lead to biased outcomes, and the paper’s bias-handling workflow describes bias entering during model training. But the review concerns electronic-health-record-based AI models; it does not establish that ChatGPT’s responses reflect subjective leanings or describe how such leanings might subtly skew its responses. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPrior reviews of bias in ML models and mitigation strategies often overlook the importance of addressing bias across multiple levels of the development process.
“However, these reviews often overlook the importance of addressing bias across multiple levels of the development process.”
From Advancing Health Equity through Multi-Level Fairness in Health Informatics (2026) · cites this paper as [6]
The Background and significance section says bias can arise at various stages of model development and stresses the importance of detecting and mitigating it. The Bias analyses workflow construction section likewise addresses three development stages. But the paper says prior reviews lacked a focus on EHR-based models; it does not say those reviews overlooked the importance of addressing bias across multiple stages. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions
Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., Henry, D., Altman, D. G., Ansari, M. T., Boutron, I., et al. (2016) · BMJ · doi:10.1136/bmj.i4919 · Cited by 20,329 · see citing works
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · cited 2× · see citing works
“Our analyses were further enhanced by consolidating established bias risk assessment tools, such as ROBINS-I, 16 ROBINS-E, 17 and PROBAST, 18 and relevant review articles, such as Mehrabi et al 19 which categorized potential biases in AI applications.”
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212 · cited 1× · see citing works
“8,9 Biases can emerge at various stages of the model development lifecycle, 10–12 underlining the importance of developing and implementing strategies for effective detection, mitigation, and evaluation of biases.”
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984 · cited 1× · see citing works
“Our analyses were further enhanced by consolidating established bias risk assessment tools, such as ROBINS-I, 16 ROBINS-E, 17 and PROBAST, 18 and relevant review articles, such as Mehrabi et al 19 which categorized potential biases in AI applications.”
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · cited 1× · see citing works
“46 On the other hand, AI models may exhibit biases, and if left unaddressed, they have the potential to exacerbate healthcare disparities.”
Implicit bias in healthcare professionals: a systematic review
FitzGerald, C., & Hurst, S. (2017) · BMC Medical Ethics · doi:10.1186/s12910-017-0179-8 · Cited by 2,398 · cited 1× · see citing works
“22 It often stems from preexisting bias within the data (such as stereotypes and flawed case assumptions) which may occur in the data collection and data preprocessing steps.”
Scalable and accurate deep learning with electronic health records.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242 · see citing works
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3763 · Cited by 1,395 · cited 1× · see citing works
“48,49 In this systematic review, we have comprehensively identified and scrutinized potential biases in AI models developed from EHR data, revealing their potential influence on healthcare disparities.”
Do no harm: a roadmap for responsible machine learning for health care
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0548-6 · Cited by 1,266 · cited 1× · see citing works
“13 While a few scoping reviews have been conducted to understand bias in broader medical AI, 14,15 a focused review on AI models derived from EHR data is notably absent.”
Bias in data‐driven artificial intelligence systems—An introductory survey
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., et al. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1356 · Cited by 1,043 · cited 1× · see citing works
“48,49 In this systematic review, we have comprehensively identified and scrutinized potential biases in AI models developed from EHR data, revealing their potential influence on healthcare disparities.”
Addressing bias in big data and AI for health care: A call for open science
Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100347 · Cited by 903 · see citing works
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · cited 2× · see citing works
“20,21”
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · cited 2× · see citing works
“20,21”
Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review
Celi, L. A., Cellini, J., Charpignon, M.-L., Dee, E. C., Dernoncourt, F., Eber, R., Mitchell, W. G., Moukheiber, L., Schirmer, J., Situ, J., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000022 · Cited by 576 · cited 1× · see citing works
“13 While a few scoping reviews have been conducted to understand bias in broader medical AI, 14,15 a focused review on AI models derived from EHR data is notably absent.”
Censoring Representations with an AdversaryPreprint
Edwards, H., & Storkey, A. (2015) · International Conference on Learning Representations · doi:10.48550/arxiv.1511.05897 · Cited by 563 · cited 1× · see citing works
“62 In this review, only 1 study 41 employed postprocessing methods to adjust model output, an area currently underutilized.”
HITECH Act Drove Large Gains In Hospital Electronic Health Record Adoption
Adler‐Milstein, J., & Jha, A. K. (2017) · Health Affairs · doi:10.1377/hlthaff.2016.1651 · Cited by 397 · see citing works
Personalized Medicine and the Power of Electronic Health Records
Abul-Husn, N. S., & Kenny, E. E. (2019) · Cell · doi:10.1016/j.cell.2019.02.039 · Cited by 355 · cited 1× · see citing works
“8,9 Biases can emerge at various stages of the model development lifecycle, 10–12 underlining the importance of developing and implementing strategies for effective detection, mitigation, and evaluation of biases.”
Fletcher, R. R., Nakeshimana, A., & Olubeko, O. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.561802 · Cited by 305 · cited 1× · see citing works
“55 This oversight can mask the differential impacts of AI models on diverse patient populations, inadvertently overlooking potential biases.”
Belenguer, L. (2022) · AI and Ethics · doi:10.1007/s43681-022-00138-8 · Cited by 289 · see citing works
A clarification of the nuances in the fairness metrics landscape
Castelnovo, A., Crupi, R., Greco, G., Regoli, D., Penco, I., & Cosentini, A. (2021) · Scientific Reports · doi:10.1038/s41598-022-07939-1 · Cited by 283 · see citing works
Data Decisions and Theoretical Implications when Adversarially Learning Fair RepresentationsPreprint
Beutel, A., Chen, J., Zhao, Z., & Chi, E. H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00075 · Cited by 282 · see citing works
Bero, L. A., Chartres, N., Diong, J. H. L., Fabbri, A., Ghersi, D., Lam, J., Lau, A., McDonald, S., Mintzes, B. J., Sutton, P., et al. (2018) · Systematic Reviews · doi:10.1186/s13643-018-0915-2 · Cited by 279 · cited 1× · see citing works
“Our analyses were further enhanced by consolidating established bias risk assessment tools, such as ROBINS-I, 16 ROBINS-E, 17 and PROBAST, 18 and relevant review articles, such as Mehrabi et al 19 which categorized potential biases in AI applications.”
Mitigating bias in machine learning for medicine
Vokinger, K. N., Feuerriegel, S., & Kesselheim, A. S. (2021) · Communications Medicine · doi:10.1038/s43856-021-00028-w · Cited by 269 · see citing works
Addressing fairness in artificial intelligence for medical imaging
Lara, M. A. R., Echeveste, R., & Ferrante, E. (2022) · Nature Communications · doi:10.1038/s41467-022-32186-3 · Cited by 208 · see citing works
Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset
Meng, C., Trinh, L., Xu, N., Enouen, J., & Liu, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-11012-2 · Cited by 199 · cited 2× · see citing works
“27,31,35,38,39 only detected the presence of bias in AI models by introducing quantitative measurement to identify and explain source of bias, as listed in Table 2.”
A Convex Framework for Fair Regression
Berk, R., Heidari, H., Jabbari, S., Joseph, M., Kearns, M., Morgenstern, J., Neel, S., & Roth, A. (2017) · arXiv · doi:10.48550/arxiv.1706.02409 · Cited by 198 · see citing works
Temporal quality degradation in AI models
Jarquin, D. V., Sharp, A., Zhang, R., Nguyen, T., Hoang, A. T., & Pianykh, O. S. (2022) · Scientific Reports · doi:10.1038/s41598-022-15245-z · Cited by 187 · cited 1× · see citing works
“53,54 No study has yet developed a comprehensive pipeline to address more than 2 types of bias simultaneously.”
Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review
Huang, J., Galal, G., Etemadi, M., & Vaidyanathan, M. (2022) · JMIR Medical Informatics · doi:10.2196/36388 · Cited by 186 · cited 2× · see citing works
“Second, we expanded our scope by integrating insights from the broader literature on healthcare AI, including works beyond our systematic review.”
Artificial intelligence to support clinical decision-making processes
García‐Vidal, C., Sanjuan, G., Puerta‐Alcalde, P., Moreno-García, E., & Soriano, À. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.07.019 · Cited by 150 · see citing works
Oort, F. J., Visser, M. R. M., & Sprangers, M. A. G. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.03.013 · Cited by 123 · cited 1× · see citing works
“25 Incorrect or biased labels resulting from coding errors or subjective interpretations by annotators can also impact the performance and validity of the machine learning models.”
Distinguishing Selection Bias and Confounding Bias in Comparative Effectiveness Research
Haneuse, S. (2013) · Medical Care · doi:10.1097/mlr.0000000000000011 · Cited by 94 · cited 2× · see citing works
“24 Confounding bias could be introduced when collecting data as well as training models.”
Temporal bias in case-control design: preventing reliable predictions of the future
Yuan, W., Beaulieu‐Jones, B. K., Yu, K., Lipnick, S., Palmer, N., Loscalzo, J., Cai, T., & Kohane, I. S. (2021) · Nature Communications · doi:10.1038/s41467-021-21390-2 · Cited by 84 · cited 1× · see citing works
“53,54 No study has yet developed a comprehensive pipeline to address more than 2 types of bias simultaneously.”
Juhn, Y. J., Ryu, E., Wi, C.-I., King, K. S., Malik, M., Romero-Brufau, S., Weng, C., Sohn, S., Sharp, R. R., & Halamka, J. D. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac052 · Cited by 83 · cited 7× · see citing works
“Bias detection in this review primarily relied on analyzing performance differences among groups, with 1 study introducing the HOUSE index 27 as an innovative feature to better capture socioeconomic status and related biased features in the original data.”
InfluentialEvaluating and mitigating bias in machine learning models for cardiovascular disease prediction
Li, F., Wu, P., Ong, H. H., Peterson, J. F., Wei, W., & Zhao, J. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104294 · Cited by 83 · cited 5× · see citing works
“5,28–30, 32, 33 ,”
InfluentialPeeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model.
Röösli, E., Bozkurt, S., & Hernandez-Boussard, T. (2022) · Scientific Data · doi:10.1038/s41597-021-01110-7 · Cited by 73 · see citing works
Prevention of Bias and Discrimination in Clinical Practice Algorithms
Shachar, C., & Gerke, S. (2023) · JAMA · doi:10.1001/jama.2022.23867 · Cited by 53 · cited 1× · see citing works
“There is an urgent need to thoroughly evaluate possible biases produced by AI models, 47 and assess the fairness of AI meth-ods to reduce healthcare disparities.”
Allen, A. O., Mataraso, S. J., Siefkas, A. C., Burdick, H., Braden, G. L., Dellinger, R. P., McCoy, A., Pellegrini, E., Hoffman, J. L., Green‐Saxena, A., et al. (2020) · JMIR Public Health and Surveillance · doi:10.2196/22400 · Cited by 51 · cited 3× · see citing works
“4,5,27–30, 33, 34, 36 Figure 2 demonstrates the EHR-based AI application development stages, potential biases at each stage and corresponding bias mitigation approaches.”
Calibrated Fairness in BanditsPreprint
Liu, Y., Radanović, G., Christos Dimitrakakis, A., Mandal, D., & Parkes, D. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01875 · Cited by 47 · see citing works
Getz, K., Hubbard, R. A., & Linn, K. A. (2022) · Epidemiology · doi:10.1097/ede.0000000000001578 · Cited by 42 · cited 2× · see citing works
“43 The other studies used datasets derived from individual hospitals or integrated healthcare delivery systems.”
Deep Learning-based Propensity Scores for Confounding Control in Comparative Effectiveness Research
Weberpals, J. G., Becker, T., Davies, J., Schmich, F., Rüttinger, D., Theis, F. J., & Bauer‐Mehren, A. (2021) · Epidemiology · doi:10.1097/ede.0000000000001338 · Cited by 27 · cited 1× · see citing works
“Although confounding and algorithmic biases have been extensively studied in the fields of computer science and statistics, 50–52 their distinct impact on health disparities has not been thoroughly explored when applied to EHR data.”
Fairness in the prediction of acute postoperative pain using machine learning models
Davoudi, A., Sajdeya, R., Ison, R., Hagen, J. E., Rashidi, P., Price, C. C., & Tighe, P. J. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.970281 · Cited by 25 · cited 3× · see citing works
“4,5,27–30, 33, 34, 36 Figure 2 demonstrates the EHR-based AI application development stages, potential biases at each stage and corresponding bias mitigation approaches.”
A transformer-based deep learning approach for fairly predicting post-liver transplant risk factors
Li, C., Jiang, X., & Zhang, K. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104545 · Cited by 25 · cited 3× · see citing works
“…diseases’ diagnosis or risk prediction, 27–31 treatment effect or disease progression prediction, 4,32–35 mortality or survival prediction, 5,36–39 medication or test usage prediction, diseases’ risk-association prediction, 40,41 health status classification, 42 and EHR miss-ingness imputation.”
Wang, H., Li, Y., Naidech, A. M., & Luo, Y. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01871-0 · Cited by 24 · cited 1× · see citing works
“27,31,35,38,39 only detected the presence of bias in AI models by introducing quantitative measurement to identify and explain source of bias, as listed in Table 2.”
A multi-step approach to managing missing data in time and patient variant electronic health records
Cesare, N., & Were, L. P. O. (2022) · BMC Research Notes · doi:10.1186/s13104-022-05911-w · Cited by 14 · see citing works
Wolk, D. M., Lanyado, A., Tice, A. M., Shermohammed, M., Kinar, Y., Goren, A., Chabris, C. F., Meyer, M. N., Shoshan, A., & Abedi, V. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11154342 · Cited by 14 · cited 3× · see citing works
“5,28–30, 32, 33 ,”
Bipartite Ranking Fairness Through a Model Agnostic Ordering Adjustment
Cui, S., Pan, W., Zhang, C., & Wang, F. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3290949 · Cited by 12 · cited 2× · see citing works
“29,31,37,41,44 Three studies published in 2023 37,41,44 indicate an increasing trend in addressing the generalizability and robustness of bias handling.”
Natural language processing to identify reasons for sex disparity in statin prescriptions
Witting, C., Azizi, Z., Gomez, S. E., Zammit, A., Sarraju, A., Ngo, S., Hernandez-Boussard, T., & Rodriguez, F. (2023) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2023.100496 · Cited by 10 · cited 1× · see citing works
“Two recent studies in 2023 56,57 that aimed to develop AI methods that directly detect and elucidate more nuanced bias in EHR data.”
Yin, K., Qian, D., & Cheung, W. K. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3321738 · Cited by 10 · cited 1× · see citing works
“29,31,37,41,44 Three studies published in 2023 37,41,44 indicate an increasing trend in addressing the generalizability and robustness of bias handling.”
Hayakawa, T., Nagashima, T., Akimoto, H., Minagawa, K., Takahashi, Y., & Asai, S. (2023) · Digital Health · doi:10.1177/20552076231178577 · Cited by 6 · cited 1× · see citing works
“Although confounding and algorithmic biases have been extensively studied in the fields of computer science and statistics, 50–52 their distinct impact on health disparities has not been thoroughly explored when applied to EHR data.”
Lee, T., Wollstein, G., Madu, C. T., Wronka, A., Zheng, L., Zambrano, R., Schuman, J. S., & Hu, J. (2023) · Translational Vision Science & Technology · doi:10.1167/tvst.12.12.2 · Cited by 5 · see citing works
Timeline Registration for Electronic Health Records.
Jiang, S., Han, R., Chakrabarty, K., Page, D., Stead, W. W., & Zhang, A. R. (2023) · PubMed Central · Cited by 3 · see citing works
A survey on bias in machine learning researchPreprint
Mikołajczyk, A., & Grochowski, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.11254
Flagged first, then by citations
Research works printed but not linked: 18
- Hee K. Is data quality enough for a clinical decision?: apply machine learning and avoid bias. In: 2017 IEEE International Conference on Big Data (Big Data). IEEE; 2017:2612-2619.
- Aghaei S, Azizi MJ, Vayanos P. Learning optimal and fair decision trees for non-discriminative decision-making. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence and 31st Innovative Applications of Artificial Intelligence Conference and 9th AAAI Symposium on Educational Advances in Artificial Intel- ligence. AAAI Press; 2019:175.
- Johnson LC, Beaton R, Murphy S, Pike K. Sampling bias and other methodological threats to the validity of health survey research. Int J Stress Manag. 2000;7(4):247-267.
- Khoshnevisan F, Chi M. An adversarial domain separation framework for septic shock early prediction across EHR systems. In: 2020 IEEE International Conference on Big Data (Big Data). IEEE; 2020:64-73.
- Karlsson I, Bostr€ om H, eds. Handling sparsity with random forests when predicting adverse drug events from electronic health records. In: 2014 IEEE International Conference on Healthcare Informatics. IEEE; 2014.
- Zhu X, Hurtado J, Tao H. Localized sampling for hospital re- admission prediction with imbalanced sample distributions. In: International Joint Conference on Neural Networks (IJCNN). IEEE; 2017:4571-4578.
- Raza S, Bashir SR, eds. Auditing ICU readmission rates in an clini- cal database: an analysis of risk factors and clinical outcomes. In: 2023 IEEE 11th International Conference on Healthcare Infor- matics (ICHI). IEEE; 2023.
- Huda NM. Design of Istitaah classification system based on machine learning using imbalanced dataset. In: 7th International Conference on Cyber and IT Service Management (CITSM). Vol. 7. IEEE; 2019:1-6.
- Dwork C, Hardt M, Pitassi T, et al., eds. Fairness through aware- ness. In: Proceedings of the 3rd Innovations in Theoretical Com- puter Science Conference. 2012:214-226.
- Mi X, Tighe PJ, Zou F, Zou B. 2020. A deep learning semiparametric regression for adjusting complex confounding structures. Ann Appl Stat. 2021;15(3):1086-1100.
- Jun I, Ser S, Xu J, et al., eds. Quantification of racial disparity on uri- nary tract infection recurrence and treatment resistance in Florida using algorithmic fairness methods. In: 2023 IEEE 11th Interna- tional Conference on Healthcare Informatics (ICHI). IEEE; 2023.
- Feldman M, Friedler SA, Moeller J, et al. Certifying and removing disparate impact. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Min- ing. 2015:259-268.
- Celis LE, Keswani V. Improved adversarial learning for fair classi- fication. arXiv preprint arXiv:1901.10443. 2019. · arXiv
- H� ebert-Johnson U, Kim M, Reingold O, Rothblum G, eds. Multi- calibration: calibration for the (computationally-identifiable) masses. In: International Conference on Machine Learning. PMLR; 2018:1939-1948.
- Liu LT, Simchowitz M, Hardt M, eds. The implicit fairness crite- rion of unconstrained learning. In: International Conference on Machine Learning. PMLR; 2019:4051-4060.
- Hardt M, Price E, Srebro N. Equality of opportunity in supervised learning. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. Curran Associates Inc.; 2016:3323-3331.
- Iosifidis V, Fetahu B, Ntoutsi E. FAE: a fairness-aware ensemble framework. In: 2019 IEEE International Conference on Big Data (Big Data). IEEE; 2019:1375-1380.
- Valera I, Singla AK, Rodriguez MG. Enhancing the accuracy and fairness of human decision making. Adv Neural Inf Process Syst. 2018;31:1769-1778.
Cited by
Sorted by citations
Saeidnia, H. R., Fotami, S. G. H., Lund, B. D., & Ghiasi, N. (2024) · Social Sciences · doi:10.3390/socsci13070381 · Cited by 202 · see citing works
“Future research should aim to overcome these limitations to provide a more comprehensive understanding of ethical considerations in AI-based mental health interventions and to inform responsible practice and policy development.”
Bouguettaya, A., Stuart, E. M., & Aboujaoude, E. N. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01746-4 · Cited by 53 · see citing works
Transparency of medical artificial intelligence systems
Kim, C., Gadgil, S. U., & Lee, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00363-w · Cited by 58 · see citing works
“Detailed documentation allows developers to mitigate potential biases during model training using computational techniques, even if biases are discovered after data collection 17,66,67 .”
Jamshidi, S., Nikanjam, A., Nafi, K. W., Khomh, F., & Rasta, R. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101531 · Cited by 55 · see citing works
Mei, P., Brewis, D. N., Nwaiwu, F., Sumanathilaka, D., Alva-Manchego, F., & Demaree-Cotton, J. (2025) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2025.100140 · Cited by 50 · see citing works
“Thus, its responses can reflect the subjective leanings originated from its data and embedded in its training, potentially skewing responses in subtle yet significant ways (Chen et al., 2024).”
Collaborative, T. C. (2025) · BMJ · doi:10.1136/bmj-2024-083305 · Cited by 50 · see citing works
“54 58 As with other performance assessments, results should be quantified where possible to facilitate comparative analyses.”
Artificial intelligence tool development: what clinicians need to know?
Chew, B.-H., & Ngiam, K. Y. (2025) · BMC Medicine · doi:10.1186/s12916-025-04076-0 · Cited by 41 · see citing works
“This includes creating synthetic data based on the electronic health records, omics data and bioimages to train diagnostic and predictive models [102, 103].”
Exploring Artificial Intelligence Biases in Predictive Models for Cancer Diagnosis
Smiley, A., Reátegui-Rivera, C. M., Villarreal‐Zegarra, D., Escobar-Agreda, S., & Finkelstein, J. (2025) · Cancers · doi:10.3390/cancers17030407 · Cited by 37 · see citing works
“On the other hand, other types of biases are general across different AI models in healthcare, including implicit bias, selection bias, measurement bias, confounding bias, algorithmic bias, and temporal bias [9].”
Biases in AI: acknowledging and addressing the inevitable ethical issues
Hofmann, B. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1614105 · Cited by 44 · see citing works
Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics
Sufian, M. A., Alsadder, L., Hamzi, W., Zaman, S., Sagar, A. S. M. S., & Hamzi, B. (2024) · Diagnostics · doi:10.3390/diagnostics14232675 · Cited by 38 · see citing works
The ethics of data mining in healthcare: challenges, frameworks, and future directions
Ahmed, M. M., Okesanya, O. J., Oweidat, M. H., Othman, Z. K., Musa, S. S., & Lucero‐Prisno, D. E. (2025) · BioData Mining · doi:10.1186/s13040-025-00461-w · Cited by 41 · see citing works
“A study found that fewer than a quarter of the 20 included studies reported any fairness metric and most failed to analyze performance across demographic subgroups [72].”
Leveraging Large Language Models for Enhancing Safety in Maritime Operations
Miller, T. I., Durlik, I., Kostecka, E., Łobodzińska, A., Łazuga, K., & Kozlovska, P. (2025) · Applied Sciences · doi:10.3390/app15031666 · Cited by 31 · see citing works
Riley, B. K., & Dixon, A. L. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101833 · Cited by 28 · see citing works
Cofré, S., Sanchez, C., Quezada-Figueroa, G., & López-Cortés, X. A. (2025) · British Journal Of Nutrition · doi:10.1017/s0007114525000522 · Cited by 28 · see citing works
Sousa‐Pinto, B., Marques‐Cruz, M., Neumann, I., Chi, Y., Nowak, A. J., Reinap, M., Awad, M., Nothacker, M., Trucl, M., Brożek, J., et al. (2025) · Annals of Internal Medicine · doi:10.7326/annals-24-02338 · Cited by 27 · see citing works
“For example, these outputs can re fl ect disparities in access to care, differences of attention to speci fi c health conditions, or differential measurement errors across speci fi c population subgroups (20).”
Abogunrin, S., Muir, J. M., Zerbini, C., & Sarri, G. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1454245 · Cited by 27 · see citing works
Navigating Uncertainty: A User-Perspective Survey of Trustworthiness of AI in Healthcare
Ojha, J., Presacan, O., Lind, P. G., Monteiro, E., & Yazidi, A. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3716317 · Cited by 28 · see citing works
“By acknowledging and addressing these uncertainties, we can strive for a more reliable and trustworthy integration of AI in healthcare practices [9].”
From Genomics to AI: Revolutionizing Precision Medicine in Oncology
Calvino, G., Farro, J., Zampatti, S., Peconi, C., Megalizzi, D., Trastulli, G., Andreucci, S., Cascella, R., Strafella, C., Caltagirone, C., et al. (2025) · Applied Sciences · doi:10.3390/app15126578 · Cited by 30 · see citing works
Giri, R., Firdhos, S. H., & Vida, T. A. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14196900 · Cited by 24 · see citing works
“Discussions in medical ethics and law suggest that responsibility in AI-administered anesthesia may extend to multiple stakeholders, including software developers, hospital systems, and the supervising anesthesiologist [55].”
Automatic Classification of Cardiac Arrhythmias Using Deep Learning Techniques: A Systematic Review
Vásquez-Iturralde, F., Flores-Calero, M. J., Grijalva, F., & Rosales, A. (2024) · IEEE Access · doi:10.1109/access.2024.3408282 · Cited by 23 · see citing works
A roadmap to implementing machine learning in healthcare: from concept to practice
Yan, A. P., Guo, L. L., Inoue, J., Arciniegas, S. E., Vettese, E., Wolochacz, A., Crellin‐Parsons, N., Purves, B., Wallace, S., Patel, A., et al. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1462751 · Cited by 24 · see citing works
Rochon, M., Tanner, J., Jurkiewicz, J., Beckhelling, J., Aondoakaa, A. S., Wilson, K., Dhoonmoon, L., Underwood, M., Mason, L., Harris, R., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0315384 · Cited by 21 · see citing works
Teles, A. S., de Moura, I. R., Silva, F., Roberts, A., & Stahl, D. R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102981 · Cited by 22 · see citing works
Biases in Artificial Intelligence Application in Pain Medicine
Jumreornvong, O., Perez, A., Malave, B., Fatimah, M., Kia, A., & Nwaneshiudu, C. A. (2025) · Journal of Pain Research · doi:10.2147/jpr.s495934 · Cited by 21 · see citing works
“62,63 Finally, equitable communication strategies tailored to vulnerable populations can enhance patient care, while machine learning models trained on diverse data can improve the accuracy of pain diagnoses and treatment recommendations, reducing disparities among those most affected by bias.”
Albergante, L., O’Flynn, C., & Meyer, G. D. (2025) · Journal of the American Veterinary Medical Association · doi:10.2460/javma.24.09.0617 · Cited by 16 · see citing works
“75,76 Overall, while progress is being made, the op - portunities provided by AI have not yet moved the needle significantly for early disease detection in the clinic.”
Loaiza‐Bonilla, A., Thaker, N. G., Chung, C. D., Parikh, R. B., Stapleton, S., & Borkowski, P. (2025) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-25-100048 · Cited by 16 · see citing works
Machine learning in dentistry: a scoping review
Lakhotia, S., Godrej, H., Kaur, A., Nutakki, C. S., Mun, M., Eber, P., & Celi, L. A. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000940 · Cited by 16 · see citing works
“Our findings are consistent with previous reviews that highlight the growing use of deep learning in medical imaging, but also indicate shortcomings in bias reporting [13] and calibration performance evaluation [14].”
Horsfall, L., Bondaronek, P., Ive, J., & Poduval, S. (2025) · Clinical Epidemiology · doi:10.2147/clep.s527000 · Cited by 14 · see citing works
“83,86 Preprocessing Preprocessing refers to general data cleaning and the conversion of raw data into a format appropriate for analysis.”
InfluentialUse 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 · see citing works
“Bias is engrained in EHRs, the most common data source for AI recruitment, by who receives care at the healthcare entity, insurance status, location, and choice of language, all of which will impact AI-based natural language processing (Verheij et al. 2018; Harton et al. 2022; Chen et al. 2024).”
Kababji, S. E., Mitsakakis, N., Jonker, E., Beltran-Bless, A.-A., Pond, G. R., Vandermeer, L., Radhakrishnan, D., Mosquera, L., Paterson, A. H. G., Shepherd, L. E., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/66821 · Cited by 15 · see citing works
MLHOps: Machine Learning Health Operations
Khattak, F. K., Subasri, V., Krishnan, A., Pou-Prom, C., Koçak, S. A., Dolatabadi, E., Pandya, D., Seyyed-Kalantari, L., & Rudzicz, F. (2024) · IEEE Access · doi:10.1109/access.2024.3521279 · Cited by 24 · see citing works
“…focus on developing standardized evaluation frameworks that include metrics and benchmark datasets for assessing the bias in the models [11], [51], [94]. b) Regulatory guidelines: Regulatory guidelines should be established to ensure and prioritize bias evaluation [59], [195]. c) Patient…”
Siddiqui, E. F., Haleem, M., Ahmad, S. F., Salhi, A., Zamani, A. T., & Varish, N. (2025) · IEEE Access · doi:10.1109/access.2025.3610526 · Cited by 11 · see citing works
“If you feed LLM modified data when training then LLM can utilize data poisoning and model manipulation to create biased and incorrect outcomes [51].”
Generative artificial intelligence for literature reviews
Wagner, G., Prester, J., Mousavi, R., Lukyanenko, R., & Paré, G. (2026) · Journal of Information Technology · doi:10.1177/02683962261425675 · Cited by 15 · see citing works
“This work can leverage growing research on AI data bias identi fi cation and mitigation (Chen et al., 2024; Nazer et al., 2023; Tejani et al., 2024).”
What Is Fair? Defining Fairness in Machine Learning for Health
Gao, J., Chou, B., McCaw, Z. R., Thurston, H., Varghese, P., Hong, C., & Gronsbell, J. L. (2025) · Statistics in Medicine · doi:10.1002/sim.70234 · Cited by 14 · see citing works
“Similarly, a review of EHR-based prediction models found that most studies investigate the over-all performance of ML models, but do not interrogate potential biases [34].”
Zimmerman, R. M., Hernandez, E. J., Yandell, M. D., Tristani‐Firouzi, M., Silver, R. M., Grobman, W. A., Haas, D. M., Saade, G. A., Steller, J. G., & Blue, N. R. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-024-07095-6 · Cited by 12 · see citing works
Acosta-Jiménez, S., Mendoza-Mendoza, M. M., Galvan-Tejada, C. E., Galván-Tejada, J. I., Celaya-Padilla, J. M., García-Domínguez, A., Gamboa-Rosales, H., & Solís-Robles, R. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00497-8 · Cited by 9 · see citing works
“Not only does this affect the accuracy of artificial intelligence models, but it can also perpetuate disparities in medical care (Chen et al. 2024; Celi et al. 2022).”
Preparing Allergists to Practice in 2050 Using Artificial Intelligence
Khoury, P., Oppenheimer, J., Bunyavanich, S., Ciaccio, C. E., & Portnoy, J. M. (2025) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2025.09.012 · Cited by 10 · see citing works
AI for all: bridging data gaps in machine learning and health
Wang, M. L., & Bertrand, K. A. (2025) · Translational Behavioral Medicine · doi:10.1093/tbm/ibae075 · Cited by 10 · see citing works
Integrating New Technologies in Lipidology: A Comprehensive Review
Escobar, C., García, J. S., Torremocha, A., Lorenzo, C. C., Lara-García, A., Muñoz, L. C., González, R. M., García, J. V., & Banach, M. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14144984 · Cited by 9 · see citing works
Sridhar, D., Manikandan, R., Dhanapal, Y., Khute, S., & Subash, P. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2025.08.001 · Cited by 8 · see citing works
Destination (Un)Known: Auditing Bias and Fairness in LLM-Based Travel Recommendations
Andreev, H., Kosmas, P., Livieratos, A. D., Theocharous, A., & Zopiatis, A. (2025) · Applied Informatics · doi:10.3390/ai6090236 · Cited by 6 · see citing works
“It includes sampling bias, where some groups are more likely to be included than others [27,29,30]; labelling bias, where annotator judgements alter ground truth [27] and statistical bias, where historical patterns embed pre-existing inequalities that the model reproduces [27,31].”
Artificial Intelligence and Health Equity
Bright, T. J., & Norris, K. C. (2025) · Annual Review of Medicine · doi:10.1146/annurev-med-043024-125309 · Cited by 8 · see citing works
Samra, R. M. A., & Alí, R. R. (2026) · AgriEngineering · doi:10.3390/agriengineering8070270 · Cited by 8 · see citing works
“AI models may exhibit bias due to imbalanced datasets or regional variability [77].”
Death risk prediction model for patients with non-traumatic intracerebral hemorrhage
Chen, Y., Liu, X., Yan, M., & Wan, Y. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02865-4 · Cited by 8 · see citing works
Fair streaming feature selection
Duan, Z., Li, T., Ling, Z., Wu, X., Yang, J., & Jia, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129394 · Cited by 7 · see citing works
Managing Transparency Fairness Accountability in AI for Sustainable Human Resource Management
Setiawati, R., Kusmara, A., & Kuusk, T. (2025) · International Conference on Creative Communication and Innovative Technology (ICCIT) · doi:10.1109/iccit65724.2025.11166968 · Cited by 8 · see citing works
“While they enhance efficiency and reduce human error, they also pose risks of algorithmic, implicit, and selection bias if trained on flawed or unrepresentative data [29].”
Hassoon, A., Lin, C. F., Woo, H., Irimia, R. M., Marsteller, J. A., Li, A., Banderia, A., Leo, H., Peng, X., Rastall, D. P. W., et al. (2025) · Annals of Epidemiology · doi:10.1016/j.annepidem.2025.11.004 · Cited by 8 · see citing works
A community-based approach to ethical decision-making in artificial intelligence for health care
Senghor, A. S., Bright, T. J., Kakim, S., Norris, K. C., Antwi, H. A., Cooper, J. K., Mullins, C. D., & Baquet, C. R. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf076 · Cited by 6 · see citing works
“9,14 Additionally, healthcare organizations must critically assess how biases in clinical data influence AI predictions, ensuring that models do not reinforce existing disparities in care delivery.”
The value alignment problem in advisory AI: a systematic literature review
Triantafyllopoulos, L., Paxinou, E., Tzanoulinou, D., Verykios, V. S., & Kalles, D. (2026) · AI and Ethics · doi:10.1007/s43681-026-01015-4 · Cited by 6 · see citing works
“This shift reflects a tran - sition from primarily conceptual discussions of alignment toward technically oriented experimentation with LLMs and alignment methods such as RLHF [39, 97] and DPO [10–12, 28, 89].”
Explainable artificial intelligence for multi-modal cancer analysis: from genomics to immunology
Zhang, M., & Yin, L. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.105040 · Cited by 5 · see citing works
Dantas, C., Cabrita, M., Midão, L., Carvalho, A. S., & Costa, E. M. (2025) · AI and Ethics · doi:10.1007/s43681-025-00777-7 · Cited by 5 · see citing works
“…achieve a broad standpoint of analy - sis and a holistic knowledge of the policy, technological, research and user perspectives needed to implement real - istic solutions, adjusted to the citizens’ needs and fit to be integrated into national and international policies and work - flows [ 17 , 18].”
Navigating Fairness in AI-based Prediction Models: Theoretical Constructs and Practical ApplicationsPreprint
van der Meijden, S. L., Wang, Y., Arbous, M. S., Geerts, B. F., Steyerberg, E. W., & Hernandez‐Boussard, T. (2025) · medRxiv · doi:10.1101/2025.03.24.25324500 · Cited by 5 · see citing works
“However, group fairness requires high-quality data on protected attributes like race and ethnicity [15, 16].”
The Elusive B Cell Antigen in Multiple Sclerosis: Time to Rethink CNS B Cell Functions
Mailaender, F., Vasilenko, N., Tieck, M. P., Schembecker, S., & Kowarik, M. C. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262110771 · Cited by 4 · see citing works
Through a Teacher’s Lens: Combating Bias in AI-Powered Education for a Just Future
Poornesh, M. (2024) · The Clearing House A Journal of Educational Strategies Issues and Ideas · doi:10.1080/00098655.2024.2393153 · Cited by 5 · see citing works
Bridging fair-aware artificial intelligence and co-creation for equitable mental healthcare
Timmons, A. C., Duong, J. B., Walters, S. N., Carta, K. E., Jumonville, G. A., Carrasco, A. S., Romero, D., Ahle, M. W., Comer, J. S., Khurd, I. P., et al. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-025-00491-5 · Cited by 6 · see citing works
“With the rapid adoption of AI-based mental health interventions, frameworks that ensure equitable and effective outcomes are urgently needed 16,21–24 (Fig.”
Information Distortion in Electronic Health Records: A Concept Analysis
Wang, J., Xu, Y., Zhang, R., Yang, Z., Zhang, X., & Pan, H. (2025) · Journal of Advanced Nursing · doi:10.1111/jan.70233 · Cited by 3 · see citing works
Mitigating Ethical Issues for Large Language Models in Oncology: A Systematic Review
Zhou, S., Liu, X., Xu, Z., Zhan, Z., Song, M., Wang, J., Liu, S., Xu, H., & Zhang, R. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00076 · Cited by 5 · see citing works
Raising awareness of potential biases in medical machine learning: Experience from a Datathon
Hochheiser, H., Klug, J., Mathie, T., Pollard, T., Raffa, J. D., Ballard, S. L., Conrad, E. A., Edakalavan, S., Joseph, A. M., Alnomasy, N., et al. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000932 · Cited by 4 · see citing works
Clinical Applications of Artificial Intelligence in Neurology Practice
Hadar, P., & Moura, L. M. V. R. (2025) · CONTINUUM Lifelong Learning in Neurology · doi:10.1212/cont.0000000000001552 · Cited by 4 · see citing works
Automating clinical phenotyping using natural language processing
Schmidt, L., Ibing, S., Borchert, F., Hugo, J., Marshall, A. A., Peraza, J., Cho, J. H., Böttinger, E. P., Renard, B. Y., & Ungaro, R. C. (2026) · Communications Medicine · doi:10.1038/s43856-025-01337-0 · Cited by 4 · see citing works
CLE-SH: Comprehensive Literal Explanation Package for SHapley Values by Statistical Validity
Kim, K., Lee, Y., & Seo, J. (2026) · IEEE Access · doi:10.1109/access.2026.3654890 · Cited by 3 · see citing works
“In these areas, datasets are often vulnerable to data bias, meaning high performance alone is not enough to validate the use of machine learning [1, 2, 3].”
Masison, J., Lehmann, H. P., & Wan, J. (2024) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2024.08.025 · Cited by 3 · see citing works
Ethical Artificial Intelligence and Machine Learning in Self-Service Technologies
Ghosh, R., Çela, E., Chakraborty, S., Gupta, B., Dutta, G., Chakraborty, S., & Roy, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1295-8.ch007 · Cited by 3 · see citing works
Akinniyi, O., Dixon, J., Aina, J., Weaks, F., Saleh, G. A., Rahman, M. M., Meeker, T. J., Trivedi, H., Gichoya, J. W., & Khalifa, F. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109036 · Cited by 3 · see citing works
Digital Mental Health Through an Intersectional Lens: A Narrative Review
Yesha, R., Orezzoli, M. C. E., Sims, K., & Landau, A. Y. (2026) · Healthcare · doi:10.3390/healthcare14020211 · Cited by 3 · see citing works
“Several forms of bias can arise throughout the lifecycle of AI systems, including during data collection, clinical documentation, model design, and implementation, which perpetuate existing disparities in mental health care [34–37].”
Hooshmandi, S., Tabatabaii, M., Arani, H. Z., Heidarian, A., & Nazemi, S. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-00843-x · Cited by 3 · see citing works
Addressing Bias and Ensuring Fairness in AI Systems for Healthcare
Zarnegar, A. (2025) · Health Informatics and Knowledge Management Conference (HIKM) · doi:10.1145/3774816.3774817 · Cited by 3 · see citing works
Mitigating Algorithmic Bias in Cancer Site Classification Models
Shivanna, A., Spannaus, A., Tschida, J., Gounley, J. P., Krawczuk, P., & Hanson, H. A. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00250 · Cited by 3 · see citing works
Wang, H. J., Shi, G., Liu, X., Zhang, J., Chen, S., Shi, J., Li, Y., Yue, G., & Wu, Y. (2026) · Expert Systems · doi:10.1111/exsy.70259 · Cited by 4 · see citing works
Artificial Intelligence Across the Drug Development Lifecycle
Demyashkin, G. A., Алексеевич, П. М., Zyryanov, S., Yavorskiy, A., Shegai, P., & Kaprin, A. (2026) · Medical Sciences · doi:10.3390/medsci14020248 · Cited by 4 · see citing works
“The risk of AI models amplifying errors or pre-existing biases in their training data is a significant concern, raising questions about the generalizability of insights across diverse patient populations and the ethical implications of biased outcomes [249,250].”
Kansal, M., Sibal, R., & Ram, P. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-77029-6_8 · Cited by 2 · see citing works
Applying Artificial Neural Networks to Estimate PMI: A New Era of Accuracy in Forensic Science
Ouanes, K. (2025) · Springer eBooks · doi:10.1007/978-981-96-4585-5_11 · Cited by 2 · see citing works
Efthymiou, E., Katsarou, D. V., Sofologi, M., Megari, K., Papadopoulou, S. L., Mantsos, E., Argyriadis, A., & Argyriadi, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0573-8.ch009 · Cited by 2 · see citing works
Nejadshamsi, S., Chu, C. H., McGilton, K. S., Li, X., Ronquillo, C., & Rahimi, S. A. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf171 · Cited by 2 · see citing works
“Chen et al. 26 investigated the impact of preprocessing methods such as reweighting and synthetic oversampling, which were shown to improve fairness in electronic health record (EHR)-based models without significantly compromising predictive accuracy.”
Abouelmagd, K., Saleem, N., Kuhn, K., Oyedele, T. J., Abdelbar, S. M. M., Rath, S., & Alsabri, M. (2025) · Current Emergency and Hospital Medicine Reports · doi:10.1007/s40138-025-00334-w · Cited by 2 · see citing works
“The majority of existing AI models are trained on data from adult populations and high-income countries (HICs), severely limiting their generalizability to the unique anatomical and physiological characteristics of children, particularly neonates and infants [50, 51].”
Bouterse, A., Pruneski, J. A., Oettl, F. C., Zsidai, B., Tischer, T., Longo, U. G., Seil, R., Hirschmann, M. T., & Samuelsson, K. (2026) · Knee Surgery Sports Traumatology Arthroscopy · doi:10.1002/ksa.70339 · Cited by 2 · see citing works
“Additionally, the integrity of AI models is dependent on the data they are trained on, allowing imbalanced or incomplete datasets to introduce algorithmic bias into decision ‐ making models that in fl uence aspects of clinical care [15, 17, 36, 48, 53, 73].”
Lim, Y. X., Ming, L. C., Ng, N. C., Leow, S., & Ingle, R. G. (2026) · Discover Computing · doi:10.1007/s10791-026-10205-x · Cited by 2 · see citing works
“Where imbalances exist, technical fixes can be applied, such as oversampling underrepresented groups, undersampling majority groups, reweighting data points to give more importance to minority samples, or using data augmentation to synthetically create more data for sparse classes [79, 81, 82] [–].”
The BEETS framework for responsible artificial intelligence
Tettegah, S. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01597-2 · Cited by 3 · see citing works
“The literature on artificial intelligence (AI) often focuses on key themes such as security, ethics, equity, bias, trust, and fairness [11, 19].”
Zhao, Q., Jiang, W., Zhang, C., Ouyang, M., Wu, N., & Guo, Y. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1863050 · Cited by 2 · see citing works
“This missing pattern reflects structural inequalities such as digital access barriers or specific employment systems, which go beyond the scope of random technical failures (83, 84).”
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 · see citing works
Accuracy of administrative data in ascertaining health conditions: a systematic review
Campbell, A. C., Tyler, J., Shuttleworth, R. R., Pearce, L. A., Heerde, J. A., Borschmann, R., Sawyer, S. M., Preen, D. B., Kinner, S. A., & Calais‐Ferreira, L. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag109 · Cited by 2 · see citing works
“52 When the underlying codes have variable accuracy, particularly for sensitivity and PPV, these models risk inheriting and amplifying biases in clinical predictions and resource allocation that are difficult to detect and correct once embedded in automated processes.”
AI in the Team Meeting Landscape
Allen, J. A., Grabowski, M., Thiese, M. S., & Eden, E. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/s1534-085620260000021010 · Cited by 2 · see citing works
Rudroff, T. (2025) · Infectious Disease Reports · doi:10.3390/idr17020033 · Cited by 2 · see citing works
“, and metabolic indicators, to generate comprehensive risk assessments [59].”
Assessing the risk of discriminatory bias in classification datasets
Dai, K., Kim, J., Džeroski, S., Wicker, J. S., Dobbie, G., & Dost, K. (2025) · Machine Learning · doi:10.1007/s10994-025-06843-9 · Cited by 2 · see citing works
“This approach is particularly problematic if the practitioner is unaware of the bias; even more so in numeric datasets such as in healthcare data, where patients are defined by their age and numeric lab results rather than their gender or race (Chen et al., 2024).”
Narasannagari, L., & Bhardwaj, D. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9521-9.ch003 · Cited by 2 · see citing works
Moving forward on the science of informatics and predictive analytics
Henry, S. B. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae077 · Cited by 2 · see citing works
AI-Powered clinical assessments: GPT-4o’s role in standardizing CPR skill evaluations
Semeraro, F. F. (2024) · Resuscitation · doi:10.1016/j.resuscitation.2024.110411 · Cited by 2 · see citing works
Frasier, K. M., Hash, M. G., & Pugliese, A. (2025) · Cureus · doi:10.7759/cureus.79109 · Cited by 1 · see citing works
“This is often due to biases in the training data or differences between the model’s training population and real-world application, underscoring the need for further refinement in predictive algorithms [16].”
Demiral, Y. E., & Alsahag, A. M. M. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6389-7_32 · Cited by 1 · see citing works
Muñoz-García, V., Consuegra‐Ayala, J. P., & Moreda, P. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113915 · Cited by 1 · see citing works
Emerging Trends in Early Dementia Diagnosis: An Analysis on Advanced Machine Learning Approaches
Gami, B., Agrawal, M., & Katarya, R. (2025) · ACM Computing Surveys · doi:10.1145/3764578 · Cited by 1 · see citing works
“Similarly, Chen et al. [16] suggested systematic bias detection and mitigation techniques.”
Pelka, O., Sigle, S., Werner, P., Schweizer, S. T., Iancu, A., Scherer, L., Kamzol, N. A., Eil, J. H., Apfelbacher, T., Seletkov, D., et al. (2025) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2651-6653 · Cited by 1 · see citing works
Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction
Guermazi, D., Khemchandani, S., Wahood, S., Nguyen, C. V., & Saliba, E. (2025) · Dermatopathology · doi:10.3390/dermatopathology12040042 · Cited by 1 · see citing works
Lee, J. T., Hsu, S. H., Li, V. C.-S., Anindya, K., Chen, M. M., Wang, C. I., Shen, T. K.-B., Liu, V. T. N., Chen, H.-H., & Atun, R. A. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000951 · Cited by 1 · see citing works
“Addressing these gaps requires fairness-enhancing techniques, such as the Disparate Impact Remover, to ensure consistent model accuracy across diverse groups [19,21].”
InfluentialBalancing Innovation and Responsibility: Ethical and Privacy Challenges in Stroke Digital Health
Muchada, M., Rizzo, F., Brunelli, N., & Molina, C. A. (2025) · Stroke · doi:10.1161/strokeaha.125.050450 · Cited by 1 · see citing works
Golovenko, M. Y., & Larionov, V. (2025) · Artificial Intelligence · doi:10.15407/jai2025.04.010 · Cited by 1 · see citing works
A Meta-Analytic NLP-Driven Mapping of Fairness in Healthcare AI
Tsoni, R., Koutouzis, M., & Verykios, V. S. (2025) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa66859.2025.11311222 · Cited by 1 · see citing works
Alzahrani, F., & Alnezary, F. S. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1763446 · Cited by 1 · see citing works
“This ongoing vigilance supports a dynamic, adaptive approach to equity within organizational practice (64, 65).”
Qu, H., Li, J., Gao, Y., Xu, X.-M., Zhang, X.-B., & Yang, S.-D. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1807400 · Cited by 1 · see citing works
AI for TCM: From target identification to drug design
Wang, M., Wang, W., Shen, M., Fan, X., & Liao, J. (2026) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2026.101681 · Cited by 1 · see citing works
- Authors
- Chen, F., Wang, L., Hong, J., Jiang, J., & Zhou, L.
- Affiliations
- Harvard University (US)
- Brigham and Women's Hospital (US)
- Wellesley High School , Wellesley, MA 02481, United States
- Corresponding author
- Chen, Feng
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-03-23
- Volume
- 31
- Issue
- 5
- Pages
- 1172-1183
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC11031231
- OpenAlex
- W4393119757
- Cited by
- 178
- References
- 48
- Citations by year
- 2026: 72 · 2025: 91 · 2024: 15
- FWCI
- 13.55
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Library of Medicine (1R01LM014239)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- algorithmic bias, bias detection, bias mitigation, electronic health records, artificial intelligence in healthcare, health care disparities, fairness metrics, implicit bias, selection bias, confounding, measurement bias, temporal bias, resampling, reweighting, ethical AI
- MeSH terms
- Algorithms, Artificial Intelligence, Female, Humans, Models, Theoretical, Pregnancy, Bias, Selection Bias, Benchmarking, Electronic Health Records
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
