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Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Open accessAbstract
We need to consider the ethical challenges inherent in implementing machine learning in health care if its benefits are to be realized. Some of these challenges are straightforward, whereas others have less obvious risks but raise broader ethical concerns.
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“how principles of algorithmic fairness might apply in clinical decision-making has received little attention in the medical literature7,8.”
From Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities (2020) · cites this paper as [8]
In the citing paper
“As AI becomes more pervasive in both public and personal health across diverse populations, there have been increasing concerns, and related examples, of AI solutions leading to inadvertent bias of modeling results (45–48).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [45]
In the citing paper
“Abramoff et al. proposed ethical frameworks for AI27–31 to help proactively address the issue of undesirable algorithmic bias as well as other concerns with AI.”
From Considerations for addressing bias in artificial intelligence for health equity (2023) · cites this paper as [29]
In the citing paper
“These ethical and other concerns with AI in healthcare have been shown by a number of research studies27–29.”
From Considerations for addressing bias in artificial intelligence for health equity (2023) · cites this paper as [29]
In the citing paper
“Trust between public and healthcare systems is essential for effective healthcare delivery [36].”
From Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020) · cites this paper as [36]
In the citing paper
“Ethical concerns about the use of AI in health care include undermining patient data privacy protections and exacerbating the existing tension between providing care and generating profit, as well as introducing a third party into the patient-doctor relationship, which changes expectations of confidentiality and responsibility (10).”
From Artificial intelligence for global health (2019) · cites this paper as [10]
In the citing paper
“Healthcare data are sensitive information about a person [23, 83] which is also related to personal privacy.”
From Ethics and governance of trustworthy medical artificial intelligence (2023) · cites this paper as [23]
In the citing paper
“However, some technical understanding is imperative for quality and safety assessment and therefore create trust in the AI application’s reliability [8, 21, 30].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [8]
In the citing paper
“AI is expected to cause large changes in clinical work practices and requires complementary skills from radiologists [5, 7, 8].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [8]
In the citing paper
“Similarly, the use of AI‐based solutions may raise several moral questions in medicine 64 , 65 : would we trust computers to screen for disease, prioritize treatment, diagnose, treat, discharge?”
From Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology (2020) · cites this paper as [64]
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Citations
Cites
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
Confidentiality in Medicine — A Decrepit Concept
Siegler, M. A. (1982) · New England Journal of Medicine · doi:10.1056/nejm198212093072411 · Cited by 266
Gijsberts, C. M., Groenewegen, K. A., Hoefer, I. E., Eijkemans, M. J. C., Asselbergs, F. W., Anderson, T. J., Britton, A. R., Dekker, J. M., Engström, G., Evans, G. W., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0132321 · Cited by 197
A ‘Green Button’ For Using Aggregate Patient Data At The Point Of Care
Longhurst, C., Harrington, R. A., & Shah, N. H. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0099 · Cited by 159
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The Role of Digital Transformation in Enhancing Transparency in AI-Assisted Public Administration
Ouboumlik, A., Khafif, H., Touhami, N. O., & Baiza, I. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1737-3.ch007 · Cited by 3
Massey, C., Latif, U., Deer, T. R., Kalia, H., Guirguis, M. N., Bicket, M. C., Khatri, N., Tolba, R., Chakravarthy, K. V., Budwany, R. R., et al. (2025) · Journal of Pain Research · doi:10.2147/jpr.s529465 · Cited by 3
Al-Hgaish, A. M., Atan, R. B., Yaakob, R., & Osman, M. H. (2025) · IEEE Access · doi:10.1109/access.2025.3601031 · Cited by 6
“Such biases can lead to incorrect risk assessments or treatment recommendations [48], [49], perpetuating systemic disparities in healthcare at individual and population levels.”
Manimaran, S., Priya, D. U., Maria, A., & Rajasekaran, A. S. (2025) · Cluster Computing · doi:10.1007/s10586-025-05372-3 · Cited by 4
“Ensuring robust data security frameworks and explainable AI models is critical for building trust and accountability in AI-driven healthcare [117].”
Zhong, E. H., Spector, N. M., O’Hara, C., Livanos, N., & Castillo, J. D. (2025) · Journal of Nursing Regulation · doi:10.1016/j.jnr.2025.08.015 · Cited by 5
The I-KAPCAM-AI-Q: a novel instrument for evaluating health care providers’ AI awareness in Italy
Cofini, V., Piccardi, L., Benvenuti, E., Pangrazio, G. D., Cimino, E., Mancinelli, M., Muselli, M., Petrucci, E., Picchi, G., Palermo, P., et al. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1655659 · Cited by 3
Geroscience, longevity trends, and anti-aging strategies in dogs and cats
Oh, W., & Armstrong, P. (2025) · Journal of Veterinary Science · doi:10.4142/jvs.25221 · Cited by 5
“Algorithms designed to forecast lifespan or frailty risk must be transparent, validated, and avoid misclassification that could bias treatment or end-of-life decisions [68].”
Artificial Intelligence in Ocular Oncology: Differentiating Choroidal Melanocytic Lesions
Harby, L. A., Korot, E., Keane, P. A., Lally, S. E., Ferenczy, S. R., Dalvin, L. A., Pellegrini, M., Duker, J. S., Fung, A. T.-C., Kaliki, S., et al. (2025) · Ophthalmology Science · doi:10.1016/j.xops.2025.100948 · Cited by 4
“112 Clinical validation is, hence, a key aspect of managing trust in this area.”
Xiu, Z., Zeng, W., Guo, J., Tu, G., Chen, X., Sheng, J., & Chen, H. (2025) · Frontiers in Bioscience-Landmark · doi:10.31083/fbl37633 · Cited by 3
“Ethical considerations such as patient privacy and data security, and the potential for algorithmic biases, necessitate careful scrutiny [70].”
AI-Driven Advances in Women’s Health Diagnostics: Current Applications and Future Directions
Macedonia, C. R. (2025) · Diagnostics · doi:10.3390/diagnostics15233076 · Cited by 5
“The rapid adoption of AI raises ethical, legal, and regulatory challenges, especially concerning sensitive women’s health data and underrepresented populations [16,44].”
da Costa, R. L., Pereira, M., Pereira, A. A., Canas, J. M., Correia, R., & Dimande, C. (2025) · Businesses · doi:10.3390/businesses5040044 · Cited by 6
“…the Artificial Intelligence algorithms (Issa et al., 2024), the integrity and quality of the stored data (Panch et al., 2019), the inability to evaluate (Lee & Yoon, 2017), the legal liability (Maassen et al., 2021), and the purpose of the implementation (Pesapane et al., 2018; Char et al., 2018).”
AI for screening in healthcare: promise and challenges
Saito, K., Walston, S. L., Takita, H., Mitsuyama, Y., Arita, Y., & Ueda, D. (2026) · Abdominal Radiology · doi:10.1007/s00261-025-05370-4 · Cited by 3
“Any AI system introduced into a screening program must be rigorously proven to do no harm by not inflating the false-positive rate beyond acceptable limits [39 ].”
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
Vision–language foundation model driven agentic AI systems for healthcare
Chen, L., Xu, X., Qin, Y., Li, H., & Jiang, N. (2026) · The Visual Computer · doi:10.1007/s00371-026-04437-7 · Cited by 3
Protecting clinical value judgment in the age of AI
Patil, S. V., Myers, C. G., & Dai, T. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02561-1 · Cited by 4
Ou, Y., Zhang, Q., Meng, C. K., Zhou, X., Na, M., Yu, Z., Ma, W., & Huang, C. (2026) · Therapeutics and Clinical Risk Management · doi:10.2147/tcrm.s596651 · Cited by 4
“31 A critical gap in current regulatory frameworks is the lack of explicit requirements for reporting overfitting risks and false positive rates, alongside their clinical consequences, in AI model validation studies, which is essential to ensure the safety of AI medical devices for TB care.”
Liu, N., Han, G., Gu, Q., Zhang, Y., & Chen, M. M. (2026) · Cell Death and Disease · doi:10.1038/s41419-026-08769-z · Cited by 4
“This lack of diversity leads to “ data poverty ” , where models fail to generalize to the genetic landscapes and environmental exposures of diverse global populations [262, 267, 268].”
Operational responsibility in AI governance: a user-centric liability framework
Chen, Z. (2026) · AI and Ethics · doi:10.1007/s43681-026-01163-7 · Cited by 5
“The result is a ‘professionally mediated innova - tion’ model [18] in which technology competence becomes an extension of existing professional judgment obligations.”
AI Integration in Spanish Undergraduate Medical Education: National Cross-Sectional Study
Janeiro, A. E., Pereira, K. P., Mayol, J., Crespo, J., Carballo, F., Cabello, J. B., Ramos-Casals, M., Corbacho, B. P., & Turnés, J. (2026) · JMIR Medical Education · doi:10.2196/88511 · Cited by 5
“…demand optimization of health care resources [25], and while AI offers tools to improve diagnostic accuracy, personalize treatments, and reduce clinical errors [26], realizing these benefits requires professionals capable of navigating the ethical and interpretative challenges involved [27].”
Invisibility, cloaks and daggers: Balancing clinical hazards in the age of artificial intelligence
Rose, C. C., & Chan, T. M. (2022) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13758 · Cited by 1
Towards Assessing Data Bias in Clinical Trials
Criscuolo, C., Dolci, T., & Salnitri, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23905-2_5 · Cited by 3
“Unluckily, information on bias is not provided with these datasets, leaving researchers and doctors clueless on the possible impacts on their analysis and with no means to mitigate them [8].”
BackgroundSAĞIROĞLU, E., & Tosun, H. (2023) · Turkiye Klinikleri Journal of Medical Ethics-Law and History · doi:10.5336/mdethic.2022-92760 · Cited by 4
Lee, H., Kang, B. G., Jo, J., Park, H., Yoon, S., Choi, S.-Y., & Kim, M. J. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1167468 · Cited by 4
Zhang, W., Song, L.-N., You, Y.-F., Qi, F.-N., Cui, X.-H., Yi, M., Zhu, G., Chang, R., & Zhang, H. (2024) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v5.i1.90096 · Cited by 3
Coutinho‐Almeida, J., Cruz-Correia, R. J., & Rodrigues, P. P. (2024) · Scientific Reports · doi:10.1038/s41598-024-61371-1 · Cited by 3
Responsible Innovation in E-Health Care: Empowering Patients with Emerging Technologies
Trzmielak, D., Lipka-Matusiak, I., & Oftedal, E. M. (2024) · Marketing of Scientific and Research Organizations · doi:10.2478/minib-2024-0010 · Cited by 3
“Ultimately, it is crucial for results generated by AI to be veri fi ed and approved by experts in the relevant medical fi eld (Char et al., 2018).”
MethodAnalysis of Random Initialization Methods in Machine Learning
Wei, Y., Li, X., Lang, F., Wang, Y. N., & Ma, T. (2024) · Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence (DEAI) · doi:10.1145/3675417.3675484 · Cited by 3
“Random initialization usually refers to the random initialization of model parameters in machine learning or neural networks, in order to learn data features more effectively during the training process [9].”
Edafetanure-Ibeh, F. T. (2024) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri62200.2024.00055 · Cited by 3
“[16] talks about the need for transparency in model building and the possible risks connected to algorithmic bias.”
BackgroundNoshin, K., Boland, M. R., Hou, B., Lu, V., Manning, C. A., Shen, L., & Zhang, A. (2024) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789819807024_0045 · Cited by 3
“7,8 1.3 Uni-variate Association Mining While Xu et al. 6 utilized unsupervised Machine Learning (ML) methods to learn types of ADRD (a form of neurodegenerative disorder (NDD)) from the data itself, another common method for uncovering important features or characteristics of a dataset is to…”
InfluentialScaling behavior of cross-entropy loss in the identification of percolation phase transitions
Li, H., Zhao, Y. S., & Yang, B. (2024) · Physical review. E · doi:10.1103/physreve.110.054133 · Cited by 3
Responsible AI Implementation in Healthcare Organizations
Archana, K. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-6294-5.ch001 · Cited by 3
Ahuja, A., Wairagade, A., & Gupta, N. (2025) · Journal of Artificial Intelligence Machine Learning and Data Science · doi:10.51219/jaimld/ashutosh-ahuja/497 · Cited by 4
AI in HealthTech: Building HIPAA-Compliant Solutions for Next-Generation Medical Documentation
Batista, A. S. (2025) · Journal of Next-Generation Research 5 0 · doi:10.70792/jngr5.0.v1i4.124 · Cited by 3
Grinberg, N., Ianculovici, C., Whitefield, S., Kleinman, S., Feldman, S., & Peleg, O. (2025) · Oral Radiology · doi:10.1007/s11282-025-00836-z · Cited by 3
“These should address issues such as patient safety and delineations of responsibilities between AI models and human clinicians [43, 44].”
Current Perspectives on the Artificial Intelligence in Critical Care Medicine
Lee, J., & Yoon, J. H. (2025) · Anesthesiology Clinics · doi:10.1016/j.anclin.2025.05.005 · Cited by 4
Biswas, D., Aminorroaya, A., Croon, P. M., Batinica, B., Pedroso, A. F., & Khera, R. (2025) · Current Atherosclerosis Reports · doi:10.1007/s11883-025-01337-4 · Cited by 4
“…chest pain, participants randomized to CCTA as compared to usual care had a lower rate of nonfatal myocardial infarc - tion or coronary death at 5 years, which was ascribed to ear - lier use of coronary revascularization and more aggressive use of preventive and anti-anginal therapies [ 64 ].”
Ethical and Legal Issues in the Use of Online-Based Health Services (E-Health)
Damayanti, F. N., Absori, A., Wardiono, K., & Rejeki, S. T. (2020) · SOEPRA · doi:10.24167/shk.v6i1.2626 · Cited by 4
Artificial Intelligence and Big Data in Medicine: scenarios, opportunities and critical issues
Musacchio, N. (2018) · The Journal of AMD · doi:10.36171/jamd18.21.3.04 · Cited by 4
National Security: Theories, Actors, Issues
Clarke, M., Henschke, A., Legrand, T., & Sussex, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-53494-3_1 · Cited by 3
Ethical, Legal, and Social Issues (ELSI) in Mental Health Informatics
Subbian, V., Galvin, H. K., Petersen, C. C., & Solomonides, A. E. (2021) · Health informatics · doi:10.1007/978-3-030-70558-9_18 · Cited by 3
CET‐4 Listening Test Effect on Listening Learning Based on Machine Learning
Wu, F., Chen, X., & Zheng, H. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/5604032 · Cited by 4
“Different from BP neural network based on some biological principle, the theoretical basis of SVM is statistical learning theory [19].”
BackgroundIntelligent Fault Diagnosis Method for Industrial Processing Equipment by ICECNN-1D
Li, Z., Jiang, Y., Liu, B., Ma, L., Qu, J., & Chai, Y. (2022) · Electronics · doi:10.3390/electronics11244207 · Cited by 3
Artificial intelligence in healthcare
Argenson, A., & Devi-Chou, V. (2023) · Principles of Gender-Specific Medicine · doi:10.1016/b978-0-323-88534-8.00025-0 · Cited by 3
Banda, J. M., Shah, N. H., & Periyakoil, V. S. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad043 · Cited by 4
“27 Rule-based phenotyping has been the de facto method for identifying cohorts of patients belonging to any given condition/phenotype.”
MethodSelf-citationInvestigating anatomical bias in clinical machine learning algorithms
Pedersen, J. S., Laursen, M. S., Vinholt, P. J., Alnor, A. B., & Savarimuthu, T. R. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.103 · Cited by 3
“Barriers for realisation of the algorithms’ full potential include bias and generali-* Equal contribution sation issues (Char et al., 2018; Hovy and Prabhu-moye, 2021; Carrell et al., 2017).”
BackgroundArtificial intelligence in obstetrics and gynecology
Kassir, E., Kuhn, V. C., Wong, M. S., & Han, C. S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00004-8 · Cited by 3
Saliency Guided Debiasing: Detecting and mitigating biases in LMs using feature attribution
Joshi, R. K., Chatterjee, A., & Ekbal, A. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126851 · Cited by 3
Сергеевич, К. В., Сергеевич, Б. Д., Харисович, В. В., Кавешников, А. В., Kuzmichkina, M. A., & Анатольевна, Т. И. (2023) · Complex Issues of Cardiovascular Diseases · doi:10.17802/2306-1278-2023-12-3-109-125 · Cited by 3
Sharma, R., Gulati, A., & Chopra, K. (2023) · Springer eBooks · doi:10.1007/978-981-99-6472-7_2 · Cited by 4
Challenges to AI use in anesthesia and healthcare: An anesthesiologist’s perspective
Gandotra, S., & Gupta, S. (2023) · Indian Journal of Clinical Anaesthesia · doi:10.18231/j.ijca.2023.077 · Cited by 3
Alhur, A., Alhur, A. A., Alghamdi, S. J. H., Asiri, Z. I., Alzamil, S. K. S., Alfateih, S., Fataih, S. S. M. A., Alfahad, A. H., Alzmanan, S. M. M., Alghamdi, M. M. F., et al. (2023) · Journal of Population Therapeutics and Clinical Pharmacology · doi:10.53555/jptcp.v30i19.4744 · Cited by 3
THE USE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE DECISION-MAKING
Alfseel Osamah Abdullh, A., Alshehri, Y. A. I., Hamdi, D. H. B., Alsultan, A. A. H., Aldawish, S. N., Beshri, S. O. A., Koshak, A. H. D., Alhassan, A. S. J., Zamil, A. A. H., Alsahli, A. M., et al. (2022) · doi:10.53555/jptcp.v29i04.5367 · Cited by 5
Quality, safety and artificial intelligence
Soukup, T., & Franklin, B. D. (2024) · BMJ Quality & Safety · doi:10.1136/bmjqs-2024-017382 · Cited by 5
“Furthermore, such efforts must prioritise the development of AI applications that enhance diagnostic accuracy and treatment efficacy and actively contribute to advancing the overall quality and safety of healthcare, ensuring that every technological advancement directly benefits patients.”
Al-Zanbouri, Z., Sharma, G., & Raza, S. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00105 · Cited by 4
“These disparities arise from multifaceted influences, including socioeconomic, environmental, and cultural factors, leading to inequitable health experiences and outcomes among diverse communities.”
BackgroundChu, L. F., & Kurup, V. (2025) · Current anesthesiology reports · doi:10.1007/s40140-024-00674-5 · Cited by 3
“The practical application of these models in clinical settings must also navigate ethical considerations, ensuring patient autonomy and data security while addressing potential biases inherent in the training data [32].”
Holmlund, T. B., Diaz-Asper, C. M., & Elvevåg, B. (2020) · Cortex · doi:10.1016/j.cortex.2020.08.024 · Cited by 4
Klincewicz, M., & Frank, L. E. (2020) · PhilPapers (PhilPapers Foundation) · Cited by 3
Big Data and the Threat to Moral Responsibility in Healthcare
Tigard, D. W. (2021) · Springer eBooks · doi:10.1007/978-3-662-62987-1_2 · Cited by 3
Towards the Future with AI: Work and Superintelligence
Boddington, P. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-19-9382-4_10 · Cited by 3
Performance Assessment of Different Machine Learning Algorithms for Medical Decision Support Systems
Ragupathi, T., & Govindarajan, M. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-43192-1_103 · Cited by 3
Neumann, N. A., de Oliveira, F. A. C., Machicao, J. C., & Velasquez, O. C. (2019) · IEEE Sciences and Humanities International Research Conference (SHIRCON) · doi:10.1109/shircon48091.2019.9024850 · Cited by 3
“These potentially positive interactions between a successful human healthcare experience and a highly technological set of powerful resources needs to be carefully considered, reassuring friendly and effective human adaption [5].”
BackgroundXia, M., Xu, T., & Jiang, H. (2021) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-021-2331-3 · Cited by 4
Ethics, emerging research trends, issues and challenges
SCHNEIDER, P. L., & Xhafa, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823818-9.00025-0 · Cited by 4
No Evidence of HIV-2 Infection in Argentina
Picchio, G. N., Muchinik, G. R., Bouzas, M. B., de Virologia, S., Perez, B. R. L., Anselmo, A., Tezanos, P. M., de Hemofilia, S. N., Cahn, P., P??rez, H., et al. (1990) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/00126334-199003050-00013 · Cited by 3
Germann, S., & Jasper, U. (2020) · mHealth · doi:10.21037/mhealth-2019-di-11 · Cited by 3
Zaim, S. R., Kenost, C., Berghout, J., Chiu, W., Wilson, L. S., Zhang, H. H., & Lussier, Y. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/681973 · Cited by 2
The power of clinical data empowered by clinical prediction model: an R tutorial
Dai, L., Yang, D., & Shen, H. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2020.01.114 · Cited by 3
“However, one important consideration left out is ethics, which may be seemingly irrelevant to the clinical prediction model development but actually a necessary aspect that should be addressed throughout the development process, especially in medical settings (17).”
BackgroundGene Expression Imputation with Generative Adversarial Imputation NetsPreprint
Viñas, R., Azevedo, T., Gamazon, E. R., & Liò, P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.09.141689 · Cited by 5
Early Detection of Incipient Alzheimer Pathology
Barkhof, F., & Pressman, P. (2021) · Neurology · doi:10.1212/wnl.0000000000011739 · Cited by 3
To Catch a Killer: A Data-Driven Personalized and Compliance-Aware Sepsis Alert SystemPreprint
Ayvaci, M., Mobini, Z., & Özer, Ö. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3805931 · Cited by 4
Explaining medical AI performance disparities across sites with confounder Shapley value analysisPreprint
Wu, E., Wu, K., & Zou, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.08168 · Cited by 3
“Previous work has focused on cataloging various types of machine learning bias (Mehrabi et al., 2021; Gu and Oelke, 2019; Char et al., 2018; Hitti et al., 2019; Gianfrancesco et al., 2018).”
Awawdeh, M. A., & Dervis, H. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4017021 · Cited by 3
Artificial Intelligence (AI) and its application on Human HealthPreprint
Jawaid, S. A. (2023) · Preprints.org · doi:10.20944/preprints202303.0246.v1 · Cited by 2
Exploring the Performance and Explainability of BERT for Medical Image Protocol AssignmentPreprint
Talebi, S., Tong, E., & Mofrad, M. R. K. (2023) · medRxiv · doi:10.1101/2023.04.20.23288684 · Cited by 4
“However, given the high stakes of medical tasks, 92 machine learning models must be evaluated for any system93 atic biases or errors before they can be trusted by clinicians 94 and patients [7].”
BackgroundArtificial Intelligence in Cancer ImagingPreprint
Jonah, U., & Parwani, A. V. (2024) · Preprints.org · doi:10.20944/preprints202401.0335.v1 · Cited by 3
Mansoor, M. A., & Ansari, K. (2024) · medRxiv · doi:10.1101/2024.08.13.24311933 · Cited by 3
“Integrating AI-based tools into psychiatric practice requires careful consideration of ethical implications and potential biases 21 .”
MethodOptimizing Healthcare with AI Chatbots: Addressing Challenges and OpportunitiesPreprint
Bodas, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5148492 · Cited by 3
A Study of Possible AI Aversion in Healthcare Consumers
Mukherjee, T., & Mukherjee, A. (2025) · Wiley eBooks · doi:10.1002/9781394234073.ch1 · Cited by 3
The Human Element: Ethical Guardrails for AI in Modern Medicine
Ferguson, J. (2025) · The American Journal of Cosmetic Surgery · doi:10.1177/07488068251359686 · Cited by 3
Raza, A. (2023) · theses.fr (ABES) · doi:10.70675/ac35dc8az89ccz4977z8f25zb7c8937f7cf0 · Cited by 4
Fiduciary requirements for virtual assistants
Koessler, L. (2024) · Ethics and Information Technology · doi:10.1007/s10676-023-09741-7 · Cited by 2
“In traditional fiduci-ary settings, such as the areas of health and finance, the deployment of AI has already given rise to renewed discussions of fiduciary duties (see, e.g., on health Anom, 2020, p. 5; Char et al., 2018, p. 983; and on finance Lightbourne, 2017; Pesapane et al., 2018, p. 751).”
Machine Learning in Medical Systems
Hu, W. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197653609.013.28 · Cited by 3
Post-Ischemic Stroke Cardiovascular Risk Prevention and Management
Guo, Y., Pan, D., Wan, H., & Yang, J. (2024) · Healthcare · doi:10.3390/healthcare12141415 · Cited by 2
“Currently, many hospitals are gradually implementing shift systems, which may affect the continuity of medical records and individualized patient care [31].”
BackgroundUnderstanding Artificial Intelligence Along with Legal and Ethical Issues
ÇİÇİN, F. N., & Sayın, S. (2024) · Journal of Oncological Sciences · doi:10.37047/jos.2023-99598 · Cited by 3
“34 As AI continues to evolve and becomes more integrated into medical decision-making, the conventional definition of malpractice may need to be reevaluated to address these new complexities.”
BackgroundVilela, L. F. C., Cabral, N. O., Destêfani, A. C., & Destefani, V. C. (2024) · Revista Ibero-Americana de Humanidades, Ciências e Educação · doi:10.51891/rease.v10i8.15395 · Cited by 3
“Thirdly, the ethical implications of AI in ophthalmology must be carefully considered, including privacy, security, and bias (34).”
BackgroundOpportunities and Challenges of Applying Artificial Intelligence in Healthcare
Fahmy, A. M. (2024) · Premier Journal of Artificial Intelligence · doi:10.70389/pjai.100003 · Cited by 3
Ba, N., Wang, Y., Zhang, K., & Mao, Y. (2025) · Sustainable materials and technologies · doi:10.1016/j.susmat.2025.e01452 · Cited by 2
Zhu, H., Zhang, N., Ni, Y.-Z., & Sun, Q. (2025) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2025.2514133 · Cited by 2
“Machine learning algorithms are usually trained on large datasets of labeled data to find the hidden patterns and relationships in the data (Char et al. 2018).”
The Need for Synergy in Foresight Research for Healthcare and Medical Sciences
Sargsyan, K., Hartl, G., Kozlakidis, Z., Landesz, T., Marino, D., Tatonetti, N. P., Mitchell, C., Nazarko, Ł., & Popper, R. (2025) · Innovations in Digital Health Diagnostics and Biomarkers · doi:10.36401/iddb-25-1 · Cited by 4
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Choudhury, P., & Goel, P. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4690-8_22 · Cited by 2
Evaluating Canada's Position in AI Adoption for eHealth System
Kimiagari, S. (2025) · International Journal of Knowledge Management · doi:10.4018/ijkm.384590 · Cited by 2
Rajeshwari, A. V., D, S., Bantu, P., & Kotha, R. (2025) · Cureus · doi:10.7759/cureus.88212 · Cited by 2
“Incorporating explainable AI is crucial for clinical acceptance, as it helps providers understand model decision-making and fosters trust in AI-assisted care [5,16].”
Ceviz, A., & Özden, G. (2025) · Yoğun Bakım Hemşireliği Dergisi · doi:10.62111/ybhd.1606594 · Cited by 4
Risks of digitalization in mental health care
Volpe, U., Ramalho, R., & Gäebel, W. (2025) · Neuropsychiatrie · doi:10.1007/s40211-025-00551-5 · Cited by 2
“If algorithms are trained on datasets that underrepresent minority populations, the tools may yield less accurate results for these groups, leading to “diagnostic in-justice” [17].”
Elgin, C. Y., & Elgin, C. (2025) · European Journal of Futures Research · doi:10.1186/s40309-025-00257-5 · Cited by 2
“As documented by Char et al. [8] in their analysis of AI ethics in healthcare, questions arise about responsibility and liability when diagnostic decisions involve both human practitioners and AI systems.”
Emssaad, I., Ben-Bouazza, F.-E., Tafala, I., Mezali, M. C. E., & Jioudi, B. (2025) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2025.100221 · Cited by 2
Artificial intelligence in diagnostic imaging: collaborative asset or looming replacement?
da Silva-Filho, J. E., de Aguiar, A. W. O., Silva, C. M., de Albuquerque, D. F., & Filho, E. D. G. (2025) · Oral Radiology · doi:10.1007/s11282-025-00871-w · Cited by 2
EndoCompass project: artificial intelligence in endocrinology
Assié, G., Carneiro, G., Davies, E., Šojat, A. S., & Mertens, J. (2025) · European Journal of Endocrinology · doi:10.1093/ejendo/lvaf073 · Cited by 2
Hundal, J., Veettil, A. A. V., Chung, C., & Hosseini, M. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-517280 · Cited by 1
Alhejji, B., Cunningham, S., Dorney, S., & Palmer, R. (2026) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.70322 · Cited by 1
“Furthermore, concerns around model explainability and algorithmic transparency (Holzinger et al. 2019 ), as well as the ethical use of patient speech data including informed consent and data protection (Char et al. 2018 ), warrant careful consideration before integration to clinical practice.”
Metadata
- Authors
- Char, D. S., Shah, N. H., & Magnus, D.
- Affiliations
- Stanford Medicine (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2018-03-15
- Volume
- 378
- Issue
- 11
- Pages
- 981-983
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC5962261
- OpenAlex
- W2789970635
- Cited by
- 1,857
- References
- 4
- Citations by year
- 2026: 445 · 2025: 309 · 2024: 263 · 2023: 214 · 2022: 162 · 2021: 174 · 2020: 152 · 2019: 95 · 2018: 26
- FWCI
- 29.24
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (K01HG008498)
- 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 › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- machine learning, health care ethics, ethical considerations, algorithmic bias, patient privacy, clinical decision support
- MeSH terms
- Machine Learning, Algorithms, Decision Making, Computer-Assisted, Delivery of Health Care, Ethics, Medical, Humans, Physician-Patient Relations, Codes of Ethics, Healthcare Disparities, Racism
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