The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Items 401–500 of 1,002
Regulatory Considerations on the use of Machine Learning based tools in Clinical Trials
Massella, M., Dri, D. A., & Gramaglia, D. (2022) · Health and Technology · doi:10.1007/s12553-022-00708-0 · Cited by 25
“Another potential bias could derive from representativeness of datasets that could contain disparities and when these are used to train the algorithm, it could lead to an over or under estimation of the results [30] and so to biased programs with reduced predictive accuracy that generate or exacerbate discrimination i…”
BackgroundThe role of AI and mobile apps in patient-centric healthcare delivery
Oyeniyi, J. G. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.22.1.1331 · Cited by 28
Chau, M. T. (2024) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2024.101733 · Cited by 30
Alfaraj, A., Nagai, T., AlQallaf, H., & Lin, W. (2024) · Dentistry Journal · doi:10.3390/dj13010013 · Cited by 25
“Additionally, data minimization and anonymization should be implemented to regulate the handling of sensitive information.”
Inteligencia artificial y aprendizaje automático en medicina
Vega, M. Á., Mora, L. M. Q., & Badilla, M. V. C. (2020) · Revista Medica Sinergia · doi:10.31434/rms.v5i8.557 · Cited by 43
An African Relational Approach to Healthcare and Big Data Challenges
Ewuoso, C. (2021) · Science and Engineering Ethics · doi:10.1007/s11948-021-00313-w · Cited by 25
Assessing Technology Innovation of Mobile Health Apps for Medical Care Providers
Galetsi, P., Katsaliaki, K., & Kumar, S. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3142619 · Cited by 24
“For example, security, reliability, and usability issues are raised for automated healthcare consultations [93] offered by differential diagnosis apps.”
BackgroundMobilizing artificial intelligence to cardiac telerehabilitation
Su, J., Zhang, Y., Ke, Q., Su, J.-K., & Yang, Q. (2022) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2302045 · Cited by 28
“Despite the notion of bias being very complex, and biases commonly existing in the human world, it is possible and ethically necessary to design AI systems to help offset human biases to try to lead to outcomes closer to fairness [103].”
BackgroundWasserzug, Y., Degani, Y., Bar-Shaked, M., Binyamin, M., Klein, A., Hershko, S., & Levkovitch, Y. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.12.117 · Cited by 25
Automated Assessment of Psychiatric Disorders Using Speech: A Systematic ReviewPreprint
Low, D. M., Bentley, K. H., & Ghosh, S. (2019) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/5pwze · Cited by 30
Yang, Y., Liu, Y., Chen, Y., Luo, D., Xu, K., & Zhang, L. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1477130 · Cited by 28
Ozcan, B. B., Doğan, B. E., Xi, Y., & Knippa, E. E. (2025) · Radiology Imaging Cancer · doi:10.1148/rycan.240290 · Cited by 24
“Our findings highlight the critical need to address racial and ethnic disparities within AI health care applications, revealing that participants, like researchers, are concerned about potential biases (13–16).”
How Might Artificial Intelligence Applications Impact Risk Management?
Banja, J. D. (2020) · The AMA Journal of Ethic · doi:10.1001/amajethics.2020.945 · Cited by 24
A Comparative Analysis of SVM and ELM Classification on Software Reliability Prediction Model
Rath, S. K., Sahu, M., Das, S. P., Bisoy, S. K., & Sain, M. (2022) · Electronics · doi:10.3390/electronics11172707 · Cited by 29
Toward best practices in embedded ethics: Suggestions for interdisciplinary technology development
Tigard, D. W., Braun, M., Breuer, S., Ritt, K., Fiske, A., McLennan, S., & Buyx, A. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104467 · Cited by 29
Chen, H. (2023) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-023-04280-9 · Cited by 24
“It is necessary to consider establishing legal and ethical regulations for AI medical data [50].”
BackgroundKudina, O., & de Boer, B. (2021) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13535 · Cited by 34
“As a result, researchers are worried about the potentially ethically problematic outcomes that can be expected when ML systems are constructed as black boxes, making it more likely that problems such as the ones mentioned in the previous paragraph might remain unnoticed.(6)”
BackgroundTimilsina, M., Buosi, S., Razzaq, M. A., Haque, R., Judge, C. S., & Curry, E. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109925 · Cited by 27
Esmaeilzadeh, P. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03182-6 · Cited by 26
Lee, Y. K., Yoon, E.-J., Kim, T. H., Kim, J.-I., & Kim, J.-H. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14238467 · Cited by 29
“Business Associate Agreements (BAAs) must govern data sharing with third parties [88].”
Chu, A., Squirrell, D., Phillips, A. M., & Vaghefi, E. (2020) · Journal of Ophthalmology · doi:10.1155/2020/8841927 · Cited by 26
“Whilst some biases may be obvious, others are more subtle and human bias may, therefore, be inadvertently built into a DLA’s decision making [49].”
BackgroundKelly-Hedrick, M., Abd‐El‐Barr, M. M., Aarabi, B., Curt, A. E. P., Howley, S. P., Harrop, J. S., Kirshblum, S., Neal, C. J., Noonan, V. K., Park, C., et al. (2022) · Journal of Neurotrauma · doi:10.1089/neu.2022.0450 · Cited by 24
Artificial Intelligence in Medical Filed
Fatima, I., Grover, V., Khan, I. R., Ahmad, N., & Yadav, A. (2023) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eetpht.9.4713 · Cited by 27
“Ensuring patient consent, data security, and unbiased algorithms are essential to maintaining trust and equity in healthcare [5].”
BackgroundWang, T.-W., Shiao, Y.-C., Hong, J.-S., Lee, W.-K., Hsu, M.-S., Cheng, H.-M., Yang, H.-C., Lee, C.-C., Pan, H.-C., You, W. C., et al. (2024) · Mayo Clinic Proceedings: Digital Health · doi:10.1016/j.mcpdig.2024.01.002 · Cited by 23
“12 Re fl ecting on our previous research, 13 it was evident that the model ’ s generalization capability was closely tied to data distribution.”
AI and Aphasia in the Digital Age: A Critical Review
Privitera, A. J., Ng, S. H. S., Kong, A. P.-H., & Weekes, B. S. (2024) · Brain Sciences · doi:10.3390/brainsci14040383 · Cited by 26
“Ethical guidelines for AI in healthcare, as outlined by Char et al. [109], emphasize the critical importance of addressing these concerns.”
BackgroundThe promise of AI and technology to improve quality of life and care for older adults
Abadir, P. M., Chellappa, R., Choudhry, N., Demiris, G., Ganesan, D., Karlawish, J., Li, R. M., Moore, J. H., Walston, J. D., AITCs, D. &. A. O. T., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00430-0 · Cited by 24
The Environmental Costs of Artificial Intelligence for Healthcare
Katirai, A. (2024) · Asian Bioethics Review · doi:10.1007/s41649-024-00295-4 · Cited by 28
“…impact of AI despite its focus on the need to “mitigate … potential harms, especially for the most vulner-able” (Murphy et al. 2021; see also Char et al. 2018; Gerke et al. 2020; Martinez-Mar-tin et al. 2021; Hadley et al. 2020), or to offer a second example, in an examination of one…”
BackgroundAdithyan, N., Chowdhury, R. R., Padmavathy, L., Peter, R. M., & Anantharaman, V. V. (2024) · Cureus · doi:10.7759/cureus.69910 · Cited by 35
“One of the primary concerns is the potential for bias and discrimination in AI algorithms, which can perpetuate existing inequalities in healthcare [12].”
BackgroundProfessional identity and its relationships with AI readiness and interprofessional collaboration
Ta’an, W., Damrah, S., Al‐Hammouri, M. M., & Williams, B. A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322794 · Cited by 22
“For example, Char et al. [12] noted that as AI applications increase in healthcare, clinicians’ roles decrease which could give AI tools unintended power and affect their professional identity.”
Jung, K., Sudat, S. E. K., Kwon, N., Stewart, W. F., & Shah, N. H. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103115 · Cited by 25
“There are a host of cultural issues with predicting end of life including, but not limited to, ethical challenges as to how such models might be used, communication challenges around how providers, patients, and families are contacted and engaged to discuss options, and management challenges to ensure that patient aut…”
BackgroundSelf-citationMachine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases
Muhsen, I. N., Shyr, D. C., Sung, A. D., & Hashmi, S. K. (2020) · Clinical Hematology International · doi:10.2991/chi.k.201130.001 · Cited by 23
“The integration of ML and DL in clinical care is faced with multiple ethical and logistic concerns [5].”
BackgroundMachine Learning for Geriatric Clinical Care: Opportunities and Challenges
Javadi‐Pashaki, N., Ghazanfari, M. J., & Karkhah, S. (2021) · Annals of Geriatric Medicine and Research · doi:10.4235/agmr.21.0054 · Cited by 22
Filipow, N., Main, E., Sebire, N. J., Booth, J., Taylor, A. M., Davies, G., & Stanojevic, S. (2022) · BMJ Open Respiratory Research · doi:10.1136/bmjresp-2021-001165 · Cited by 24
Sveen, W. N., Dewan, M. L., & Dexheimer, J. W. (2022) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2022.04.024 · Cited by 24
Cagliero, D., Deuitch, N. T., Shah, N. H., Feudtner, C., & Char, D. S. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad022 · Cited by 23
Self-citationParchmann, N., Hansen, D., Orzechowski, M., & Steger, F. (2024) · GeroScience · doi:10.1007/s11357-024-01229-6 · Cited by 19
“Intentional and systematic, for example, political biases can be installed for funding reasons, influencing unfair decision-making, which users do not necessarily recognize [31, 32].”
BackgroundStefely, J. A., Theisen, E., Hanewall, C., Scholl, L. M., Burkard, M. E., Anna Huttenlocher, A., & Yu, J. J. (2019) · BMC Medical Education · doi:10.1186/s12909-019-1523-0 · Cited by 22
Ntlhakana, L., Nelson, G., Khoza‐Shangase, K., & Dorkin, E. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19010001 · Cited by 22
“In 2005, the global prevalence of hearing loss due to exposure to noise in the workplace was estimated to range from 7% in high-income countries to 21% in low- and middle-income countries (LMICs) [5] In LMICs, mining, agriculture and construction industries have been reported to produce the most excessive noise exposu…”
BackgroundInfluentialArtificial Intelligence and Health Care Disparities in Radiology
Sorin, V., & Klang, E. (2021) · Radiology · doi:10.1148/radiol.2021210566 · Cited by 22
Muehlematter, U. J., Daniore, P., & Vokinger, K. N. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30292-2 · Cited by 665
Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials
Donmazov, S., Saruhan, E. N., Pekkan, K., & Pişkin, Ş. (2024) · Cardiovascular Engineering and Technology · doi:10.1007/s13239-024-00737-y · Cited by 23
Doherty, G., McLaughlin, L., Hughes, C., McConnell, J., Bond, R., & McFadden, S. L. (2024) · Radiography · doi:10.1016/j.radi.2024.10.010 · Cited by 35
Marano, G., Rossi, S., Marzo, E. M., Ronsisvalle, A., Artuso, L., Traversi, G., Pallotti, A., Bove, F., Piano, C., Bentivoglio, A. R., et al. (2025) · Biomedicines · doi:10.3390/biomedicines13071764 · Cited by 27
“In particular, Char et al. [150] underline critical ethical risks associated with AI and ML, such as algorithmic bias, lack of fairness, and transparency, issues that must be addressed to ensure their equitable and safe use in healthcare.”
Singh, S., & Steeves, V. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113112 · Cited by 21
Cox, M., Panagides, J. C., Tabari, A., Kalva, S. P., Kalpathy–Cramer, J., & Daye, D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277507 · Cited by 22
Stanley, E. A. M., Wilms, M., & Forkert, N. D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23223-7_2 · Cited by 21
A comprehensive review of AI applications in personalized medicine
Okolo, C. A., Olorunsogo, T. O., & Babawarun, O. (2024) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2024.11.1.0338 · Cited by 24
“From image recognition in radiology to predictive modeling for disease outcomes, AI has demonstrated its potential to revolutionize healthcare practices (Char et al., 2018).”
Impact of artificial intelligence in transforming the doctor–cancer patient relationship
Heudel, P., Crochet, H., & Blay, J. (2024) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2024.100026 · Cited by 28
SLE: reconciling heterogeneity
Lockshin, M. D., Barbhaiya, M., Izmirly, P., Buyon, J. P., & Crow, M. K. (2019) · Lupus Science & Medicine · doi:10.1136/lupus-2018-000280 · Cited by 21
Artificial Intelligence in the healthcare of older people
Mukaetova-Ladinska, E. B., Harwood, T. G., & Maltby, J. (2020) · Archives of Psychiatry and Mental Health · doi:10.29328/journal.apmh.1001011 · Cited by 21
“The Canadian Association of Radiologists recently provided a framework for the study of legal and ethical issues related to AI in medical imaging for patient data (privacy, con identiality, ownership, and sharing), algorithms (levels of autonomy, liability and jurisprudence), practice (best practices and current legal…”
Backgroundde Boer, B., & Kudina, O. (2021) · Metamedicine · doi:10.1007/s11017-021-09553-0 · Cited by 26
“ML shapes the relationship between health care professionals and patients and ties patients to certain images and responsibilities [8, 11].”
BackgroundInfluentialArtificial intelligence, medications, pharmacogenomics, and ethics
Haga, S. B. (2024) · Pharmacogenomics · doi:10.1080/14622416.2024.2428587 · Cited by 25
Alghamdi, M. A. (2025) · Pharmaceuticals · doi:10.3390/ph18071041 · Cited by 23
“For the mitigation of bias in using AI tools, the use of different and representative datasets, data quality control, and bias detection methods is required [104,105].”
Bhushan, A., & Misra, P. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01992-6 · Cited by 26
“Furthermore, the “ black-box ” nature of many AI algorithms challenges trans-parency, complicating clinical decision-making and undermining trust among practitioners and patients 98 .”
Adversarial attack on deep learning-based dermatoscopic image recognition systems
Allyn, J., Allou, N., Vidal, C., Renou, A., & Ferdynus, C. (2020) · Medicine · doi:10.1097/md.0000000000023568 · Cited by 21
Artificial intelligence and clinical deterioration
Malycha, J., Bacchi, S., & Redfern, O. (2022) · Current Opinion in Critical Care · doi:10.1097/mcc.0000000000000945 · Cited by 20
“CONSORT-AI contains 14 new items that are specific to reporting AI research These items include how the AI was integrated into the trial setting, data input and validity methodology, development this field because research methodologies to date have been of varying quality and heterogeneous [75 & ].”
MethodMorris, A. H., Horvat, C. M., Stagg, B. C., Grainger, D. W., Lanspa, M. J., Orme, J. F., Clemmer, T. P., Weaver, L. K., Thomas, F. O., Grissom, C. K., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac143 · Cited by 21
MacKay, C., Klement, W., Vanberkel, P. T., Lamond, N. W. D., Urquhart, R., & Rigby, M. H. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100155 · Cited by 23
Artificial Intelligence in Breast Imaging: Challenges of Integration Into Clinical Practice
Ozcan, B. B., Patel, B., Banerjee, I., & Doğan, B. E. (2023) · Journal of Breast Imaging · doi:10.1093/jbi/wbad007 · Cited by 24
Quek, L. J., Heikkonen, M. R., & Lau, Y. (2023) · Journal of Clinical Nursing · doi:10.1111/jocn.16699 · Cited by 22
The Next Frontier in Sarcoma Care: Digital Health, AI, and the Quest for Precision Medicine
Fuchs, B., Studer, G., Bode‐Lesniewska, B., Heesen, P., & on behalf of the Swiss Sarcoma Network (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13111530 · Cited by 20
“As we advance toward a new paradigm of value-based precision care in sarcoma, underpinned by digital health and AI, ethical and regulatory considerations must be addressed with the same vigor as technological innovations [31,32].”
BackgroundArtificial Intelligence in Healthcare Sector: A Literature Review of the Adoption Challenges
Aldwean, A., & Tenney, D. (2024) · Open Journal of Business and Management · doi:10.4236/ojbm.2024.121009 · Cited by 20
Talebi, S., Tong, E., Li, A., Yamin, G., Zaharchuk, G., & Mofrad, M. R. K. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02444-z · Cited by 25
“However, given the high stakes of medical tasks, machine learning models must be evaluated for any systematic biases or errors before they can be trusted by clinicians and patients [16].”
BackgroundChallenges and Opportunities for AI in Healthcare
Kan, Y. (2024) · International Journal of Law and Policy · doi:10.59022/ijlp.203 · Cited by 26
Dheyab, H. F., Uçan, O. N., Khalaf, M., & Mohammed, A. H. (2020) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit50672.2020.9254994 · Cited by 20
“We conclude there are many SCD sufferers need care and providing appropriate treatment in accurate quantities and as quickly as possible[4], to improve this process by providing technical methods that help the specialist doctor and provide the patient with the appropriate dose and also save time…”
BackgroundBig Data Analytics and AI in Mental HealthcarePreprint
Rosenfeld, A., Benrimoh, D., Armstrong, C., Mirchi, N., Langlois-Therrien, T., Rollins, C. P. E., Tanguay-Sela, M., Mehltretter, J., Fratila, R., Israel, S., et al. (2019) · arXiv.org · doi:10.48550/arxiv.1903.12071 · Cited by 19
Current and Potential Applications of Ambient Artificial Intelligence
Nahar, J. K., & Kachnowski, S. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.05.003 · Cited by 19
Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways
Lin, H., Ni, L., Phuong, C., & Hong, J. C. (2024) · Pharmacogenomics and Personalized Medicine · doi:10.2147/pgpm.s396971 · Cited by 22
“60 Adding to these challenges is the growing interest in building multi-institution or even international databases to improve the generalizability and effectiveness of NLP tools.”
BackgroundDigital pathology implementation in cancer diagnostics: towards informed decision-making
Sulaieva, O., Dudin, O., Koshyk, O., Panko, M., & Kobyliak, N. M. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1358305 · Cited by 19
“AHI relies on the use of computers and AI-based algorithms to facilitate and accelerate pathologists ’ role in image and data analysis (13).”
MethodAdvancing Psoriasis Care through Artificial Intelligence: A Comprehensive Review
Smith, P. L., Johnson, C. E., Haran, K., Orcales, F., Kranyak, A. C., Bhutani, T., Riera‐Monroig, J., & Liao, W. J. (2024) · Current Dermatology Reports · doi:10.1007/s13671-024-00434-y · Cited by 19
Can Artificial Intelligence Speak for Incapacitated Patients at the End of Life?
Brender, T. D., Smith, A. K., & Block, B. L. (2024) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2024.2676 · Cited by 19
Present and Future Applications of Artificial Intelligence in Kidney Transplantation
Kotsifa, E., & Mavroeidis, V. K. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13195939 · Cited by 24
“Furthermore, the incorporation of AI-related private-sector investments and designers poses the potential danger of manipulating the clinical decision-support systems to serve specific financial interests without necessarily optimising healthcare [117,121].”
Backgroundvan Zyl-Cillié, M., Bührmann, J. H., Blignaut, A. J., Demirtas, D., & Coetzee, S. K. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-12184-5 · Cited by 19
Ethics and Algorithms to Navigate AI’s Emerging Role in Organ Transplantation
Salybekov, A. A., Yerkos, A., Sedlmayr, M., & Wolfien, M. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14082775 · Cited by 20
“To address these concerns, it is essential to establish clear guidelines that balance the use of AI with human oversight, ensuring that AI tools augment rather than replace clinical expertise [66,67].”
Haugg, F., Lee, G. C., He, J., Johnson, J., Zapaishchykova, A., Bitterman, D. S., Kann, B. H., Aerts, H. J. W. L., & Mak, R. H. (2025) · The Lancet Healthy Longevity · doi:10.1016/j.lanhl.2025.100728 · Cited by 27
Xue, H., Wang, S.-Y., Cui, L., & Hong, K. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20436 · Cited by 20
Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques
Lamba, D., Hsu, W. H., & Alsadhan, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821777-1.00023-9 · Cited by 26
Rice, E., Wilder, B., Onasch-Vera, L., DiGuiseppi, G., Petering, R., Hill, C., Yadav, A., Lee, S.-J., & Tambe, M. (2021) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0000000000002807 · Cited by 20
Willem, T., Krammer, S., Böhm, A. ‐., French, L. E., Hartmann, D., Lasser, T., & Buyx, A. (2022) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.18192 · Cited by 20
“The distribution of (unintended) power and authority from human specialists to ML-HCAs 17 or the theoretical disproportionate adoption of ML-HCAs through its uneven distribution of harm and benefits 57 as global medical concerns need attention.”
BackgroundArtificial Intelligence and Deep Learning in Ophthalmology
Wang, Z., Keane, P. A., Chiang, M. F., Cheung, C. Y.-L., Wong, T. Y., & Ting, D. S. W. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_200-1 · Cited by 20
Magnusson, C. G. M., Hagiwara, M. A., Boysen, G. N., Kauppi, W., Herlitz, J. D., Axelsson, C., Packendorff, N., Larsson, G., & Wibring, K. (2022) · BMC Emergency Medicine · doi:10.1186/s12873-022-00643-3 · Cited by 19
“Furthermore, publicly open source data of these models is also preferrable to avoid bias and could also increase knowledge of the model, which data that is included and also model limitations [46].”
BackgroundSmart Sensing Enhanced Diagnostic Expert Systems
Terhorst, Y., Knauer, J., & Baumeister, H. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_24 · Cited by 22
Leveraging Artificial Intelligence for Enhanced Revenue Cycle Management in the United States
Kilanko, V. (2023) · International Journal Of Scientific Advances · doi:10.51542/ijscia.v4i4.3 · Cited by 20
Vagliano, I., Dormosh, N., Rios, M., Luik, T. T., Buonocore, T. M., Elbers, P., Dongelmans, D. A., Schut, M. C., & Abu–Hanna, A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104504 · Cited by 22
Quishpe-Usca, A., Cuenca-Dominguez, S., Arias-Viñansaca, A., Bosmediano-Angos, K., Villalba-Meneses, F., Ramírez-Cando, L., Tirado-Espín, A., Cadena-Morejón, C., Almeida-Galárraga, D. A., & Guevara, C. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1953 · Cited by 22
“This technique allows efficient automation of skin lesion assessment based on the creation of statistical algorithms for learning purposes ( Char, Shah & Magnus, 2018 ; Fu’adah et al., 2020 ).”
MethodOuattara, S. A., Gody, M., & de Thé, G. (1990) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/00126334-199003050-00017 · Cited by 20
Enhancing skin lesion classification: a CNN approach with human baseline comparison
Ajabani, D., Shaikh, Z. A., Yousef, A., Ali, K., & Albahar, M. A. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2795 · Cited by 18
Karami, M., & Madlool, H. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1532361 · Cited by 18
“…concerns related to AI in health care include potential violations of patient privacy, the tension among providing care and generating pro fi t and the introduction of a third party into the patient ‒ doctor relationship, which modi fi es expectations of con fi dentiality and responsibility (11).”
Global AI Ethics Documents: What They Reveal About Motivations, Practices, and Policies
Schiff, D. S., Laas, K., Biddle, J. B., & Borenstein, J. (2022) · The International library of ethics, law and technology · doi:10.1007/978-3-030-86201-5_7 · Cited by 22
Ethical Challenges of Artificial Intelligence in Health Care: A Narrative Review
Hui, A. T., Ahn, S. S., Lye, C. T., & Deng, J. (2021) · Ethics in Biology Engineering and Medicine An International Journal · doi:10.1615/ethicsbiologyengmed.2022041580 · Cited by 20
Negriff, S. L., Dilkina, B., Matai, L., & Rice, E. R. W. (2022) · PLoS ONE · doi:10.1371/journal.pone.0274998 · Cited by 21
Artificial intelligence in neuroradiology: a scoping review of some ethical challenges
Khosravi, P., & Schweitzer, M. (2023) · Frontiers in Radiology · doi:10.3389/fradi.2023.1149461 · Cited by 18
“It is quite easy for potential bias to be embedded within algorithms that grow from “selected” data that are used to train algorithms (53, 54).”
BackgroundKim, J. P., Ryan, K., Kasun, M., Hogg, J., Dunn, L. B., & Roberts, L. W. (2023) · JMIR AI · doi:10.2196/47449 · Cited by 18
“Bringing awareness to the areas of translation where developers’intentions may not align with the goals of end users may serve to minimize ethical “strain,” as noted by Char et al [21].”
BackgroundMaiter, A., Hocking, K., Matthews, S., Taylor, J. C., Sharkey, M. J., Metherall, P., Alabed, S., Dwivedi, K., Shahin, Y., Anderson, E., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-077348 · Cited by 20
Khalpey, Z. I., Kumar, U. A., King, N. C., Abraham, A., & Khalpey, A. (2024) · Cureus · doi:10.7759/cureus.65083 · Cited by 19
Muhsen, I. N., Elhassan, T. A. M., & Hashmi, S. K. (2018) · Turkish Journal of Hematology · doi:10.4274/tjh.2018.0123 · Cited by 19
“On the contrary, opponents of such integration cite different disadvantages that include concerns about the accuracy of these systems, the risk of having “deskilled” physicians, and fewer future jobs, especially in diagnostic medical fields such as radiology and pathology [6,7].”
BackgroundVanderelst, D. F., & Willems, J. (2019) · International Journal of Social Robotics · doi:10.1007/s12369-019-00612-0 · Cited by 18
“Indeed, machine learning methods are sensitive to the biases and limitations of the training data (See [22,32], for concerns about the use of machine learning in medicine).”
BackgroundPredicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach
Wolff, J., Gary, A., Jung, D., Normann, C., Kaier, K., Binder, H., Domschke, K., Klimke, A., & Franz, M. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1042-2 · Cited by 22
The Challenges of Implementing Artificial Intelligence into Surgical Practice
Tranter‐Entwistle, I., Wang, H., Daly, K., Maxwell, S., & Connor, S. J. (2020) · World Journal of Surgery · doi:10.1007/s00268-020-05820-8 · Cited by 19
“Where the data is not reflective of the group to which it is applied this can result in under or over diagnosis of pathology, or may introduce pre-existing biases as seen in the racial bias of programs used to evaluate a criminal’s risk of recidivism [29].”
BackgroundGupta, Y. D., & Bhandary, S. (2024) · Wiley eBooks · doi:10.1002/9781394234196.ch5 · Cited by 21
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
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
