The DOI matches its Crossref record; 11 of the 12 references it cites were found in a registry or an index.
Big data and black-box medical algorithms
Open accessAbstract
New machine-learning techniques entering medicine present challenges in validation, regulation, and integration into practice.
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
“For example, the kernels or intermediate features of a trained neural network may shed light on the learned structure in different layers of the network, giving rise to methods like class activation mapping (CAM).67”
From Addressing bias in big data and AI for health care: A call for open science (2021) · cites this paper as [67]
In the citing paper
“We propose that another important barrier to adoption of medical AI is its perceived opacity, that it is a 'black box' 13,14 .”
From Understanding, explaining, and utilizing medical artificial intelligence (2021) · cites this paper as [13]
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
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
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
How the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 1×
“Some techniques do not identify any explicit relationships; for example, a neural network trained to identify tumors can identify them but uses opaque hidden layers to do so ( 6 ).”
BackgroundExternal validity of randomised controlled trials: “To whom do the results of this trial apply?”
Rothwell, P. M. (2005) · The Lancet · doi:10.1016/s0140-6736(04)17670-8 · Cited by 2,544
Real-World Evidence — What Is It and What Can It Tell Us?
Sherman, R. E., Anderson, S. A., Pan, G. J. D., Gray, G. W., Gross, T. P., Hunter, N. L., LaVange, L. M., Marinac‐Dabic, D., Marks, P. W., Robb, M. A., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmsb1609216 · Cited by 2,192
New FDA Breakthrough-Drug Category — Implications for Patients
Darrow, J. J., Avorn, J., & Kesselheim, A. S. (2014) · New England Journal of Medicine · doi:10.1056/nejmhle1311493 · Cited by 119
Liu, N. T., Holcomb, J. B., Wade, C. E., Batchinsky, A. I., Cancio, L. C., Darrah, M. I., & Salinas, J. (2013) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-013-1130-x · Cited by 67
Price, W. (2017) · Michigan Law Review · doi:10.36644/mlr.116.3.regulating · Cited by 64
Self-citationLiehr, T., Acquarola, N., Pyle, K., St‐Pierre, S., Rinholm, M., Bar, O., Wilhelm, K., & Schreyer, I. (2017) · Clinical Genetics · doi:10.1111/cge.13087 · Cited by 48
Price, & Nicholson, W. T. (2015) · Harvard journal of law & technology · Cited by 25
Cited by
Sorted by citations
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
Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A., Valencia, A., Rementeria, M. J., Chadha, A. S., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0288-5 · Cited by 553
Understanding, explaining, and utilizing medical artificial intelligence
Cadario, R., Longoni, C., & Morewedge, C. K. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01146-0 · Cited by 342
Saillard, C., Schmauch, B., Laifa, O., Moarii, M., Toldo, S., Zaslavskiy, M., Pronier, E., Laurent, A., Amaddeo, G., Regnault, H., et al. (2020) · Hepatology · doi:10.1002/hep.31207 · Cited by 332
Burgos-Artizzu, X. P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Bonet-Carne, E., Eixarch, E., Crispi, F., & Gratacós, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-67076-5 · Cited by 240
Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review
Plana, D., Shung, D. L., Grimshaw, A. A., Saraf, A., Sung, J. J. Y., & Kann, B. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.33946 · Cited by 236
Let’s talk about sex: Differences in drug therapy in males and females
Madla, C. M., Gavins, F. K. H., Merchant, H. A., Orlu, M., Murdan, S., & Basit, A. W. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.05.014 · Cited by 167
Momtazmanesh, S., Nowroozi, A. A., & Rezaei, N. (2022) · Rheumatology and Therapy · doi:10.1007/s40744-022-00475-4 · Cited by 149
BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125
Harvey, H. B., & Gowda, V. (2020) · Academic Radiology · doi:10.1016/j.acra.2019.09.017 · Cited by 112
Song, D., Wang, Y., Wang, W., Wang, Y., Cai, J., Zhu, K., Lv, M., Gao, Q., Zhou, J., Fan, J., et al. (2021) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-021-03617-3 · Cited by 103
Antes, A. L., Burrous, S., Sisk, B. A., Schuelke, M. J., Keune, J. D., & DuBois, J. M. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01586-8 · Cited by 114
Zhang, S., Yu, J., Xu, X., Yin, C., Lu, Y., Yao, B., Tory, M., Padilla, L. M., Caterino, J., Zhang, P., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642343 · Cited by 99
“However, despite the advantages of AI models’ performance, these methods still often fail to garner clinician confidence, thereby hindering their practical implementation in real-world clinical settings [21, 22, 41, 56].”
BackgroundWarin, K., Limprasert, W., Suebnukarn, S., Jinaporntham, S., & Jantana, P. (2021) · International Journal of Oral and Maxillofacial Surgery · doi:10.1016/j.ijom.2021.09.001 · Cited by 102
Lee, L. S., Ping Keung, C. H. A. N., Wen, C., Fung, W. C., Cheung, A., Chan, V. W. K., Cheung, M. H., Fu, H., Yan, C. H., & Chiu, P. (2022) · Arthroplasty · doi:10.1186/s42836-022-00118-7 · Cited by 97
EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases
Gossec, L., Kedra, J., Servy, H., Pandit, A., Stones, S. R., Berenbaum, F., Finckh, A., Baraliakos, X., Stamm, T., Gómez-Cabrero, D., et al. (2019) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2019-215694 · Cited by 94
Hakkoum, H., Idri, A., & Abnane, I. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107829 · Cited by 98
Re-focusing explainability in medicine
Ossa, L. A., Starke, G., Lorenzini, G., Vogt, J. E., Shaw, D., & Elger, B. S. (2022) · Digital Health · doi:10.1177/20552076221074488 · Cited by 89
Ladbury, C., Zarinshenas, R., Semwal, H., Tam, A., Vaidehi, N., Rodin, A. S., Liu, A., Glaser, S., Salgia, R., & Amini, A. (2022) · Translational Cancer Research · doi:10.21037/tcr-22-1626 · Cited by 86
Hu, C., Li, L., Li, Y., Wang, F., Hu, B., & Peng, Z. (2022) · Infectious Diseases and Therapy · doi:10.1007/s40121-022-00671-3 · Cited by 63
Sisk, B. A., Antes, A. L., Burrous, S., & DuBois, J. M. (2020) · Children · doi:10.3390/children7090145 · Cited by 77
Liu, Z., Liu, Y., Zhang, W., Hong, Y., Meng, J., Wang, J., Zheng, S., & Xu, X. (2022) · Hepatology International · doi:10.1007/s12072-022-10321-y · Cited by 69
Zhou, B., Wang, L., Yin, H., Wu, T., Ren, T., Peng, C., Li, D.-X., Shi, H., Sun, L., Zhao, C.-K., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103684 · Cited by 67
Artificial intelligence for image analysis in total hip and total knee arthroplasty
Gurung, B. B., Liu, P., Harris, P., Sagi, A., Field, R. E., Sochart, D. H., Tucker, K., & Asopa, V. (2022) · The Bone & Joint Journal · doi:10.1302/0301-620x.104b8.bjj-2022-0120.r2 · Cited by 67
Two heads are better than one: current landscape of integrating QSP and machine learning
Zhang, T., Androulakis, I. P., Bonate, P. L., Cheng, L., Helikar, T., Parikh, J., Rackauckas, C., Subramanian, K., Cho, C. R., on behalf of the Working Group, et al. (2022) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-022-09805-z · Cited by 62
Regulatory Issues and Challenges to Artificial Intelligence Adoption
Harvey, H. B., & Gowda, V. (2021) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2021.07.007 · Cited by 59
Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms
Dickinson, A. H., Daniel, M., Marin, A., Gaonkar, B., Dapretto, M., McDonald, N. M., & Jeste, S. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.06.003 · Cited by 66
Machine Learning: The Next Paradigm Shift in Medical Education
James, C. A., Wheelock, K. M., & Woolliscroft, J. O. (2021) · Academic Medicine · doi:10.1097/acm.0000000000003943 · Cited by 58
Inventing Liraglutide, a Glucagon-Like Peptide-1 Analogue, for the Treatment of Diabetes and Obesity
Knudsen, L. B. (2019) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.9b00048 · Cited by 59
Applying machine learning to predict viral assembly for adeno-associated virus capsid libraries
Marques, A. D., Kummer, M., Kondratov, O., Banerjee, A., Moskalenko, O. V., & Zolotukhin, S. (2020) · Molecular Therapy — Methods & Clinical Development · doi:10.1016/j.omtm.2020.11.017 · Cited by 59
Definition of hypertension‐associated oral pathogens in NHANES
Pietropaoli, D., Pinto, R. D., Ferri, C., Ortu, E., & Monaco, A. (2019) · Journal of Periodontology · doi:10.1002/jper.19-0046 · Cited by 55
Bernstam, E. V., Shireman, P. K., Meric‐Bernstam, F., Zozus, M. N., Jiang, X., Brimhall, B. B., Windham, A. K., Schmidt, S., Visweswaran, S., Ye, Y., et al. (2021) · Clinical and Translational Science · doi:10.1111/cts.13175 · Cited by 59
Artificial Intelligence in Reproductive Medicine – An Ethical Perspective
Rolfes, V., Bittner, U., Gerhards, H., Krüssel, J.-S., Fehm, T. N., Ranisch, R., & Fangerau, H. (2023) · Geburtshilfe und Frauenheilkunde · doi:10.1055/a-1866-2792 · Cited by 59
In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare
Topaloglu, M. Y., Morrell, E. M., Rajendran, S., & Topaloĝlu, Ü. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.746497 · Cited by 50
Patino, G. A., Amiel, J. M., Brown, M., Lypson, M. L., & Chan, T. M. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005636 · Cited by 43
The AI Future of Emergency Medicine
Petrella, R. J. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2024.01.031 · Cited by 43
Kedra, J., Radstake, T. R. D. J., Pandit, A., Baraliakos, X., Berenbaum, F., Finckh, A., Fautrel, B., Stamm, T., Gómez-Cabrero, D., Pristipino, C., et al. (2019) · RMD Open · doi:10.1136/rmdopen-2019-001004 · Cited by 44
Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review
Zhang, T., Nikouline, A., Lightfoot, D., & Nolan, B. (2022) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2022.05.011 · Cited by 44
Jeong, H., Han, S., Jung, H., Lee, W., & Jeon, K. J. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05441-y · Cited by 41
Perumal, M. K. K., Renuka, R. R., Kumar, S. S., & Natarajan, P. M. (2025) · Frontiers in Oral Health · doi:10.3389/froh.2025.1592428 · Cited by 46
Han, T., Nebelung, S., Pedersoli, F., Zimmermann, M. N., Schulze-Hagen, M. F., Ho, M., Haarburger, C., Kiessling, F. M., Kuhl, C. K., Schulz, V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24464-3 · Cited by 40
Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation
Nava, A., Atienza, F., Bermejo, J., & Fernández‐Avilés, F. (2021) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00764.2020 · Cited by 43
Current applications of artificial intelligence in vascular surgery
Fischer, U. M., Shireman, P. K., & Lin, J. C. (2021) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2021.10.008 · Cited by 40
Sangers, T. E., Wakkee, M., Moolenburgh, F. J., Nijsten, T., & Lugtenberg, M. (2022) · Archives of Dermatological Research · doi:10.1007/s00403-022-02492-3 · Cited by 38
Clinical Decision Support Systems
Teufel, A., & Binder, H. (2021) · Visceral Medicine · doi:10.1159/000519420 · Cited by 44
Ramlakhan, S. L., Saatchi, R., Sabir, L., Singh, Y., Hughes, R., Shobayo, O., & Ventour, D. (2022) · Emergency Medicine Journal · doi:10.1136/emermed-2021-212068 · Cited by 40
Rieg, T., Frick, J., Baumgartl, H., & Buettner, R. (2020) · PLOS ONE · doi:10.1371/journal.pone.0243615 · Cited by 34
Li, J., Zhu, Y., Dong, Z., He, X., Xu, M., Liu, J., Zhang, M., Tao, X., Du, H., Chen, D., et al. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101366 · Cited by 35
Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions
Nitiéma, P. (2022) · Journal of Medical Internet Research · doi:10.2196/41138 · Cited by 40
Protecting patient safety and privacy in the era of artificial intelligence
Alonso, A. G., & Siracuse, J. J. (2023) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2023.06.002 · Cited by 37
Filho, E. M. D. S., Fernandes, F. D. A., Soares, C. L. D. A., Seixas, F. L., dos Santos, A. A. S. M. D., Gismondi, R. A., Mesquita, E. T., & Mesquita, C. T. (2019) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20180431 · Cited by 47
Zolotarev, A. M., Hansen, B. J., Ivanova, E. A., Helfrich, K. M., Li, N., Janssen, P. M. L., Mohler, P. J., Mokadam, N. A., Whitson, B. A., Fedorov, M. V., et al. (2020) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.119.008249 · Cited by 35
Chabrun, F., Dieu, X., Ferré, M., Gaillard, O., Mery, A., de la Barca, J. M. C., Taisne, A., Urbanski, G., Reynier, P., & Mirebeau‐Prunier, D. (2021) · Clinical Chemistry · doi:10.1093/clinchem/hvab133 · Cited by 34
Big Data and artificial intelligence: Will they change our practice?
Kedra, J., & Gossec, L. (2019) · Joint Bone Spine · doi:10.1016/j.jbspin.2019.09.001 · Cited by 33
Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology
Valikodath, N. G., Al-Khaled, T., Cole, E. D., Ting, D. S. W., Tu, E. Y., Campbell, J. P., Chiang, M. F., Hallak, J., & Chan, R. V. P. (2021) · Journal of American Association for Pediatric Ophthalmology and Strabismus · doi:10.1016/j.jaapos.2021.01.011 · Cited by 31
Guo, J., Chen, B., Cao, H., Dai, Q., Qin, L., Zhang, J., Zhang, Y., Zhang, H., Sui, Y., Chen, T., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00678-8 · Cited by 32
Kedra, J., Davergne, T., Braithwaite, B., Servy, H., & Gossec, L. (2021) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2022.2017773 · Cited by 33
Dahiya, D. S., Al‐Haddad, M., Chandan, S., Gangwani, M. K., Aziz, M., Mohan, B. P., Ramai, D., Canakis, A., Bapaye, J., & Sharma, N. R. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11247476 · Cited by 35
Chong, Y. Y., Ping Keung, C. H. A. N., Chan, V. W. K., Cheung, A., Luk, M. H., Cheung, M. H., Fu, H., & Chiu, P. (2023) · Arthroplasty · doi:10.1186/s42836-023-00195-2 · Cited by 31
Lam, H. Y. I., Pincket, R., Han, H., Ong, X. E., Wang, Z., Hinks, J., Wei, Y., Li, W., Zheng, L., & Mu, Y. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00683-9 · Cited by 29
Role of Artificial Intelligence in Global Surgery: A Review of Opportunities and Challenges
Malhotra, K., Wong, B. N. X., Lee, S. O., Franco, H., Singh, C. M., Silva, L. A. C., Iraqi, H. A., Sinha, A., Burger, S., Breedt, D. S., et al. (2023) · Cureus · doi:10.7759/cureus.43192 · Cited by 32
Huang, J., Fan, X., & Liu, W. (2023) · Diagnostics · doi:10.3390/diagnostics13172815 · Cited by 30
Clinical applications of AI in MSK imaging: a liability perspective
Harvey, H. B., & Gowda, V. (2022) · Skeletal Radiology · doi:10.1007/s00256-021-03782-z · Cited by 28
Chen, H., Damarjati, C., Putra, K. T., Chen, H.-M., Hsieh, C., Lin, H., Wu, M., & Chen, C. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2021.102855 · Cited by 27
Zaka, A., Mutahar, D., Gorcilov, J., Gupta, A. K., Kovoor, J. G., Stretton, B., Mridha, N., Sivagangabalan, G., Thiagalingam, A., Chow, C. K., et al. (2024) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae074 · Cited by 28
Kahraman, F., Aktaş, A., Bayrakceken, S., Çakar, T. E., Tarcan, H. S., Bayram, M. B., Durak, B., & Ülman, Y. I. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1428396 · Cited by 41
Bankuoru Egala, S., & Liang, D. (2024) · European Journal of Information Systems · doi:10.1080/0960085x.2023.2251927 · Cited by 30
Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy
Ellertsson, S., Loftsson, H., & Sigurðsson, E. L. (2021) · Scandinavian Journal of Primary Health Care · doi:10.1080/02813432.2021.1973255 · Cited by 25
Tingrun, C., Ruilong, L. I. U., Jiazheng, X. U., Fu, J. Q., Li, Z., 郝立波, Yang, J., Yanli, S. H. I., Fu, J. Q., & Chen, J. (2023) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-023-03837-y · Cited by 25
On the Opacity of Deep Neural Networks
Søgaard, A. (2023) · Canadian Journal of Philosophy · doi:10.1017/can.2024.1 · Cited by 31
Huang, T., Lin, Y., Yen, C.-H., Lan, J., Yu, C.-C., Lin, W., Chen, Y.-S., Wang, C., Huang, E., & Ho, S. (2023) · Cancer Imaging · doi:10.1186/s40644-023-00601-7 · Cited by 23
Grabarek, A., Senel, E., Menzen, T., Hoogendoorn, K. H., Pike‐Overzet, K., Hawe, A., & Jiskoot, W. (2020) · Cytotherapy · doi:10.1016/j.jcyt.2020.04.093 · Cited by 23
Geraedts, V. J., Koch, M., Kuiper, R., Kefalas, M., Bäck, T., van Hilten, J. J., Wang, H., Middelkoop, H. A. M., Gaag, N. A. V. D., Contarino, M. F., et al. (2021) · Movement Disorders · doi:10.1002/mds.28661 · Cited by 25
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
Pierre, N., Huynh‐Thu, V. A., Marichal, T., Allez, M., Bouhnik, Y., Laharie, D., Bourreille, A., Colombel, J., Meuwis, M., & Louis, E. J. (2022) · Gut · doi:10.1136/gutjnl-2022-327321 · Cited by 22
Machine learning in primary biliary cholangitis: A novel approach for risk stratification
Gerussi, A., Verda, D., Bernasconi, D. P., Carbone, M., Komori, A., Abe, M., Inao, M., Namisaki, T., Mochida, S., Yoshiji, H., et al. (2021) · Liver International · doi:10.1111/liv.15141 · Cited by 23
Cheng, R. K.-H., Aggarwal, A. K., Chakraborty, A., Harish, V., McGowan, M., Roy, A., Szulewski, A., & Nolan, B. (2024) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2024.05.020 · Cited by 21
Research Handbook on Health, AI and the Law
Biasin, E., Kamenjašević, E., & Ludvigsen, K. R. (2024) · Edward Elgar Publishing eBooks · doi:10.4337/9781802205657 · Cited by 29
Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis
Gerussi, A., Saldanha, O. L., Cazzaniga, G., Verda, D., Carrero, Z. I., Engel, B., Taubert, R., Bolis, F., Cristoferi, L., Malinverno, F., et al. (2024) · JHEP Reports · doi:10.1016/j.jhepr.2024.101198 · Cited by 22
AI in medical diagnosis: A contextualised study of patient motivations and concerns
Hajiheydari, N., Delgosha, M. S., & Saheb, T. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.117850 · Cited by 21
Approximate and Situated Causality in Deep Learning
Vallverdú, J. (2020) · Philosophies · doi:10.3390/philosophies5010002 · Cited by 21
Sobkowich, K. E. (2025) · American Journal of Veterinary Research · doi:10.2460/ajvr.24.09.0275 · Cited by 23
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
Chang, H.-H., Chiang, J.-H., Wang, C.-S., Chiu, P., Abdel-Kader, K. F. M., Chen, H., Siew, E. D., Yabes, J. G., Murugan, R., Clermont, G., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11185289 · Cited by 19
Gerussi, A., Verda, D., Cappadona, C., Cristoferi, L., Bernasconi, D. P., Bottaro, S., Carbone, M., Muselli, M., Invernizzi, P., Asselta, R., et al. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12101587 · Cited by 22
Rademakers, F. E., Biasin, E., Bruining, N., Caiani, E. G., Davies, R. H., Gilbert, S., Kamenjašević, E., McGauran, G., O’Connor, G., Rouffet, J.-B., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01459-8 · Cited by 21
Artificial Intelligence and Its Role in Predicting Periprosthetic Joint Infections
Vulpe, D. E., Anghel, C., Scheau, C., Dragosloveanu, Ş., & Săndulescu, O. (2025) · Biomedicines · doi:10.3390/biomedicines13081855 · Cited by 18
Nava, A., Arenal, Á., Fernández‐Avilés, F., & Atienza, F. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.768468 · Cited by 18
A Review of Plan-Based Approaches for Dialogue Management
Teixeira, M. S., & Dragoni, M. (2022) · Cognitive Computation · doi:10.1007/s12559-022-09996-0 · Cited by 18
A prediction model for asthma exacerbations after stopping asthma biologics
Inselman, J., Jeffery, M. M., Maddux, J. T., Lam, R. W., Shah, N. D., Rank, M. A., & Ngufor, C. G. (2022) · Annals of Allergy Asthma & Immunology · doi:10.1016/j.anai.2022.11.025 · Cited by 18
Perceived opacity leads to algorithm aversion in the workplace
Yijun, Z. H. A. O., Xu, L., Yu, F., & Wanglong, J. I. N. (2024) · Acta Psychologica Sinica · doi:10.3724/sp.j.1041.2024.00497 · Cited by 19
Big Data and Artificial Intelligence for Precision Medicine in the Neuro-ICU: Bla, Bla, Bla
Citerio, G. (2022) · Neurocritical Care · doi:10.1007/s12028-021-01427-6 · Cited by 18
Customizable Natural Language Processing Biomarker Extraction Tool
Holmes, B. R., Chitale, D. A., Loving, J., Tran, M. T., Subramanian, V., Berry, A. B., Rioth, M. J., Warrier, R., & Brown, T. D. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.21.00017 · Cited by 17
Guitton, T., Allaume, P., Rabilloud, N., Rioux-Leclercq, N. C., Henno, S., Turlin, B., Galibert, M., Lièvre, A., Lespagnol, A., Pécot, T., et al. (2023) · Diagnostics · doi:10.3390/diagnostics14010099 · Cited by 22
Bryant, A. K., Zamora‐Resendiz, R., Dai, X., Morrow, D., Lin, Y., Jungles, K. M., Rae, J. M., Tate, A., Pearson, A. N., Jiang, R., et al. (2024) · Cancer Medicine · doi:10.1002/cam4.7253 · Cited by 20
Building artificial intelligence and machine learning models : a primer for emergency physicians
Ramlakhan, S. L., Saatchi, R., Sabir, L., Ventour, D., Shobayo, O., Hughes, R., & Singh, Y. (2022) · Emergency Medicine Journal · doi:10.1136/emermed-2022-212379 · Cited by 19
Using Explainable AI (XAI) for the Prediction of Falls in the Older Population
Tang, Y. T., & Romero‐Ortuño, R. (2022) · Algorithms · doi:10.3390/a15100353 · Cited by 16
Wang, H., Sun, Q., Gu, L. C., Lai, K., & He, L. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.1006173 · Cited by 14
GMM‐LIME explainable machine learning model for interpreting sensor‐based human gait
Mulwa, M. M., Mwangi, R. W., & Mindila, A. (2024) · Engineering Reports · doi:10.1002/eng2.12864 · Cited by 20
Sun, T., & Li, Y. (2024) · American Journal of Industrial and Business Management · doi:10.4236/ajibm.2024.144024 · Cited by 17
Metadata
- Authors
- Price, W. N.
- Affiliations
- University of Copenhagen (DK)
- Corresponding author
- Price, W. Nicholson
- ORCID
- Price, W. Nicholson
- Venue
- Science Translational Medicine
- Published
- 2018-12-12
- Volume
- 10
- Issue
- 471
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- PMC
- PMC6345162
- OpenAlex
- W2905322176
- Cited by
- 254
- References
- 12
- Citations by year
- 2026: 30 · 2025: 36 · 2024: 46 · 2023: 29 · 2022: 38 · 2021: 46 · 2020: 17 · 2019: 12
- FWCI
- 5.08
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science Translational Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (1-R01-CA-214829-01-A1)
- Novo Nordisk Fonden (NNF17SA0027784)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- big data, black-box algorithms, machine learning in medicine, algorithm validation, integration into clinical practice, algorithmic transparency
- MeSH terms
- Machine Learning, Big Data, Algorithms, Social Control, Formal, Reproducibility of Results
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