Illuminating the dark spaces of healthcare with ambient intelligence

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works

Communication-Efficient Learning of Deep Networks from Decentralized Data

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

Machine learning: Trends, perspectives, and prospects

Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works

Fully homomorphic encryption using ideal lattices

Gentry, C. (2009) · Proceedings of the forty-first annual ACM symposium on Theory of computing · doi:10.1145/1536414.1536440 · Cited by 6,806 · see citing works

The global landscape of AI ethics guidelines

Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · cited 1× · see citing works

A surgical safety checklist to reduce morbidity and mortality in a global population

Haynes, A. B., Weiser, T. G., Berry, W. R., Lipsitz, S. R., Breizat, A.-H. S., Dellinger, E. P., Herbosa, T., Joseph, S., Kibatala, P. L., Lapitan, M. C. M., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmsa0810119 · Cited by 5,688 · see citing works

A guide to deep learning in healthcare

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107 · see citing works

Universal Language Model Fine-tuning for Text Classification

Howard, J., & Ruder, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1031 · Cited by 4,100 · see citing works

The size and burden of mental disorders and other disorders of the brain in Europe 2010

Wittchen, H. U., Jacobi, F., Rehm, J., Gustavsson, A., Svensson, M., Jönsson, B., Olesen, J., Allgulander, C., Alonso, J., Faravelli, C., et al. (2011) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2011.07.018 · Cited by 3,754 · see citing works

Untitled work

(2018) · Cited by 3,510 · see citing works

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement

Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · British Journal of Cancer · doi:10.1038/bjc.2014.639 · Cited by 3,312 · see citing works

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement

Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0697 · Cited by 3,267 · see citing works

Medical error—the third leading cause of death in the US

Makary, M. A., & Daniel, M. (2016) · BMJ · doi:10.1136/bmj.i2139 · Cited by 3,264 · see citing works

Generalizing from a Few Examples: A Survey on Few-shot Learning

Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2021) · ACM Computing Surveys · doi:10.1145/3386252 · Cited by 3,165 · see citing works

An overview of clinical decision support systems: Benefits, risks, and strategies for success

Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0221-y · Cited by 3,019 · see citing works

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984 · see citing works

Assessing Self‐maintenance: Activities of Daily Living, Mobility, and Instrumental Activities of Daily Living

Katz, S. (1983) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.1983.tb03391.x · Cited by 2,734 · see citing works

Model Cards for Model Reporting

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · cited 1× · see citing works

An estimation of the global volume of surgery: a modelling strategy based on available data

Weiser, T. G., Regenbogen, S. E., Thompson, K. D., Haynes, A. B., Lipsitz, S. R., Berry, W. R., & Gawande, A. A. (2008) · The Lancet · doi:10.1016/s0140-6736(08)60878-8 · Cited by 2,517 · see citing works

Image Inpainting for Irregular Holes Using Partial Convolutions

Liu, G., Reda, F. A., Shih, K. J., Wang, T.-C., Tao, A., & Catanzaro, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_6 · Cited by 2,419 · cited 1× · see citing works

The practical implementation of artificial intelligence technologies in medicine

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246 · see citing works

Definitions, methods, and applications in interpretable machine learning

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works

Dimensionality Reduction: A Comparative Review

van der Maaten, L., Postma, E. O., & van den Herik, H. J. (2009) · Cited by 2,066 · see citing works

Implementing Machine Learning in Health Care — Addressing Ethical Challenges

Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857 · see citing works

Fall risk index for elderly patients based on number of chronic disabilities

Tinetti, M. E., Franklin Williams, T., & Mayewski, R. (1986) · The American Journal of Medicine · doi:10.1016/0002-9343(86)90717-5 · Cited by 1,587 · see citing works

Supporting the health care workforce during the COVID-19 global epidemic

Adams, J. G., & Walls, R. M. (2020) · JAMA · doi:10.1001/jama.2020.3972 · Cited by 1,498 · see citing works

Artificial intelligence in surgery: promises and perils

Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002693 · Cited by 1,468 · see citing works

Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons

Scheffer, A. C., Schuurmans, M. J., van Dijk, N., van der Hooft, T., & de Rooij, S. E. (2008) · Age and Ageing · doi:10.1093/ageing/afm169 · Cited by 1,451 · see citing works

Ensuring fairness in machine learning to advance health equity.

Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018) · Annals of Internal Medicine · doi:10.7326/m18-1990 · Cited by 1,341 · see citing works

Do no harm: a roadmap for responsible machine learning for health care

Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0548-6 · Cited by 1,266 · see citing works

Falls and fall injuries among adults aged =65 years—United States, 2014.

Bergen, G., Stevens, M. R., & Burns, E. R. (2016) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6537a2 · Cited by 1,235 · see citing works

Mixed Precision Training

Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., et al. (2017) · arXiv · doi:10.48550/arxiv.1710.03740 · Cited by 1,179 · see citing works

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

Twinanda, A. P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., & Padoy, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2593957 · Cited by 1,116 · see citing works

Image retrieval using scene graphs

Johnson, J., Krishna, R., Stark, M., Li, L.-J., Shamma, D. A., Bernstein, M. S., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298990 · Cited by 1,101 · see citing works

VIBE: Video Inference for Human Body Pose and Shape Estimation

Kocabas, M., Athanasiou, N., & Black, M. J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00530 · Cited by 1,065 · see citing works

Machine learning in mental health: a scoping review of methods and applications

Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047 · see citing works

A Survey on Distributed Machine Learning

Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & Rellermeyer, J. S. (2020) · ACM Computing Surveys · doi:10.1145/3377454 · Cited by 902 · see citing works

Factors related to physician burnout and its consequences: a review

Patel, R. S., Bachu, R., Adikey, A., Malik, M., & Shah, M. (2018) · Behavioral Sciences · doi:10.3390/bs8110098 · Cited by 896 · see citing works

Visual Domain Adaptation: A survey of recent advances

Patel, V. M., Gopalan, R., Li, R., & Chellappa, R. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2347059 · Cited by 883 · see citing works

A look into the nature and causes of human errors in the intensive care unit

Donchin, Y., Gopher, D., Olin, M., Badihi, Y., Bieski, M., Sprung, C. L., Pizov, R., & Cotev, S. (1995) · Critical Care Medicine · doi:10.1097/00003246-199502000-00015 · Cited by 838 · see citing works

A Review on Recent Advances in Doppler Radar Sensors for Noncontact Healthcare Monitoring

Li, C., Lubecke, V. M., Borić-Lubecke, O., & Lin, J. (2013) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2013.2256924 · Cited by 826 · see citing works

Critical care medicine in the United States 2000–2005: An analysis of bed numbers, occupancy rates, payer mix, and costs*

Halpern, N. A., & Pastores, S. M. (2009) · Critical Care Medicine · doi:10.1097/ccm.0b013e3181b090d0 · Cited by 818 · see citing works

‘My five moments for hand hygiene’: a user-centred design approach to understand, train, monitor and report hand hygiene

Sax, H., Allegranzi, B., Uçkay, İ., Larson, E., Boyce, J. M., & Pittet, D. (2007) · Journal of Hospital Infection · doi:10.1016/j.jhin.2007.06.004 · Cited by 810 · see citing works

AI can be sexist and racist — it’s time to make it fair

Zou, J., & Schiebinger, L. (2018) · Nature · doi:10.1038/d41586-018-05707-8 · Cited by 765 · see citing works

Nosocomial infections in adult intensive-care units

Vincent, J. (2003) · The Lancet · doi:10.1016/s0140-6736(03)13644-6 · Cited by 705 · see citing works

Super-resolution: a comprehensive survey

Nasrollahi, K., & Moeslund, T. B. (2014) · Machine Vision and Applications · doi:10.1007/s00138-014-0623-4 · Cited by 689 · see citing works

Interventions to improve hand hygiene compliance in patient care

Gould, D. J., Moralejo, D. G., Drey, N., Chudleigh, J. H., & Taljaard, M. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd005186.pub4 · Cited by 672 · see citing works

Exploring Generalization in Deep Learning

Neyshabur, B., Bhojanapalli, S., McAllester, D., & Srebro, N. (2017) · neural information processing systems · Cited by 670 · see citing works

A Survey on Ambient Intelligence in Healthcare

Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2262913 · Cited by 655 · see citing works

Human fall detection on embedded platform using depth maps and wireless accelerometer

Kwolek, B., & Kepski, M. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.09.005 · Cited by 650 · see citing works

Large Batch Optimization for Deep Learning: Training BERT in 76 minutesPreprint

You, Y., Li, J., Reddi, S. J., Hseu, J., Kumar, S., Bhojanapalli, S., Song, X., Demmel, J. W., Keutzer, K., & Hsieh, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.00962 · Cited by 597 · see citing works

Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies

Sadeghian, A., Alahi, A., & Savarese, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.41 · Cited by 567 · see citing works

Acute Outcomes and 1-Year Mortality of Intensive Care Unit–acquired Weakness. A Cohort Study and Propensity-matched Analysis

Hermans, G., Mechelen, H. V., Clerckx, B., Vanhullebusch, T., Mesotten, D., Wilmer, A. P., Casaer, M. P., Meersseman, P., Debaveye, Y. A., Cromphaut, S. J. V., et al. (2014) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201312-2257oc · Cited by 536 · see citing works

Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease

Galna, B., Barry, G., Jackson, D., Mhiripiri, D., Olivier, P. L., & Rochester, L. (2014) · Gait & Posture · doi:10.1016/j.gaitpost.2014.01.008 · Cited by 536 · see citing works

Fall Detection in Homes of Older Adults Using the Microsoft Kinect

Stone, E. E., & Skubic, M. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2312180 · Cited by 533 · see citing works

On Fairness and CalibrationPreprint

Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J. M., & Weinberger, K. Q. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02012 · Cited by 509 · see citing works

Time-driven activity-based costing in health care: A systematic review of the literature

Keel, G., Savage, C., Rafiq, M., & Mazzocato, P. (2017) · Health Policy · doi:10.1016/j.healthpol.2017.04.013 · Cited by 456 · see citing works

Monocular human pose estimation: A survey of deep learning-based methods

Chen, Y., Tian, Y., & He, M. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102897 · Cited by 455 · see citing works

Early mobilization of critically ill patients in the intensive care unit: A systematic review and meta-analysis

Zhang, L., Hu, W., Cai, Z., Liu, J., Wu, J., Deng, Y., Yu, K., Chen, X., Zhu, L., Ma, J., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0223185 · Cited by 454 · see citing works

A Systematic Review of Re-Identification Attacks on Health Data

El Emam, K., Jonker, E., Arbuckle, L., & Malin, B. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028071 · Cited by 427 · see citing works

Prevention of Falls in Community-Dwelling Older Adults

Ganz, D. A., & Latham, N. K. (2020) · New England Journal of Medicine · doi:10.1056/nejmcp1903252 · Cited by 425 · see citing works

Can AI Help Reduce Disparities in General Medical and Mental Health Care?

(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.167 · Cited by 401 · see citing works

Bias in Mental Health Assessment and Intervention: Theory and Evidence

Snowden, L. R. (2003) · American Journal of Public Health · doi:10.2105/ajph.93.2.239 · Cited by 371 · see citing works

Value-Based Health Care Delivery

Porter, M. E. (2008) · Annals of Surgery · doi:10.1097/sla.0b013e31818a43af · Cited by 367 · see citing works

The global landscape of AI ethics guidelines

Jobin, A., Ienca, M., & Vayena, E. (2019) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000375277 · Cited by 360 · see citing works

A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

Wang, Z., Yang, Z., & Dong, T. (2017) · Sensors · doi:10.3390/s17020341 · Cited by 357 · see citing works

How dangerous are falls in old people at home?

Wild, D., Nayak, U. S., & Isaacs, B. (1981) · BMJ · doi:10.1136/bmj.282.6260.266 · Cited by 344 · see citing works

Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks

Jin, A., Yeung, S., Jopling, J., Krause, J., Azagury, D., Milstein, A., & Fei-Fei, L. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00081 · Cited by 323 · see citing works

Efficacy of Clinical Gait Analysis: A Systematic Review

Wren, T. A. L., Gorton, G. E., Õunpuu, S., & Tucker, C. A. (2011) · Gait & Posture · doi:10.1016/j.gaitpost.2011.03.027 · Cited by 312 · see citing works

Hidden Two-Stream Convolutional Networks for Action Recognition

Zhu, Y., Lan, Z., Newsam, S. D., & Hauptmann, A. G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_23 · Cited by 300 · see citing works

Surgical adverse events: a systematic review

Anderson, O., Davis, R. E., Hanna, G. B., & Vincent, C. A. (2013) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2012.11.009 · Cited by 291 · see citing works

Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos

Heilbron, F. C., Niebles, J. C., & Ghanem, B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.211 · Cited by 279 · see citing works

Comprehensive Surgical Coaching Enhances Surgical Skill in the Operating Room: A Randomized Controlled Trial

Bonrath, E. M., Dedy, N. J., Gordon, L. E., & Grantcharov, T. P. (2015) · Annals of Surgery · doi:10.1097/sla.0000000000001214 · Cited by 260 · see citing works

Doppler Radar Fall Activity Detection Using the Wavelet Transform

Su, B.-Y., Ho, K. C., Rantz, M., & Skubic, M. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2367038 · Cited by 242 · see citing works

Surgical robotics beyond enhanced dexterity instrumentation: A survey of machine learning techniques and their role in intelligent and autonomous surgical actions

Kassahun, Y., Yu, B., Tibebu, A. T., Stoyanov, D., Giannarou, S., Metzen, J. H., & Vander Poorten, E. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1305-z · Cited by 238 · see citing works

Explainable and Explicit Visual Reasoning Over Scene Graphs

Shi, J., Zhang, H., & Li, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00857 · Cited by 234 · see citing works

Therapist effects in outpatient psychotherapy: A three-level growth curve approach.

Lutz, W., Leon, S. C., Martinovich, Z., Lyons, J. S., & Stiles, W. B. (2007) · Journal of Counseling Psychology · doi:10.1037/0022-0167.54.1.32 · Cited by 233 · see citing works

Trends in Critical Care Beds and Use Among Population Groups and Medicare and Medicaid Beneficiaries in the United States: 2000–2010

Halpern, N. A., Goldman, D. A., Tan, K. S., & Pastores, S. M. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001722 · Cited by 224 · see citing works

The Use of Wearable Activity Trackers Among Older Adults: Focus Group Study of Tracker Perceptions, Motivators, and Barriers in the Maintenance Stage of Behavior Change

Kononova, A. G., Li, L., Kamp, K., Bowen, M., Rikard, R. V., Cotten, S. R., & Peng, W. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9832 · Cited by 224 · see citing works

First-year Analysis of the Operating Room Black Box Study

Jung, J. J., Jüni, P., Lebovic, G., & Grantcharov, T. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002863 · Cited by 222 · see citing works

Activity Recognition for the Smart Hospital

Sánchez-Cortés, D., Tentori, M. E., & Favela, J. (2008) · IEEE Intelligent Systems · doi:10.1109/mis.2008.18 · Cited by 220 · see citing works

Clinical review: Early patient mobilization in the ICU

Hodgson, C. L., Berney, S. C., Harrold, M., et al. (2012) · Critical Care · doi:10.1186/cc11820 · Cited by 220 · see citing works

Fall Detection Devices and Their Use With Older Adults: A Systematic Review

Chaudhuri, S., Thompson, H., & Demiris, G. (2014) · Journal of Geriatric Physical Therapy · doi:10.1519/jpt.0b013e3182abe779 · Cited by 215 · see citing works

Gait analysis with wearables predicts conversion to Parkinson disease

Din, S. D., Elshehabi, M., Galna, B., Hobert, M. A., Warmerdam, E., Suenkel, U., Brockmann, K., Metzger, F. G., Hansen, C., Berg, D., et al. (2019) · Annals of Neurology · doi:10.1002/ana.25548 · Cited by 211 · see citing works

Using Time-Driven Activity-Based Costing to Identify Value Improvement Opportunities in Healthcare

Kaplan, R. S., Witkowski, M. L., Abbott, M. M., et al. (2014) · Journal of Healthcare Management · doi:10.1097/00115514-201411000-00005 · Cited by 210 · see citing works

Quantification of the Hawthorne effect in hand hygiene compliance monitoring using an electronic monitoring system: a retrospective cohort study

Srigley, J. A., Furness, C. D., Baker, G. R., & Gardam, M. A. (2014) · BMJ Quality & Safety · doi:10.1136/bmjqs-2014-003080 · Cited by 203 · see citing works

Wrong-Site Surgery, Retained Surgical Items, and Surgical Fires

Hempel, S., Maggard-Gibbons, M. A., Nguyen, D. K., Dawes, A. J., Miake-Lye, I. M., Beroes, J. M., Booth, M. J., Miles, J. N. V., Shanman, R., & Shekelle, P. (2015) · JAMA Surgery · doi:10.1001/jamasurg.2015.0301 · Cited by 196 · see citing works

Fast action proposals for human action detection and search

Yu, G., & Yuan, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298735 · Cited by 190 · see citing works

Using Smart City Technology to Make Healthcare Smarter

Cook, D. J., Duncan, G. E., Sprint, G., & Fritz, R. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2787688 · Cited by 184 · see citing works

Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning

Davoudi, A., Malhotra, K. R., Shickel, B., Siegel, S., Williams, S., Ruppert, M. M., Bihorac, E., Ozrazgat‐Baslanti, T., Tighe, P. J., Bihorac, A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-44004-w · Cited by 184 · see citing works

The normal surface of conjunctiva epithelium. A scanning electron microscopic study.

Pfister, R. R. (1973) · PubMed · Cited by 177 · see citing works

Ambient Sensors for Elderly Care and Independent Living: A Survey

Uddin, M. Z., Khaksar, W., & Torresen, J. (2018) · Sensors · doi:10.3390/s18072027 · Cited by 166 · see citing works

Evaluation of Deep Learning Models for Identifying Surgical Actions and Measuring Performance

Khalid, S., Goldenberg, M. G., Grantcharov, T., Taati, B., & Rudzicz, F. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.1664 · Cited by 159 · see citing works

All‐Cause 1‐, 5‐, and 10‐Year Mortality in Elderly People According to Activities of Daily Living Stage

Stineman, M. G., Xie, D., Pan, Q., Kurichi, J. E., Zhang, Z. M., Saliba, D., Henry‐Sánchez, J. T., & Streim, J. E. (2012) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2011.03867.x · Cited by 155 · see citing works

A vision-based analysis system for gait recognition in patients with Parkinson’s disease

Cho, C., Chao, W.-H., Lin, S.-H., et al. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.08.076 · Cited by 151 · see citing works

Time spent on dedicated patient care and documentation tasks before and after the introduction of a structured and standardized electronic health record

Joukes, E., Abu-Hanna, A., Cornet, R., & de Keizer, N. (2018) · Applied Clinical Informatics · doi:10.1055/s-0037-1615747 · Cited by 150 · see citing works

Putting the data before the algorithm in big data addressing personalized healthcare

Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S., & Rubin, D. L. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0157-2 · Cited by 149 · cited 1× · see citing works

Motor Rehabilitation Using Kinect: A Systematic Review

Gama, A. E. F. D., Fallavollita, P., Teichrieb, V., & Navab, N. (2015) · Games for Health Journal · doi:10.1089/g4h.2014.0047 · Cited by 146 · see citing works

The Frequency and Significance of Discrepancies in the Surgical Count

Greenberg, C. C., Regenbogen, S. E., Lipsitz, S. R., et al. (2008) · Annals of Surgery · doi:10.1097/sla.0b013e318181c9a3 · Cited by 134 · see citing works

Use of wearable sensor technology in gait, balance, and range of motion analysis

Díaz, S., Stephenson, J. B., & Labrador, M. A. (2019) · Applied Sciences · doi:10.3390/app10010234 · Cited by 132 · see citing works

A computer vision system for deep learning-based detection of patient mobilization activities in the ICU

Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N. L., Guo, M., Bianconi, G. M., Alahi, A., Lee, J., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0087-z · Cited by 125 · see citing works

Automatic real-time gait event detection in children using deep neural networks

Kidziński, Ł., Delp, S. L., & Schwartz, M. H. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211466 · Cited by 122 · see citing works

Bedside Computer Vision — Moving Artificial Intelligence from Driver Assistance to Patient Safety

Yeung, S. Y., Downing, N. L., Fei-Fei, L., & Milstein, A. S. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1716891 · Cited by 118 · see citing works

Broviac catheter sepsis: The natural history of an iatrogenic infection

King, D. R., Komer, M., Hoffman, J., et al. (1985) · Journal of Pediatric Surgery · doi:10.1016/s0022-3468(85)80034-8 · Cited by 117 · see citing works

"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing

Xiao, B., Imel, Z. E., Georgiou, P., Atkins, D. C., & Narayanan, S. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0143055 · Cited by 111 · see citing works

Quantifying the association between psychotherapy content and clinical outcomes using deep learning

Ewbank, M. P., Cummins, R., Tablan, V., Bateup, S., Catarino, A., Martin, A. J., & Blackwell, A. D. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.2664 · Cited by 108 · see citing works

Equipment Location in Hospitals Using RFID-Based Positioning System

Shirehjini, A. A. N., Yassine, A., & Shirmohammadi, S. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2204896 · Cited by 102 · see citing works

Surgical Skill Assessment Using Motion Quality and Smoothness

Ghasemloonia, A., Maddahi, Y., Zareinia, K., et al. (2016) · Journal of surgical education · doi:10.1016/j.jsurg.2016.10.006 · Cited by 99 · see citing works

Speech recognition for clinical documentation from 1990 to 2018: a systematic review

Blackley, S. V., Huynh, J., Wang, L., Korach, Z., & Zhou, L. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy179 · Cited by 97 · see citing works

How do impairments in cognitive functions affect activities of daily living functions in older adults?

Lee, M.-T., Jang, Y., & Chang, W.-Y. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218112 · Cited by 96 · see citing works

Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders

Rantz, M., Skubic, M., Abbott, C. C., et al. (2015) · The Gerontologist · doi:10.1093/geront/gnv044 · Cited by 95 · see citing works

Activities of Daily Living Function and Disability in Older Adults in a Randomized Trial of the Health Enhancement Program

Phelan, E. A., Williams, B., Penninx, B. W. J. H., LoGerfo, J. P., & Leveille, S. G. (2004) · The Journals of Gerontology Series A · doi:10.1093/gerona/59.8.m838 · Cited by 90 · see citing works

A Nurse-Driven Method for Developing Artificial Intelligence in “Smart” Homes for Aging-in-Place

Fritz, R., & Dermody, G. (2018) · Nursing Outlook · doi:10.1016/j.outlook.2018.11.004 · Cited by 88 · see citing works

Health Monitors for Chronic Disease by Gait Analysis with Mobile Phones

Juen, J. P., Cheng, Q., Prieto-Centurion, V., Krishnan, J. A., & Schatz, B. R. (2014) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2014.0025 · Cited by 85 · see citing works

Interactive Rehabilitation System for Improvement of Balance Therapies in People With Cerebral Palsy

Jaume-i-Capó, A., Martínez-Bueso, P., Moyà-Alcover, B., & Varona, J. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2279155 · Cited by 84 · see citing works

Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene CompliancePreprint

Haque, A., Guo, M., Alahi, A., Yeung, S. Y., Luo, Z., Rege, A. M., Jopling, J. K., Downing, N. L., Beninati, W., Singh, A. T., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00163 · Cited by 84 · see citing works

Outcomes of lower extremity orthopedic surgery in ambulatory children with cerebral palsy with and without gait analysis: Results of a randomized controlled trial

Wren, T. A. L., Otsuka, N. Y., Bowen, R. E., et al. (2012) · Gait & Posture · doi:10.1016/j.gaitpost.2012.11.018 · Cited by 83 · see citing works

Assessing the accuracy of automatic speech recognition for psychotherapy

Miner, A. S., Haque, A., Fries, J., Fleming, S. L., Wilfley, D. E., Wilson, G. T., Milstein, A. S., Jurafsky, D., Arnow, B. A., Agras, W. S., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0285-8 · Cited by 82 · see citing works

A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department

Pestian, J. P., Grupp‐Phelan, J., Cohen, K. B., Meyers, G., Richey, L., Matykiewicz, P., & Sorter, M. T. (2015) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12180 · Cited by 81 · see citing works

Measuring the value of process improvement initiatives in a preoperative assessment center using time-driven activity-based costing

French, K. E., Albright, H. W., Frenzel, J. C., et al. (2013) · Healthcare · doi:10.1016/j.hjdsi.2013.07.007 · Cited by 77 · see citing works

Older People’s Perspectives Regarding the Use of Sensor Monitoring in Their Home: Table 1.

Pol, M., van Nes, F., van Hartingsveldt, M., et al. (2014) · The Gerontologist · doi:10.1093/geront/gnu104 · Cited by 76 · see citing works

Effect of scribes on patient throughput, revenue, and patient and provider satisfaction: a systematic review and meta-analysis

Heaton, H. A., Castañeda-Guarderas, A., Trotter, E. R., Erwin, P. J., & Bellolio, F. (2016) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2016.07.056 · Cited by 75 · see citing works

Current Status of Technical Skills Assessment Tools in Surgery: A Systematic Review

Vaidya, A., Aydın, A., Ridgley, J., Raison, N. T. J., Prokar Dasgupta, A., & Ahmed, K. (2019) · Journal of Surgical Research · doi:10.1016/j.jss.2019.09.006 · Cited by 74 · see citing works

Early Experiences With Big Data At An Academic Medical Center

Halamka, J. D. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0031 · Cited by 73 · see citing works

Speech Recognition for Medical Conversations

Chiu, C., Tripathi, A., Chou, K., Co, C., Jaitly, N., Jaunzeikare, D., Kannan, A., Nguyen, P., Sak, H., Sankar, A., et al. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-40 · Cited by 73 · see citing works

Randomized Trial of Intelligent Sensor System for Early Illness Alerts in Senior Housing

Rantz, M., Phillips, L. J., Galambos, C. M., Lane, K. R., Alexander, G. L., Despins, L. A., Koopman, R. J., Skubic, M., Hicks, L. L., Miller, S. J., et al. (2017) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2017.05.012 · Cited by 72 · see citing works

Range-Doppler radar sensor fusion for fall detection

Erol, B., Amin, M. G., & Boashash, B. (2017) · Proceedings of the IEEE National Radar Conference · doi:10.1109/radar.2017.7944316 · Cited by 70 · see citing works

A Context Encoder For Audio Inpainting

Marafioti, A., Perraudin, N., Holighaus, N., & Majdak, P. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2947232 · Cited by 70 · see citing works

Using a Data-Matrix–Coded Sponge Counting System Across a Surgical Practice: Impact After 18 Months

Cima, R. R., Kollengode, A., Clark, J., et al. (2011) · The Joint Commission Journal on Quality and Patient Safety · doi:10.1016/s1553-7250(11)37007-9 · Cited by 63 · see citing works

Automatic detection of hand hygiene using computer vision technology

Singh, A., Haque, A., Alahi, A., Yeung, S., Guo, M., Glassman, J. R., Beninati, W., Platchek, T., Fei-Fei, L., & Milstein, A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa115 · Cited by 61 · see citing works

Acoustic Gaits: Gait Analysis With Footstep Sounds

Altaf, M. U. B., Butko, T., & Juang, B.-H. F. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2410142 · Cited by 59 · see citing works

DNA packaging of bacteriophage T4 proheads in vitro evidence that prohead expansion is not coupled to DNA packaging

Rao, V. B., & Black, L. W. (1985) · Journal of Molecular Biology · doi:10.1016/0022-2836(85)90072-5 · Cited by 59 · see citing works

Effectiveness of a Radiofrequency Detection System as an Adjunct to Manual Counting Protocols for Tracking Surgical Sponges: A Prospective Trial of 2,285 Patients

Rupp, C. C., Kagarise, M. J., Nelson, S. M., et al. (2012) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2012.06.014 · Cited by 57 · see citing works

Ethical and legal aspects of ambient intelligence in hospitals

Gerke, S., Yeung, S., & Cohen, I. G. (2020) · JAMA · doi:10.1001/jama.2019.21699 · Cited by 57 · see citing works

A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms

Kadkhodamohammadi, A., Gangi, A., de Mathelin, M., & Padoy, N. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.47 · Cited by 56 · see citing works

Data sharing at a crossroads

Rockhold, F., Nisen, P., & Freeman, A. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1608086 · Cited by 55 · see citing works

Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor

Ma, A. J., Rawat, N., Reiter, A., Shrock, C., Zhan, A., Stone, A., Rabiee, A., Griffin, S., Needham, D. M., & Saria, S. (2017) · Critical Care Medicine · doi:10.1097/ccm.0000000000002265 · Cited by 53 · see citing works

A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults

Tegou, T. I., Kalamaras, I., Tsipouras, M. G., Γιαννακέας, Ν., Votis, K., & Tzovaras, D. K. (2019) · Sensors · doi:10.3390/s19030452 · Cited by 53 · see citing works

Effectiveness of sensor monitoring in a rehabilitation programme for older patients after hip fracture: a three-arm stepped wedge randomised trial

Pol, M., ter Riet, G., van Hartingsveldt, M., et al. (2019) · Age and Ageing · doi:10.1093/ageing/afz074 · Cited by 53 · see citing works

Use of Accelerometry to Monitor Physical Activity in Critically Ill Subjects: A Systematic Review

Verceles, A. C., & Hager, E. R. (2015) · Respiratory Care · doi:10.4187/respcare.03677 · Cited by 51 · see citing works

Privacy-preserving aggregation of personal health data streams

Kim, J. W., Jang, B., & Yoo, H. (2018) · PLOS ONE · doi:10.1371/journal.pone.0207639 · Cited by 49 · see citing works

The Relationship between Older Adults’ Risk for a Future Fall and Difficulty Performing Activities of Daily Living

Mamikonian, A., & Laganà, L. (2015) · Journal of Aging and Gerontology · doi:10.12974/2309-6128.2015.03.01.2 · Cited by 47 · see citing works

Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural NetworksPreprint

Jin, A., Yeung, S. Y., Jopling, J. K., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.08774 · Cited by 42 · see citing works

Rural ED transfers due to lack of radiology services

Lyon, M. L., Sturgis, L. C., Lendermon, D., et al. (2015) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2015.07.050 · Cited by 35 · see citing works

An Automatic Acoustic Bathroom Monitoring System

Chen, J., Zhang, J., Kam, A. H., et al. (2005) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2005.1464946 · Cited by 35 · see citing works

Public Health Practice Is Not Research

Otto, J. L., Holodniy, M., & DeFraites, R. F. (2014) · American Journal of Public Health · doi:10.2105/ajph.2013.301663 · Cited by 31 · see citing works

Deliberative democracy and cancer screening consent: a randomised control trial of the effect of a community jury on men's knowledge about and intentions to participate in PSA screening

Thomas, R., Glasziou, P., Rychetnik, L., et al. (2014) · BMJ Open · doi:10.1136/bmjopen-2014-005691 · Cited by 29 · see citing works

10.1016/j.ysur.2016.04.053

(2000) · Time to knit · doi:10.1016/j.ysur.2016.04.053 · Cited by 28 · see citing works

Use of simulation based on an electronic health records environment to evaluate the structure and accuracy of notes generated by medical scribes: proof-of-concept study

Pranaat, R., Mohan, V., O'Reilly, M., Hirsh, M., McGrath, K., Scholl, G., Woodcock, D., & Gold, J. A. (2017) · JMIR Medical Informatics · doi:10.2196/medinform.7883 · Cited by 26 · see citing works

The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene

Awwad, S., Tarvade, S., Piccardi, M., & Gattas, D. J. (2018) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzy099 · Cited by 26 · see citing works

Quantitatve assay of dissociated tissue cell motility in vitro

Stenn, K. S. (1980) · In Vitro Cellular & Developmental Biology - Plant · doi:10.1007/bf02618342 · Cited by 23 · see citing works

Real world activity summary for senior home monitoring

Cheng, H., Liu, Z., Zhao, Y., Ye, G., & Sun, X. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1162-5 · Cited by 23 · see citing works

Oxidation of 3‐oxygenated Δ4‐ and Δ5‐C27 steroids by soybean lipoxygenase and rat liver microsomessteroids by soybean lipoxygenase and rat liver microsomes

Aringer, L. (1980) · Lipids · doi:10.1007/bf02534180 · Cited by 21 · see citing works

Self-reported versus professionally assessed functional limitations in community-dwelling very old individuals

Carlsson, G., Haak, M., Nygren, C., et al. (2012) · International Journal of Rehabilitation Research · doi:10.1097/mrr.0b013e3283544d07 · Cited by 19 · see citing works

Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms

Chen, J., Cremer, J. F., Zarei, K., et al. (2015) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofv200 · Cited by 19 · see citing works

Counting Matters: Lessons from the Root Cause Analysis of a Retained Surgical Item

Agrawal, A. (2012) · The Joint Commission Journal on Quality and Patient Safety · doi:10.1016/s1553-7250(12)38074-4 · Cited by 19 · see citing works

Differences in the isozymes involved in alcohol metabolism between caucasians and orientals.

Yoshida, A. (1983) · PubMed · Cited by 15 · see citing works

Assessment and prediction of negative symptoms of schizophrenia from RGB+D movement signals

Chakraborty, D., Tahir, Y., Yang, Z., et al. (2017) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2017.8122218 · Cited by 13 · see citing works

Exploring Error in Team-Based Acute Care Scenarios

Tallentire, V. R., Smith, S. E., Skinner, J., & Cameron, H. S. (2012) · Academic Medicine · doi:10.1097/acm.0b013e318253c9e0 · Cited by 11 · see citing works

Estimation of the Postmortem Interval by Measuring Blood Oxidation-reduction Potential Values

Yang, T., Jiang, Z., You, M., et al. (2015) · Journal of Forensic Science and Medicine · doi:10.4103/2349-5014.155727 · Cited by 8 · see citing works

The impact of working as a medical scribe

Rich, N. J. (2016) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2016.12.020 · Cited by 7 · see citing works

Multimodal gait analysis based on wearable inertial and microphone sensors

Wang, C., Wang, X., Long, Z., Yuan, J., Qian, Y., & Li, J. (2017) · 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/uic-atc.2017.8397481 · Cited by 7 · see citing works

F5‐05‐04: Ecologically Valid Assessment of Life Activities: Unobtrusive Continuous Monitoring with Sensors

Kaye, J. A., Austin, J., Dodge, H. H., et al. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.06.702 · Cited by 6 · see citing works

Evaluation of Medical Malpractice Litigations in China, 2002-2011

Li, L., Yang, T., Zhang, G., Wang, X., Di, S., Zhang, H., Sun, D., Xiang, S., Zhao, D., & Chang, L. (2016) · Journal of Forensic Science and Medicine · doi:10.4103/2349-5014.197931 · Cited by 6 · see citing works

Elderly care: activities of daily living classification with an S band radar

Shrestha, A., Kernec, J. L., Fioranelli, F., et al. (2019) · The Journal of Engineering · doi:10.1049/joe.2019.0561 · Cited by 6 · see citing works

Burn Recovery: Transition to Full Employment

Murray, K. H. (1983) · Occupational Health Nursing · doi:10.1177/216507998303100205 · Cited by 5 · see citing works

Detection of Gait Asymmetry Using Indoor Doppler Radar

Seifert, A.-K., Zoubir, A. M., & Amin, M. G. (2019) · Proceedings of the IEEE National Radar Conference · doi:10.1109/radar.2019.8835611 · Cited by 3 · see citing works

The Telescoped Overdenture: Advantages and Limitations

Pardo, G. I., & Renner, R. P. (1980) · The Journal of the American Dental Association · doi:10.14219/jada.archive.1980.0457 · Cited by 2 · see citing works

Respiratory manifestations of systemic disease

Prakash, U. B. S. (1984) · Postgraduate Medicine · doi:10.1080/00325481.1984.11698720 · Cited by 2 · see citing works

Wert der Angiographie zur Differentialdiagnose der benignen und malignen Strumen

Petračić, B., Schmidt, H., & Weber, V. (1970) · Langenbeck s Archives of Surgery · doi:10.1007/bf01259015 · Cited by 1 · see citing works

[Some aids for the handicapped].

Rygaard, K., & Soegaard, O. (1974) · PubMed · Cited by 1 · see citing works

Changes of the blood circulation in the spinal cord upon cross section.

Ichev, K., & Gulubov, G. (1968) · PubMed · Cited by 1 · see citing works

[The importance of the use of auxiliary personnel in dentistry].

Viegas, A. R. (1966) · PubMed · Cited by 1 · see citing works

Carotid occlusions intentional and spontaneous.

Flanigan, S., Wj, G., & Sp, B. (1967) · PubMed · Cited by 1 · see citing works

[The health education at Taipei Municipal Jen-Ai Hospital].

Ml, P. (1980) · PubMed · Cited by 1 · see citing works

[Contribution to the study of vegetative irritability examined by the orthostatic and clinostatic method].

Joachimsthaler, J., Chaloupková, E., & Chaloupka, Z. (1968) · PubMed · Cited by 1 · see citing works

[Control over the prevention of postural disorders in children].

Vorotilova, S. I. (1988) · PubMed · Cited by 1 · see citing works

[Measurement of serum plasminogen by Hedner-Nilsson method].

Maki, M., Nagayama, M., & Sato, T. (1967) · PubMed · Cited by 1 · see citing works

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works

Artificial intelligence in healthcare: transforming the practice of medicine

Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2021-0095 · Cited by 1,856 · see citing works

Multimodal biomedical AI

Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · see citing works

A comprehensive review on ensemble deep learning: Opportunities and challenges

Mohammed, A., & Kora, R. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.01.014 · Cited by 1,127 · see citing works

A programmable diffractive deep neural network based on a digital-coding metasurface array

Liu, C., Ma, Q., Luo, Z. J., Hong, Q. R., Xiao, Q., Zhang, H. C., Miao, L., Yu, W. M., Cheng, Q., Li, L., et al. (2022) · Nature Electronics · doi:10.1038/s41928-022-00719-9 · Cited by 633 · see citing works

Deep Learning applications for COVID-19

Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal of Big Data · doi:10.1186/s40537-020-00392-9 · Cited by 350 · see citing works

Ensuring that biomedical AI benefits diverse populations

Zou, J., & Schiebinger, L. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103358 · Cited by 124 · see citing works

Big data analytics in healthcare

Guo, C., & Chen, J. (2023) · Translational Systems Sciences · doi:10.1007/978-981-99-1075-5_2 · Cited by 90 · see citing works

Learning Privacy-preserving Optics for Human Pose Estimation

Hinojosa, C., Niebles, J. C., & Arguello, H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00257 · Cited by 62 · see citing works

Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms

Rieg, T., Frick, J., Baumgartl, H., & Buettner, R. (2020) · PLOS ONE · doi:10.1371/journal.pone.0243615 · Cited by 34 · see citing works

Agent transparency in mixed-initiative multi-UxV control: How should intelligent agent collaborators speak their minds?

Wohleber, R. W., Stowers, K. L., Barnes, M., & Chen, J. Y. C. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107866 · Cited by 19 · see citing works

Democratic Transparency in the Platform Society

Gorwa, R., & Ash, T. G. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108890960.013 · Cited by 67 · see citing works

The Problem with Government TransparencyPreprint

Verdon, L. L. (2008) · SSRN Electronic Journal

Workflow Reuse in Practice: A Study of Neuroimaging Pipeline Users

Garijo, D., Corcho, Ó., Gil, Y., et al. (2014) · IEEE International Conference on eScience · doi:10.1109/escience.2014.33 · Cited by 14 · see citing works

A good man but a bad wizard. About the limits and future of transparency of democratic governments

GrimmelikhuijsenStephan (2012) · Information Polity · doi:10.5555/2656990.2656998