Questions for artificial intelligence in health care

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3763 · Cited by 1,395 · see citing works

Fully Automated Echocardiogram Interpretation in Clinical Practice: Feasibility and Diagnostic Accuracy

Zhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., Lassen, M. H., Fan, E., Aras, M. A., Jordan, C., et al. (2018) · Circulation · doi:10.1161/circulationaha.118.034338 · Cited by 1,006 · see citing works

What This Computer Needs Is a Physician: Humanism and Artificial Intelligence

Verghese, A., Shah, N. H., & Harrington, R. A. (2018) · JAMA · doi:10.1001/jama.2017.19198 · Cited by 468 · see citing works

Clinical Implications and Challenges of Artificial Intelligence and Deep Learning

Stead, W. W. (2018) · JAMA · doi:10.1001/jama.2018.11029 · Cited by 250 · see citing works

Artificial Intelligence in Dentistry: Chances and Challenges

Schwendicke, F., Samek, W., & Krois, J. (2020) · Journal of Dental Research · doi:10.1177/0022034520915714 · Cited by 1,446 · see citing works

Artificial intelligence literacy in higher and adult education: A scoping literature review

Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100101 · Cited by 658 · see citing works

"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making

Cai, C. J., Winter, S., Steiner, D., Wilcox, L., & Terry, M. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359206 · Cited by 594 · see citing works

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

Peiffer-Smadja, N., Rawson, T. M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F.-X., Birgand, G., & Holmes, A. H. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2019.09.009 · Cited by 561 · see citing works

Towards a standard for identifying and managing bias in artificial intelligence

Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022) · doi:10.6028/nist.sp.1270 · Cited by 548 · see citing works

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

Abels, E., Pantanowitz, L., Aeffner, F., Zarella, M. D., van der Laak, J. A. W. M., Bui, M. M., Vemuri, V. N. P., Parwani, A. V., Gibbs, J., Agosto‐Arroyo, E., et al. (2019) · The Journal of Pathology · doi:10.1002/path.5331 · Cited by 460 · see citing works

Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers

Kim, D. W., Jang, H. Y., Kim, K. W., Shin, Y., & Park, S. H. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0025 · Cited by 459 · see citing works

Role of artificial intelligence in patient safety outcomes: systematic literature review

Choudhury, A., & Asan, O. (2020) · JMIR Medical Informatics · doi:10.2196/18599 · Cited by 440 · see citing works

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

Lee, E., Torous, J. B., Choudhury, M. D., Depp, C. A., Graham, S. A., Kim, H., Paulus, M. P., Krystal, J. H., & Jeste, D. V. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.02.001 · Cited by 477 · see citing works

Identifying Ethical Considerations for Machine Learning Healthcare Applications

Char, D. S., Abràmoff, M. D., & Feudtner, C. (2020) · The American Journal of Bioethics · doi:10.1080/15265161.2020.1819469 · Cited by 415 · see citing works

Artificial intelligence for clinical oncology

Kann, B. H., Hosny, A., & Aerts, H. J. W. L. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.04.002 · Cited by 387 · see citing works

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review

Sapci, A. H., & Sapci, H. A. (2020) · JMIR Medical Education · doi:10.2196/19285 · Cited by 377 · see citing works

Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning

Chen, M., Zhang, B., Topatana, W., Cao, J., Zhu, H.-P., Juengpanich, S., Mao, Q., Yu, H., & Cai, X. (2020) · npj Precision Oncology · doi:10.1038/s41698-020-0120-3 · Cited by 246 · see citing works

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients

Fritsch, S. J., Blankenheim, A., Wahl, A., Hetfeld, P., Maaßen, O., Deffge, S., Kunze, J., Rossaint, R., Riedel, M., Marx, G., et al. (2022) · Digital Health · doi:10.1177/20552076221116772 · Cited by 255 · see citing works

Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence

Coccia, M. (2019) · Technology in Society · doi:10.1016/j.techsoc.2019.101198 · Cited by 234 · see citing works

Design, development and evaluation of a human-computer trust scale

Gulati, S., Sousa, S., & Lamas, D. (2019) · Behaviour & Information Technology · doi:10.1080/0144929x.2019.1656779 · Cited by 229 · see citing works

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

Blasiak, A., Khong, J., & Kee, T. W. (2019) · SLAS TECHNOLOGY · doi:10.1177/2472630319890316 · Cited by 225 · see citing works

Artificial Intelligence, Machine Learning, and Cardiovascular Disease

Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020) · Clinical Medicine Insights Cardiology · doi:10.1177/1179546820927404 · Cited by 223 · see citing works

Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future

Rony, M. K. K., Parvin, M. R., & Ferdousi, S. (2024) · Nursing Open · doi:10.1002/nop2.2070 · Cited by 202 · see citing works

What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review

Langerhuizen, D. W. G., Janssen, S. J., Mallee, W. H., van den Bekerom, M. P. J., Ring, D., Kerkhoffs, G. M. M. J., Jaarsma, R. L., & Doornberg, J. N. (2019) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000000848 · Cited by 197 · see citing works

What should medical students know about artificial intelligence in medicine?

Park, S. H., Do, K.-H., Kim, S., Park, J. H., & Lim, Y.-S. (2019) · Journal of Educational Evaluation for Health Professions · doi:10.3352/jeehp.2019.16.18 · Cited by 196 · see citing works

Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions

Javeed, A., Dallora, A., Berglund, J., Ali, A., Ali, L., & Anderberg, P. (2023) · Journal of medical systems · doi:10.1007/s10916-023-01906-7 · Cited by 173 · see citing works

Electronic Health Record Data Quality Assessment and Tools: A Systematic Review

Lewis, A. E., Weiskopf, N., Abrams, Z. B., Foraker, R., Lai, A. M., Payne, P. R. O., & Gupta, A. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad120 · Cited by 171 · see citing works

Ethics of Artificial Intelligence in Medicine and Ophthalmology

Abdullah, Y. I., Schuman, J. S., Shabsigh, R., Caplan, A. L., & Al‐Aswad, L. A. (2021) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000397 · Cited by 159 · see citing works

Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar

Li, Y., Sperrin, M., Ashcroft, D. M., & van Staa, T. P. (2020) · BMJ · doi:10.1136/bmj.m3919 · Cited by 141 · see citing works

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

Kumar, S., Oh, I. Y., Schindler, S. E., Lai, A. M., Payne, P., & Gupta, A. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab052 · Cited by 125 · see citing works

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23020828 · Cited by 120 · see citing works

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114 · see citing works

Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers

Rahimi, A. K., Pienaar, O., Ghadimi, M., Canfell, O. J., Pole, J. D., Shrapnel, S., van der Vegt, A. H., & Sullivan, C. M. (2024) · Journal of Medical Internet Research · doi:10.2196/49655 · Cited by 131 · see citing works

Randomized clinical trials of artificial intelligence

Angus, D. C. (2020) · JAMA · doi:10.1001/jama.2020.1039 · Cited by 112 · see citing works

Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey

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 105 · see citing works

Artificial intelligence in cardiology

Itchhaporia, D. (2020) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2020.11.007 · Cited by 98 · see citing works

Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review

Choudhury, A., Renjilian, E., & Asan, O. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa034 · Cited by 91 · see citing works

Environmentally sustainable development and use of artificial intelligence in health care

Richie, C. (2022) · Bioethics · doi:10.1111/bioe.13018 · Cited by 92 · see citing works

Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?

Schreuder, A., Scholten, E. T., van Ginneken, B., & Jacobs, C. (2021) · Translational Lung Cancer Research · doi:10.21037/tlcr-2020-lcs-06 · Cited by 91 · see citing works

The Science of Learning Health Systems: Scoping Review of Empirical Research

Ellis, L. A., Sarkies, M., Churruca, K., Dammery, G., Meulenbroeks, I., Smith, C. L., Pomare, C., Mahmoud, Z., Zurynski, Y. A., & Braithwaite, J. (2022) · JMIR Medical Informatics · doi:10.2196/34907 · Cited by 91 · see citing works

How Clinicians Perceive Artificial Intelligence–Assisted Technologies in Diagnostic Decision Making: Mixed Methods Approach

Hah, H., & Goldin, D. S. (2021) · Journal of Medical Internet Research · doi:10.2196/33540 · Cited by 81 · see citing works

Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses

Pantanowitz, L., Hartman, D. J., Yan, Q., Cho, E. Y., Suh, B., Paeng, K., Dhir, R., Michelow, P. M., Hazelhurst, S., Song, S. Y., et al. (2020) · Diagnostic Pathology · doi:10.1186/s13000-020-00995-z · Cited by 81 · see citing works

The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set

Ambagtsheer, R. C., Shafiabady, N., Dent, E., Seiboth, C., & Beilby, J. P. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104094 · Cited by 79 · see citing works

Predictive Modeling for Perinatal Mortality in Resource-Limited Settings

Shukla, V. V., Eggleston, B. S., Ambalavanan, N., McClure, E. M., Mwenechanya, M., Chomba, E., Bose, C. L., Bauserman, M., Tshefu, A. K., Goudar, S. S., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.26750 · Cited by 72 · see citing works

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms

Park, Y. H., Kim, S. H., & Choi, Y. Y. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18168613 · Cited by 72 · see citing works

Parental Attitudes toward Artificial Intelligence-Driven Precision Medicine Technologies in Pediatric Healthcare

Sisk, B. A., Antes, A. L., Burrous, S., & DuBois, J. M. (2020) · Children · doi:10.3390/children7090145 · Cited by 71 · see citing works

Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Electronics · doi:10.3390/electronics12071558 · Cited by 75 · see citing works

Artificial intelligence and robot nurses: from nurse managers' perspective: a descriptive cross‐sectional study

Ergin, E., Karaarslan, D., Şahan, S., & Çınar Yücel, Ş. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13646 · Cited by 70 · see citing works

Best practices in the real-world data life cycle

Zhang, J., Symons, J., Agapow, P., Teo, J., Paxton, C. A., Abdi, J., Mattie, H., Davie, C., Torres, A. Z., Folarin, A., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000003 · Cited by 68 · see citing works

Yapay Zeka ve Sağlıkta Yapay Zekanın Kullanımına Yönelik Sağlık Bilimleri Fakültesi Öğrencilerinin Görüşleri

Yılmaz, Y., Yılmaz, D. U., Yıldırım, D., Korhan, E. A., & Kaya, D. Ö. (2021) · Süleyman Demirel Üniversitesi Sağlık Bilimleri Dergisi · doi:10.22312/sdusbed.950372 · Cited by 68 · see citing works

Psychiatry in the Digital Age: A Blessing or a Curse?

Roth, C. B., Papassotiropoulos, A., Brühl, A. B., Lang, U. E., & Huber, C. G. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18168302 · Cited by 71 · see citing works

The impact of artificial intelligence on remote healthcare: Enhancing patient engagement, connectivity, and overcoming challenges

Chaturvedi, U., Chauhan, S. B., & Singh, I. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.12.003 · Cited by 96 · see citing works

High Hopes for “Deep Medicine”? AI, Economics, and the Future of Care

Sparrow, R., & Hatherley, J. J. (2020) · The Hastings Center Report · doi:10.1002/hast.1079 · Cited by 66 · see citing works

Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

Shaw, J., Ali, J., Atuire, C., Cheah, P. Y., Español, A. G., Gichoya, J. W., Hunt, A., Jjingo, D., Littler, K., Paolotti, D., et al. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01044-w · Cited by 63 · see citing works

Transforming healthcare ecosystems through blockchain: Opportunities and capabilities for business process innovation

Aloini, D., Benevento, E., Stefanini, A., & Zerbino, P. (2022) · Technovation · doi:10.1016/j.technovation.2022.102557 · Cited by 65 · see citing works

An improved explainable artificial intelligence tool in healthcare for hospital recommendation

Wang, Y.-C., Chen, T., & Chiu, M.-C. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100147 · Cited by 61 · see citing works

The Future of Digital Psychiatry

Hariman, K. W., Ventriglio, A., & Bhugra, D. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1074-4 · Cited by 62 · see citing works

The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Nuzzi, R., Boscia, G., Marolo, P., & Ricardi, F. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.710329 · Cited by 60 · see citing works

Reform and innovation of artificial intelligence technology for information service in university physical education

Zhang, J. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189372 · Cited by 58 · see citing works

Artificial intelligence tools utilized in nursing education: incidence and associated factors

Jallad, S. T., Alsaqer, K., Albadareen, B. I., & Al-maghaireh, D. (2024) · Nurse Education Today · doi:10.1016/j.nedt.2024.106355 · Cited by 58 · see citing works

A Medical Image Visualization Technique Assisted with AI-Based Haptic Feedback for Robotic Surgery and Healthcare

Minopoulos, G. M., Memos, V. A., Stergiou, K. D., Stergiou, C. L., & Psannis, K. E. (2023) · Applied Sciences · doi:10.3390/app13063592 · Cited by 55 · see citing works

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

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 54 · see citing works

Computational Audiology: New Approaches to Advance Hearing Health Care in the Digital Age

Wasmann, J.-W. A., Lanting, C., Huinck, W. J., Mylanus, E. A. M., van der Laak, J. A. W. M., Govaerts, P., Swanepoel, D. W., Moore, D. R., & Barbour, D. L. (2021) · Ear and Hearing · doi:10.1097/aud.0000000000001041 · Cited by 53 · see citing works

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 55 · see citing works

Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review

Sedaño, R., Solitano, V., Vuyyuru, S. K., Yuan, Y., Hanžel, J., Ma, C., Nardone, O. M., & Jairath, V. (2025) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848251321915 · Cited by 57 · see citing works

The application and use of artificial intelligence in cancer nursing: A systematic review

O'Connor, S., Vercell, A., Wong, D., Yorke, J., Fallatah, F. A., Cave, L., & Anny Chen, L.-Y. (2024) · European Journal of Oncology Nursing · doi:10.1016/j.ejon.2024.102510 · Cited by 51 · see citing works

Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Baselli, G., Codari, M., & Sardanelli, F. (2020) · European Radiology Experimental · doi:10.1186/s41747-020-00159-0 · Cited by 51 · see citing works

Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts

Starke, G., Gille, F., Termine, A., Aquino, Y. S. J., Chavarriaga, R., Ferrario, A., Hastings, J., Jongsma, K. R., Kellmeyer, P., Kulynych, B., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/56306 · Cited by 55 · see citing works

Developing an Artificial Intelligence Capability: A Theoretical Framework for Business Value

Mikalef, P., Fjørtoft, S. O., & Torvatn, H. Y. (2019) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-030-36691-9_34 · Cited by 72 · see citing works

AiIoMT: IoMT-Based System-Enabled Artificial Intelligence for Enhanced Smart Healthcare Systems

Awotunde, J. B., Folorunso, S. O., Ajagbe, S. A., Garg, J., & Ajamu, G. J. (2022) · Springer eBooks · doi:10.1007/978-3-030-80928-7_10 · Cited by 50 · see citing works

Artificial Intelligence on Call: The Physician's Decision of Whether to Use AI in Clinical Practice

Dai, T., & Singh, S. (2025) · Journal of Marketing Research · doi:10.1177/00222437251332898 · Cited by 50 · see citing works

Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries

Tachkov, K., Zemplenyi, A., Kamusheva, M., Dimitrova, M., Siirtola, P., Pontén, J., Nemeth, B., Kalo, Z., & Petrova, G. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.921226 · Cited by 49 · see citing works

Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer

Li, Z., Kitajima, K., Hirata, K., Togo, R., Takenaka, J., Miyoshi, Y., Kudo, K., Ogawa, T., & Haseyama, M. (2021) · EJNMMI Research · doi:10.1186/s13550-021-00751-4 · Cited by 49 · see citing works

Role of AI physical education based on application of functional sports training

Guo, Q., & Li, B. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189373 · Cited by 46 · see citing works

Augmented decision‐making for acral lentiginous melanoma detection using deep convolutional neural networks

Lee, S. W., Chu, Y., Yoo, S. K., Choi, S. G., Choe, S., Koh, S. B., Chung, K. Y., Xing, L., Oh, B. H., & Yang, S. (2020) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.16185 · Cited by 47 · see citing works

Who's Learning? Using Demographics in EDM Research

Paquette, L., Ocumpaugh, J. L., Li, Z., Andres, A., & Baker, R. S. (2020) · Journal of Educational Data Mining · doi:10.5281/zenodo.4143611 · Cited by 46 · see citing works

Applications of Artificial Intelligence in Dental Medicine: A Critical Review

Sitaras, S., Tsolakis, I. A., Gelsini, M., Tsolakis, A. I., Schwendicke, F., Wolf, T. G., & Perlea, P. (2025) · International Dental Journal · doi:10.1016/j.identj.2024.11.009 · Cited by 42 · see citing works

The Gap Between AI and Bedside: Participatory Workshop on the Barriers to the Integration, Translation, and Adoption of Digital Health Care and AI Startup Technology Into Clinical Practice

Olaye, I. M., & Seixas, A. A. (2023) · Journal of Medical Internet Research · doi:10.2196/32962 · Cited by 42 · see citing works

Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review

Adegboro, C. O., Choudhury, A., Asan, O., & Kelly, M. M. (2021) · Hospital Pediatrics · doi:10.1542/hpeds.2021-006094 · Cited by 41 · see citing works

Understanding challenges of using routinely collected health data to address clinical care gaps: a case study in Alberta, Canada

McGuckin, T., Crick, K., Myroniuk, T. W., Setchell, B., Yeung, R. O., & Campbell-Scherer, D. L. (2022) · BMJ Open Quality · doi:10.1136/bmjoq-2021-001491 · Cited by 41 · see citing works

A systematic approach to enhance the explainability of artificial intelligence in healthcare with application to diagnosis of diabetes

Wang, Y.-C., Chen, T., & Chiu, M.-C. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100183 · Cited by 40 · see citing works

Current status and future direction of digital health in Korea

Shin, S.-Y. (2019) · Korean Journal of Physiology and Pharmacology · doi:10.4196/kjpp.2019.23.5.311 · Cited by 40 · see citing works

Artificial intelligence and the world of work, a co‐constitutive relationship

Østerlund, C. S., Jarrahi, M. H., Willis, M., Boyd, K., & Wolf, C. T. (2020) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24388 · Cited by 40 · see citing works

Forecasting a Crisis: Machine-Learning Models Predict Occurrence of Intraoperative Bradycardia Associated With Hypotension

Solomon, S., Saxena, R. C., Neradilek, M. B., Hau, V., Fong, C. T., Lang, J. D., Posner, K. L., & Nair, B. G. (2020) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000004636 · Cited by 37 · see citing works

Speciesism and Preference of Human–Artificial Intelligence Interaction: A Study on Medical Artificial Intelligence

Huo, W., Zhang, Z., Qu, J., Yan, J., Yan, S., Yan, J., & Shi, B. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2176985 · Cited by 39 · see citing works

Use and Application of Large Language Models for Patient Questions Following Total Knee Arthroplasty

Bains, S. S., Dubin, J. A., Hameed, D., Sax, O. C., Douglas, S., Mont, M. A., Nace, J., & Delanois, R. E. (2024) · The Journal of Arthroplasty · doi:10.1016/j.arth.2024.03.017 · Cited by 36 · see citing works

人工智能醫學應用的前景與風險

Sparrow, R., & Hatherley, J. J. (2019) · International Journal of Chinese & Comparative Philosophy of Medicine · doi:10.24112/ijccpm.171678 · Cited by 35 · see citing works

Prediction of all-cause mortality in haemodialysis patients using a Bayesian network

Siga, M. M., Ducher, M., Florens, N., Roth, H., Mahloul, N., Fouque, D. P., & Fauvel, J. (2020) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfz295 · Cited by 36 · see citing works

Pharmacogenomics of Cisplatin‐Induced Ototoxicity: Successes, Shortcomings, and Future Avenues of Research

Drögemöller, B. I., Wright, G. E. B., Lo, C., Le, T. T., Brooks, B., Bhavsar, A. P., Rassekh, S. R., Ross, C. J., & Carleton, B. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1483 · Cited by 34 · see citing works

A Pattern for Arguing the Assurance of Machine Learning in Medical Diagnosis Systems

Picardi, C., Hawkins, R., Paterson, C., & Habli, I. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26601-1_12 · Cited by 34 · see citing works

The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery

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