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

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

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

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

Clinical Implications and Challenges of Artificial Intelligence and Deep Learning

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

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

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

"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

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

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

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 469

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

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

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 523

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

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 404

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

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

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 272

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 248

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

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

Blasiak, A., Khong, J., & Kee, T. W. (2019) · SLAS TECHNOLOGY · doi:10.1177/2472630319890316 · Cited by 234

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 228

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

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

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

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

Javeed, A., Dallora, A. L., Berglund, J. S., Ali, A., Ali, L., & Anderberg, P. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01906-7 · Cited by 211

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

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 166

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 148

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

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

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

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 143

Randomized clinical trials of artificial intelligence

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

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 114

Artificial intelligence in cardiology

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

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 94

Environmentally sustainable development and use of artificial intelligence in health care

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

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 94

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

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

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

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 84

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

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

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 77

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 77

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

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 72

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 79

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 76

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 104

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

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 69

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 69

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 67

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 64

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 67

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

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

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

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 59

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 54

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

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 59

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

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 54

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 62

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

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 54

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 54

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

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

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

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

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 48

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

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

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 47

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

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 43

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 43

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 48

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

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 41

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

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 39

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 37

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

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

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

Rimmer, L. J., Howard, C., Picca, L., & Bashir, M. (2020) · European Journal of Trauma and Emergency Surgery · doi:10.1007/s00068-020-01444-8 · Cited by 33

Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges

Babu, A., Onesimu, J. A., & Sagayam, K. M. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202129701074 · Cited by 33

Patient Data-Sharing for AI: Ethical Challenges, Catholic Solutions

Baric-Parker, J., & Anderson, E. E. (2020) · The Linacre Quarterly · doi:10.1177/0024363920922690 · Cited by 37

Correcting the Brain? The Convergence of Neuroscience, Neurotechnology, Psychiatry, and Artificial Intelligence

Rainey, S., & Erden, Y. J. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-020-00240-2 · Cited by 32

Challenges of Building, Deploying, and Using AI-Enabled Telepsychiatry Platforms for Clinical Practice Among Urban Indians: A Qualitative Study

Thenral, M., & Annamalai, A. (2020) · Indian Journal of Psychological Medicine · doi:10.1177/0253717620973414 · Cited by 30

Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review

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