The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
The practical implementation of artificial intelligence technologies in medicine
Open accessAbstract
The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“The increasing availability of digital health data, advances in computational capacity, and progress in algorithm development have accelerated its integration into clinical practice [3].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [3]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“An AI tools' effectiveness in improving clinical outcomes depends on its adoption by front-line clinicians, where “the rubber meets the road.”12”
From Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. (2022) · cites this paper as [12]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“In general, AI tools have shown promise in development, but few have been able to translate into the real-world settings for patient management [4].”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [4]
The cited source says what the claim attributes to it.
In the citing paper
“Beyond developing AI algorithms, productizing them for clinical use is an exceptionally complex task4,13.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [4]
The cited source says what the claim attributes to it.
In the citing paper
“While AI adoption is expected to grow, its actual implementation currently remains limited due to a variety of challenges.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [4]
In the citing paper
“There is a global consensus that AI solutions must be fair and nondiscriminatory and that AI solutions in health care should have a positive impact across all sectors of social and economic life.2,14,15”
From MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care. (2020) · cites this paper as [2]
In the citing paper
“AI-related studies consider the safety and quality of autonomous operations an essential factor affecting the use of AI devices [27].”
From Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020) · cites this paper as [27]
In the citing paper
“This decade can be viewed as a stabilization phase for AI applications in oncology, marked by incremental improvements and specialized uses, predominantly in image analysis5.”
From Large language models and multimodal foundation models for precision oncology (2024) · cites this paper as [5]
In the citing paper
“Researchers in healthcare can use Machine Learning and Artificial Intelligence to create various designs and assess the practicality without endangering the lives of human volunteers [131,132].”
From Medical 4.0 technologies for healthcare: Features, capabilities, and applications (2022) · cites this paper as [131]
In the citing paper
“While technical performance of AI applications is expected to increase continuously, its implementation in clinical radiology practice is rather complex and has so far been slow [6, 12, 13].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [6]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
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
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842
Self-citationArtificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
Goodman, B., & Flaxman, S. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2741 · Cited by 2,131
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
Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775
The Inevitable Application of Big Data to Health Care
Murdoch, T. B., & Detsky, A. S. (2013) · JAMA · doi:10.1001/jama.2013.393 · Cited by 1,676
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
Research works printed but not linked: 2
- Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (The MIT Press, Cambridge, MA, USA, 2016).
- How long it takes the US FDA to approve 510(k) submissions. Emergo https://www.emergobyul.com/resources/research/fda-510k-review-times-research (2013).
Cited by
Sorted by citations
Items 101–200 of 1,004
Singhal, A., Neveditsin, N., Tanveer, H., & Mago, V. (2024) · JMIR Medical Informatics · doi:10.2196/50048 · Cited by 172
A Path for Translation of Machine Learning Products into Healthcare Delivery
(2020) · EMJ Innovations · doi:10.33590/emjinnov/19-00172 · Cited by 172
“21,103,104 Data sources and data transformations also impact model performance across studies.”
BackgroundAdvancing Patient Care: How Artificial Intelligence Is Transforming Healthcare
Poalelungi, D. G., Mușat, C. L., Fulga, A., Neagu, M., Neagu, A., Piraianu, A. I., & Fulga, I. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13081214 · Cited by 191
Artificial Intelligence in U.S. Health Care Delivery
Sahni, N. R., & Carrus, B. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2204673 · Cited by 175
Leveraging electronic health records for data science: common pitfalls and how to avoid them
Sauer, C. M., Chen, L.-C., Hyland, S. L., Girbes, A. R. J., Elbers, P., & Celi, L. A. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00154-6 · Cited by 183
A Survey on Multimodal Data-Driven Smart Healthcare Systems: Approaches and Applications
Cai, Q., Wang, H. H., Li, Z., & Liu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2941419 · Cited by 172
Artificial Intelligence in Health Care: Current Applications and Issues
Park, C.-W., Seo, S. W., Kang, N., Ko, B., Choi, B. W., Park, C. M., Chang, D. K., Kim, H., Kim, H., Lee, H., et al. (2020) · Journal of Korean Medical Science · doi:10.3346/jkms.2020.35.e379 · Cited by 187
Hemming, S., de Zwart, F., Elings, A., Righini, I., & Πετροπούλου, Ά. (2019) · Sensors · doi:10.3390/s19081807 · Cited by 176
Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity
Alami, H., Lehoux, P., Auclair, Y., de Guise, M., Gagnon, M., Shaw, J., Roy, D. A., Fleet, R. P., Ahmed, M. A. A., & Fortin, J. (2020) · Journal of Medical Internet Research · doi:10.2196/17707 · Cited by 197
Kumar, P. S., Dwivedi, Y. K., & Anand, A. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10136-6 · Cited by 189
How Explainability Contributes to Trust in AI
Ferrario, A., & Loi, M. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533202 · Cited by 163
Where Medical Statistics Meets Artificial Intelligence
Hunter, D. J., & Holmes, C. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2212850 · Cited by 169
Looking beyond the hype: applied AI and machine learning in translational medicine
Toh, T. S., Dondelinger, F., & Wang, D. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.027 · Cited by 159
Artificial intelligence in mathematics education: A systematic literature review
Hidayat, R., bin Mohamed, M. Z., Suhaizi, N. N. B., Sabri, N. B. M., bin Mahmud, M. K. H., & Baharuddin, S. N. B. (2022) · International Electronic Journal of Mathematics Education · doi:10.29333/iejme/12132 · Cited by 270
Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers
Solanki, P., Grundy, J., & Hussain, W. (2023) · AI and Ethics · doi:10.1007/s43681-022-00195-z · Cited by 157
“Increase team diversity and AI literacy of AI, ML and data science teams [56-62] 6.”
BackgroundMethodDevelopment of smart aquaculture farm management system using IoT and AI-based surrogate models
Chiu, M., Yan, W., Bhat, S. A., & Huang, N.-F. (2022) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2022.100357 · Cited by 155
Utilization of Self-Diagnosis Health Chatbots in Real-World Settings: Case Study
Fan, X., Chao, D., Zhang, Z., Wang, D., Li, X., & Tian, F. (2020) · Journal of Medical Internet Research · doi:10.2196/19928 · Cited by 164
High-throughput proteomics and AI for cancer biomarker discovery
Xiao, Q., Zhang, F., Xu, L., Yue, L., Kon, O. L., Zhu, Y., & Guo, T. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.113844 · Cited by 153
The digital scribe in clinical practice: a scoping review and research agenda
van Buchem, M. M., Boosman, H., Bauer, M. P., Kant, I. M. J., Cammel, S. A., & Steyerberg, E. W. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00432-5 · Cited by 152
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
Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images
Sangha, V., Nargesi, A. A., Dhingra, L. S., Khunte, A., Mortazavi, B. J., Ribeiro, A. H., Banina, E., Adeola, O., Garg, N., Brandt, C. A., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.062646 · Cited by 150
Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?
Fabrizio, C., Termine, A., Caltagirone, C., & Sancesario, G. M. (2021) · Diagnostics · doi:10.3390/diagnostics11081473 · Cited by 163
Training confounder-free deep learning models for medical applications
Zhao, Q., Adeli, E., & Pohl, K. M. (2020) · Nature Communications · doi:10.1038/s41467-020-19784-9 · Cited by 160
Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective
Sufyan, M., Shokat, Z., & Ashfaq, U. A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107356 · Cited by 170
Rahimi, S. A., Légaré, F., Sharma, G., Archambault, P. M., Zomahoun, H. T. V., Chandavong, S., Rhéault, N., Wong, S. T., Langlois, L., Couturier, Y., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/29839 · Cited by 169
Franco D’Souza, R., Mathew, M., Mishra, V., & Surapaneni, K. M. (2024) · Medical Education Online · doi:10.1080/10872981.2024.2330250 · Cited by 147
Combining DC-GAN with ResNet for blood cell image classification
Ma, L., Shuai, R., Ran, X., Liu, W., & Ye, C. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02163-3 · Cited by 154
Impact of Artificial Intelligence on Interventional Cardiology
Sardar, P., Abbott, J. D., Kundu, A., Aronow, H. D., Granada, J. F., & Giri, J. (2019) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2019.04.048 · Cited by 151
Ran, A. R., Cheung, C. Y.-L., Wang, X., Chen, H., Luo, L., Chan, P. P. M., Wong, M. O. M., Chang, R. T., Mannil, S. S., Young, A. L., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30085-8 · Cited by 146
Morley, J., Murphy, L., Mishra, A. B., Joshi, I., & Karpathakis, K. (2022) · JMIR Formative Research · doi:10.2196/31623 · Cited by 182
Dzobo, K., Adotey, S. K., Thomford, N. E., & Dzobo, W. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0038 · Cited by 157
Machine Learning Algorithm Validation
Maleki, F., Muthukrishnan, N., Ovens, K. L., Reinhold, C., & Forghani, R. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.08.004 · Cited by 162
The integration of AI in nursing: addressing current applications, challenges, and future directions
Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1545420 · Cited by 143
Clinician checklist for assessing suitability of machine learning applications in healthcare
Scott, I. A., Carter, S. M., & Coiera, E. W. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2020-100251 · Cited by 143
Ye, T., Xue, J., He, M., Gu, J., Lin, H., Xu, B., & Cheng, Y. (2019) · Journal of Medical Internet Research · doi:10.2196/14316 · Cited by 141
A skin-like sensor for intelligent Braille recognition
Zhao, X., Hang, C.-Z., Lü, H.-L., Xu, K., Zhang, H., Yang, F., Ma, R., Wang, J., & Zhang, D. W. (2019) · Nano Energy · doi:10.1016/j.nanoen.2019.104346 · Cited by 141
Bernstein, M. H., Atalay, M. K., Dibble, E. H., Maxwell, A. W. P., Karam, A. R., Agarwal, S., Ward, R. C., Healey, T. T., & Baird, G. L. (2023) · European Radiology · doi:10.1007/s00330-023-09747-1 · Cited by 140
The Regulatory Challenge of 3D Bioprinting
Mladenovska, T., Choong, P., Wallace, G. G., & O’Connell, C. D. (2023) · Regenerative Medicine · doi:10.2217/rme-2022-0194 · Cited by 140
Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives
Jin, K., & Ye, J. (2022) · Advances in Ophthalmology Practice and Research · doi:10.1016/j.aopr.2022.100078 · Cited by 151
van de Sande, D., Van Genderen, M. E., Smit, J. M., Huiskens, J., Visser, J. J., Veen, R. E. R., van Unen, E., BA, O. H., Gommers, D., & Bommel, J. V. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100495 · Cited by 135
Mammography with deep learning for breast cancer detection
Wang, L. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1281922 · Cited by 164
Wenderott, K., Krups, J., Zaruchas, F., & Weigl, M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01248-9 · Cited by 161
Artificial intelligence in healthcare: A bibliometric analysis
Jimma, B. L. (2023) · Telematics and Informatics Reports · doi:10.1016/j.teler.2023.100041 · Cited by 133
The Future of Precision Medicine in the Cure of Alzheimer’s Disease
Arafah, A., Khatoon, S., Rasool, I., Khan, A., Rather, M. A., Abujabal, K. A., Faqih, Y. A. H., Rashid, H., Rashid, S. M., Bilal Ahmad, S., et al. (2023) · Biomedicines · doi:10.3390/biomedicines11020335 · Cited by 133
Lee, T. C., Shah, N. U., Haack, A., & Baxter, S. L. (2020) · Informatics · doi:10.3390/informatics7030025 · Cited by 133
Self-citationAlami, H., Lehoux, P., Denis, J., Motulsky, A., Petitgand, C., Savoldelli, M., Rouquet, R., Gagnon, M., Roy, D. A., & Fortin, J. (2020) · Journal of Health Organization and Management · doi:10.1108/jhom-03-2020-0074 · Cited by 156
Crossnohere, N. L., Elsaid, M. I., Paskett, J., Bose‐Brill, S., & Bridges, J. F. P. (2022) · Journal of Medical Internet Research · doi:10.1016/j.jval.2022.04.1285 · Cited by 148
Machine learning and artificial intelligence in haematology
Shouval, R., Fein, J. A., Savani, B. N., Mohty, M., & Nagler, A. (2020) · British Journal of Haematology · doi:10.1111/bjh.16915 · Cited by 133
Dawoodbhoy, F. M., Delaney, J., Cecula, P., Yu, J., Peacock, I., Tan, J. S., & Cox, B. M. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06993 · Cited by 143
Introduction to digital pathology and computer-aided pathology
Nam, S., Chong, Y., Jung, C. K., Kwak, T.-Y., Lee, J. Y., Park, J., Rho, M. J., & Go, H. (2020) · Journal of Pathology and Translational Medicine · doi:10.4132/jptm.2019.12.31 · Cited by 126
Cheng, J., Abel, J. T., Balis, U. J., McClintock, D. S., & Pantanowitz, L. (2020) · American Journal Of Pathology · doi:10.1016/j.ajpath.2020.10.018 · Cited by 133
Gordon, A. C., Alipanah, N., Bos, L. D. J., Dianti, J., Diaz, J. V., Finfer, S. R., Fujii, T., Giamarellos‐Bourboulis, E. J., Goligher, E. C., Gong, M. N., et al. (2024) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202311-2086so · Cited by 130
Maassen, O., Fritsch, S., Palm, J., Deffge, S., Kunze, J., Marx, G., Riedel, M., Schuppert, A., & Bickenbach, J. (2021) · Journal of Medical Internet Research · doi:10.2196/26646 · Cited by 125
Wamba, S. F., & de Queiroz, M. M. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10142-8 · Cited by 138
Contemporary Role and Applications of Artificial Intelligence in Dentistry
Bonny, T., Nassan, W. A., Obaideen, K., Mallahi, M. N. A., Mohammad, Y., & El-Damanhoury, H. M. (2023) · F1000Research · doi:10.12688/f1000research.140204.1 · Cited by 132
A healthy debate: Exploring the views of medical doctors on the ethics of artificial intelligence
Martinho, A., Kroesen, M., & Chorus, C. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102190 · Cited by 124
Artificial Intelligence in Clinical Health Care Applications: Viewpoint
van Hartskamp, M., Consoli, S., Verhaegh, W., Petković, M., & van de Stolpe, A. (2019) · Interactive Journal of Medical Research · doi:10.2196/12100 · Cited by 127
Artificial Intelligence in the Public Sector - a Research Agenda
Wirtz, B. W., Langer, P. F., & Fenner, C. (2021) · International Journal of Public Administration · doi:10.1080/01900692.2021.1947319 · Cited by 163
Synthesis of diagnostic quality cancer pathology images by generative adversarial networks
Levine, A., Peng, J., Farnell, D., Nursey, M., Wang, Y., Naso, J. R., Ren, H., Farahani, H., Chen, C., Chiu, D. S., et al. (2020) · The Journal of Pathology · doi:10.1002/path.5509 · Cited by 128
The role of artificial intelligence in surgical simulation
Park, J. J., Tiefenbach, J., & Demetriades, A. K. (2022) · Frontiers in Medical Technology · doi:10.3389/fmedt.2022.1076755 · Cited by 133
Artificial intelligence and clinical anatomical education: Promises and perils
Lazarus, M. D., Truong, M., Douglas, P., & Selwyn, N. (2024) · Anatomical Sciences Education · doi:10.1002/ase.2221 · Cited by 116
The Potential Impact of Artificial Intelligence on Healthcare Spending
Sahni, N. R., Stein, G., Zemmel, R. W., & Cutler, D. (2023) · National Bureau of Economic Research · doi:10.3386/w30857 · Cited by 116
Challenges of artificial intelligence in medicine and dermatology
Grzybowski, A., Jin, K., & Wu, H. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2023.12.013 · Cited by 115
Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy
Huang, X., Wang, H., She, C., Feng, J., Liu, X., Hu, X., Chen, L., & Tao, Y. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.946915 · Cited by 115
Artificial intelligence and digital health in global eye health: opportunities and challenges
Tan, T. F., Thirunavukarasu, A. J., Jin, L., Lim, J., Poh, S. S. J., Teo, Z. L., Ang, M., Chan, R. V. P., Ong, J., Turner, A. W., et al. (2023) · The Lancet Global Health · doi:10.1016/s2214-109x(23)00323-6 · Cited by 131
Tackling prediction uncertainty in machine learning for healthcare
Chua, M., Kim, D., Choi, J., Lee, N. G., Deshpande, V., Schwab, J., Lev, M. H., Gonzalez, R. G., Gee, M. S., & Do, S. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00988-x · Cited by 114
Aravazhi, P. S., Gunasekaran, P., Benjamin, N. Z. Y., Thai, A., Chandrasekar, K. K., Kolanu, N. D., Prajjwal, P., Tekuru, Y., Villacreses-Brito, L. C., & Inban, P. (2025) · Disease-a-Month · doi:10.1016/j.disamonth.2025.101882 · Cited by 141
Attitudes of Patients and Their Relatives Toward Artificial Intelligence in Neurosurgery
Palmisciano, P., Jamjoom, A. A. B., Taylor, D., Stoyanov, D., & Marcus, H. J. (2020) · World Neurosurgery · doi:10.1016/j.wneu.2020.03.029 · Cited by 113
Chen, Z., Chen, Y., Sun, Y., Tang, L., Zhang, L., Hu, Y., He, M., Li, Z., Cheng, S., Yuan, J., et al. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01932-y · Cited by 122
Fernandez, E. I., Ferreira, A. S., Cecílio, M. H. M., Chéles, D. S., de Souza, R. C. M., Nogueira, M. F. G., & Rocha, J. C. (2020) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-020-01881-9 · Cited by 119
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
Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment
Chen, H., Yang, B.-W., Qian, L., Meng, Y.-S., Bai, X.-H., Hong, X.-W., He, X., Jiang, M.-J., Yuan, F., Du, Q.-W., et al. (2022) · Radiology · doi:10.1148/radiol.211367 · Cited by 110
Dai, N., Lei, I. M., Li, Z., Li, Y., Fang, P., & Zhong, J. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.108041 · Cited by 111
Artificial Intelligence in Nursing Education: Opportunities and Challenges
Glauberman, G., Ito-Fujita, A., Katz, S., & Callahan, J. (2023) · Hawai‘i Journal of Health & Social Welfare · doi:10.62547/yeis2105 · Cited by 114
Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework
van der Vegt, A. H., Scott, I. A., Dermawan, K., Schnetler, R. J., Kalke, V. R., & Lane, P. J. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad088 · Cited by 109
Zhou, Q.-Q., Wang, J., Tang, W., Hu, Z.-C., Xia, Z.-Y., Li, X.-S., Zhang, R., Yin, X., Zhang, B., & Zhang, H. (2020) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0651 · Cited by 109
Establishing Data Provenance for Responsible Artificial Intelligence Systems
Werder, K., Ramesh, B., & Zhang, R. (2022) · ACM Transactions on Management Information Systems · doi:10.1145/3503488 · Cited by 148
The pace of artificial intelligence innovations: Speed, talent, and trial-and-error
Tang, X., Li, X., Ding, Y., Song, M., & Bu, Y. (2020) · Journal of Informetrics · doi:10.1016/j.joi.2020.101094 · Cited by 131
Artificial intelligence in radiology
Filipović-Grčić, L., & Đerke, F. (2019) · Rad Hrvatske akademije znanosti i umjetnosti Medicinske znanosti · doi:10.21857/y26kec3o79 · Cited by 117
Artificial intelligence in health‐care: implications for the job design of healthcare professionals
Tursunbayeva, A., & Renkema, M. (2022) · Asia Pacific Journal of Human Resources · doi:10.1111/1744-7941.12325 · Cited by 128
Amann, J., Vayena, E., Ormond, K. E., Frey, D., Madai, V. I., & Blasimme, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279088 · Cited by 116
Applications of artificial intelligence (AI) in diagnostic radiology: a technography study
Mehrizi, M. H. R., van Ooijen, P. M. A., & Homan, M. (2020) · European Radiology · doi:10.1007/s00330-020-07230-9 · Cited by 107
Tornero-Costa, R., Martínez-Millana, A., Muscat, N. A., Lazëri, L., Traver, V., & Novillo-Ortiz, D. (2023) · JMIR Mental Health · doi:10.2196/42045 · Cited by 133
Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review
de Vries, B. M., Zwezerijnen, G. J. C., Burchell, G. L., van Velden, F. H. P., Menke-van der Houven van Oordt, C. W., & Boellaard, R. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1180773 · Cited by 106
A survey on 5G/6G, AI, and Robotics
Qiao, L., Li, Y., Chen, D., Serikawa, S., Guizani, M., & Lv, Z. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107372 · Cited by 110
Automated Wheat Diseases Classification Framework Using Advanced Machine Learning Technique
Khan, H., Haq, I. U., Munsif, M., Mustaqeem, M., Khan, S. U., & Lee, M. Y. (2022) · Agriculture · doi:10.3390/agriculture12081226 · Cited by 111
Success Factors of Artificial Intelligence Implementation in Healthcare
Wolff, J., Pauling, J., Keck, A., & Baumbach, J. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.594971 · Cited by 104
Shi, Z., Hu, B., Schoepf, U. J., Savage, R. H., Dargis, D. M., Pan, C., Li, X., Ni, Q. Q., Lu, G., & Zhang, L. J. (2020) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6468 · Cited by 110
Arias-Londoño, J. D., Gómez-García, J. A., Moro-Velázquez, L., & Godino-Llorente, J. I. (2020) · IEEE Access · doi:10.1109/access.2020.3044858 · Cited by 114
González-Gonzalo, C., Thee, E. F., Klaver, C. C. W., Lee, A. Y., Schlingemann, R. O., Tufail, A., Verbraak, F., & Sánchez, C. I. (2022) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2021.101034 · Cited by 103
Effect of AI Explanations on Human Perceptions of Patient-Facing AI-Powered Healthcare Systems
Zhang, Z., Genc, Y., Wang, D., Ahsen, M. E., & Fan, X. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01743-6 · Cited by 111
Nenni, M. E., Felice, F. D., Luca, C. D., & Forcina, A. (2024) · Management Review Quarterly · doi:10.1007/s11301-024-00418-z · Cited by 102
Implementation of artificial intelligence in medicine: status analysis and development suggestions
Xiang, Y., Zhao, L., Liu, Z., Wu, X., Chen, J., Long, E., Lin, D., Zhu, Y., Chen, C., Lin, Z., et al. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101780 · Cited by 100
Artificial intelligence for diabetes care: current and future prospects
Sheng, B., Pushpanathan, K., Guan, Z., Lim, Q. H., Lim, Z. W., Yew, S. M. E., Goh, J. H. L., Bee, Y. M., Sabanayagam, C., Sevdalis, N., et al. (2024) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00154-2 · Cited by 106
Next-generation business models for artificial intelligence start-ups in the healthcare industry
Kulkov, I. (2023) · International Journal of Entrepreneurial Behavior & Research · doi:10.1108/ijebr-04-2021-0304 · Cited by 97
Current status and future trends of clinical diagnoses via image-based deep learning
Xu, J., Xue, K., & Zhang, K. (2019) · Theranostics · doi:10.7150/thno.38065 · Cited by 104
Self-citationAdvancements in AI based healthcare techniques with FOCUS ON diagnostic techniques
Kalra, N., Verma, P., & Verma, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108917 · Cited by 95
Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers
Davis, A., Billick, K., Horton, K. D., Jankowski, M., Knoll, P., Marshall, J. E., Paloma, A., Palma, R., & Adams, D. B. (2020) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2020.04.025 · Cited by 94
Tackling COVID-19 Through Responsible AI Innovation: Five Steps in the Right Direction
Leslie, D. (2020) · Harvard Data Science Review · doi:10.1162/99608f92.4bb9d7a7 · Cited by 103
Reinforcement Learning for Precision Oncology
Eckardt, J., Wendt, K., Bornhäuser, M., & Middeke, J. M. (2021) · Cancers · doi:10.3390/cancers13184624 · Cited by 100
Metadata
- Authors
- He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K.
- Affiliations
- First Affiliated Hospital of Guangzhou Medical University (CN)
- University of California San Diego (US)
- Beijing Tongren Hospital (CN)
- Fudan University (CN)
- Corresponding author
- He, Jianxing
- Zhang, Kang
- Venue
- Nature Medicine
- Published
- 2019-01
- Volume
- 25
- Issue
- 1
- Pages
- 30-36
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC6995276
- OpenAlex
- W2906295032
- Semantic Scholar
- d55e70a42579c7895938b6c43736163dbaf55145
- Cited by
- 2,246
- References
- 24
- Citations by year
- 2026: 308 · 2025: 420 · 2024: 395 · 2023: 356 · 2022: 282 · 2021: 251 · 2020: 164 · 2019: 69
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Guangzhou Medical University
- Guangzhou University
- Guangzhou Regenerative Medicine and Health Guangdong Laboratory
- U.S. National Library of Medicine (T15LM011271)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence in healthcare, clinical implementation, clinical workflow, data sharing, data privacy, algorithmic transparency, data standardization, interoperability, patient safety, AI regulation, health policy
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Medicine, Reference Standards, Social Control, Formal, United States
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
