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
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421
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
10 years of health-care reform in China: progress and gaps in Universal Health Coverage
Yip, W. C.-M., Fu, H., Chen, A. T., Zhai, T., Jian, W., Xu, R., Pan, J., Hu, M., Zhou, Z., Chen, Q., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)32136-1 · Cited by 1,263
The ethics of AI in health care: A mapping review
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113172 · Cited by 1,105
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035
Human-computer collaboration for skin cancer recognition
Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0942-0 · Cited by 1,024
Privacy and artificial intelligence: challenges for protecting health information in a new era
Murdoch, B. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00687-3 · Cited by 991
Artificial intelligence and the future of global health
Schwalbe, N., & Wahl, B. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30226-9 · Cited by 956
Swarm Learning for decentralized and confidential clinical machine learning
Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03583-3 · Cited by 884
Markus, A. F., Kors, J. A., & Rijnbeek, P. R. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103655 · Cited by 811
Nadarzynski, T., Miles, O., Cowie, A., & Ridge, D. (2019) · DIGITAL HEALTH · doi:10.1177/2055207619871808 · Cited by 810
AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications
Rashid, A. B., & Kausik, M. A. K. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100277 · Cited by 764
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1037-7 · Cited by 933
Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., & Denniston, A. K. (2020) · BMJ · doi:10.1136/bmj.m3164 · Cited by 702
Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges
Huang, S., Yang, J., Fong, S. J., & Zhao, Q. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.12.007 · Cited by 724
Reinforcement Learning in Healthcare: A Survey
Yu, C., Liu, J., Nemati, S., & Yin, G. (2021) · ACM Computing Surveys · doi:10.1145/3477600 · Cited by 783
Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., et al. (2025) · BMJ · doi:10.1136/bmj-2024-081554 · Cited by 957
“Although it can be argued that the FAIR principles do not cover every aspect of data management because they focus more on findability, accessibility, interoperability, and reusability of the data, and less on privacy and security, they delivered a code of practice that is now widely accepted and applied.”
InfluentialA 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
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
Xu, L., Sanders, L., Li, K., & Chow, J. C. L. (2021) · JMIR Cancer · doi:10.2196/27850 · Cited by 571
Governance of artificial intelligence
Taeihagh, A. (2021) · Policy and Society · doi:10.1080/14494035.2021.1928377 · Cited by 562
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., Beam, A. L., Chan, A.-W., Collins, G. S., Deeks, A. D. J., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30218-1 · Cited by 555
Artificial intelligence in drug development
Zhang, K., Yang, X., Wang, Y., Yu, Y., Huang, N., Li, G. L., Li, X., Wu, J. C., & Yang, S. (2025) · Nature Medicine · doi:10.1038/s41591-024-03434-4 · Cited by 585
Self-citationUse of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives
Esmaeilzadeh, P. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01191-1 · Cited by 507
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., Ashrafian, H., Beam, A. L., Collins, G. S., Darzi, A., Deeks, J. J., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30219-3 · Cited by 486
On the interpretability of artificial intelligence in radiology: challenges and opportunities
Reyes, M., Meier, R., Pereira, S., Silva, C. A., Dahlweid, F.-M., Tengg-Kobligk, H. V., Summers, R. M., & Wiest, R. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020190043 · Cited by 484
Medical 4.0 technologies for healthcare: Features, capabilities, and applications
Haleem, A., Javaid, M., Pratap Singh, R., & Suman, R. (2022) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2022.04.001 · Cited by 468
The future of early cancer detection
Fitzgerald, R. C., Antoniou, A. C., Fruk, L., & Rosenfeld, N. (2022) · Nature Medicine · doi:10.1038/s41591-022-01746-x · Cited by 486
Rivera, S. C., Liu, X., Chan, A.-W., Denniston, A. K., & Calvert, M. J. (2020) · BMJ · doi:10.1136/bmj.m3210 · Cited by 463
Drawbacks of artificial intelligence and their potential solutions in the healthcare sector.
khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., & Abdullah, S. (2023) · Biomedical Materials & Devices · doi:10.1007/s44174-023-00063-2 · Cited by 461
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
Petersson, L., Larsson, I., Nygren, J. M., Nilsen, P., Neher, M., Reed, J. E., Tyskbo, D., & Svedberg, P. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08215-8 · Cited by 434
Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review
Yin, J., Ngiam, K. Y., & Teo, H. H. (2021) · Journal of Medical Internet Research · doi:10.2196/25759 · Cited by 419
RETRACTED ARTICLE: Artificial intelligence and machine learning in precision and genomic medicine
Quazi, S. (2022) · Medical Oncology · doi:10.1007/s12032-022-01711-1 · Cited by 466
RetractedHernandez-Boussard, T., Bozkurt, S., Ioannidis, J. P. A., & Shah, N. H. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa088 · Cited by 410
Patient apprehensions about the use of artificial intelligence in healthcare
Richardson, J. P., Smith, C., Curtis, S., Watson, S., Zhu, X., Barry, B., & Sharp, R. R. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00509-1 · Cited by 424
Shifting machine learning for healthcare from development to deployment and from models to data
Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00898-y · Cited by 420
The Recent Progress and Applications of Digital Technologies in Healthcare: A Review
Senbekov, M., Saliev, T. M., Bukeyeva, Z., Almabayeva, A., Zhanaliyeva, M., Джамбуловна, А. Н., Toishibekov, Y., & Fakhradiyev, I. R. (2020) · International Journal of Telemedicine and Applications · doi:10.1155/2020/8830200 · Cited by 513
Young, A. T., Amara, D., Bhattacharya, A., & Wei, M. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00132-1 · Cited by 400
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
Explainable, trustworthy, and ethical machine learning for healthcare: A survey
Rasheed, K., Qayyum, A., Ghaly, M., Al-Fuqaha, A., Razi, A., & Qadir, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106043 · Cited by 372
A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare
Ahmed, M. I., Spooner, B., Isherwood, J., Lane, M., Orrock, E., & Dennison, A. (2023) · Cureus · doi:10.7759/cureus.46454 · Cited by 364
Precision medicine in the era of artificial intelligence: implications in chronic disease management
Subramanian, M., Wojtusciszyn, A., Favre, L., Boughorbel, S., Shan, J., Letaief, K. B., Pitteloud, N., & Chouchane, L. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02658-5 · Cited by 362
Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
Kotsiopoulos, T., Sarigiannidis, P. G., Ioannidis, D., & Tzovaras, D. K. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2020.100341 · Cited by 371
Zhou, H.-Y., Yu, Y., Wang, C., Zhang, S., Gao, Y., Pan, J., Shao, J., Lu, G., Zhang, K., & Li, W. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01045-x · Cited by 343
Self-citationShaping the future of healthcare: ethical clinical challenges and pathways to trustworthy AI
Goktas, P., & Grzybowski, A. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14051605 · Cited by 336
Strohm, L., Hehakaya, C., Ranschaert, E. R., Boon, W. P. C., & Moors, E. H. M. (2020) · European Radiology · doi:10.1007/s00330-020-06946-y · Cited by 329
Fahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M. (2025) · European Journal of Medical Research · doi:10.1186/s40001-025-03196-w · Cited by 329
Payrovnaziri, S. N., Chen, Z., Rengifo‐Moreno, P., Miller, T., Bian, J., Chen, J. H., Liu, X., & He, Z. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa053 · Cited by 335
Yang, R., & Yu, Y. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.638182 · Cited by 322
Cheung, C. Y.-L., Xu, D., Cheng, C., Sabanayagam, C., Tham, Y. C., Yu, M., Rim, T. H., Chai, C. Y., Gopinath, B., Mitchell, P. L. R., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00626-4 · Cited by 311
Clinical applications of artificial intelligence in robotic surgery
Knudsen, J. E., Ghaffar, U., Ma, R., & Hung, A. J. (2024) · Journal of Robotic Surgery · doi:10.1007/s11701-024-01867-0 · Cited by 320
Laï, M.-C., Brian, M., & Mamzer, M.-F. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-019-02204-y · Cited by 294
Artificial Intelligence and Patient-Centered Decision-Making
Bjerring, J. C., & Busch, J. (2021) · Philosophy & Technology · doi:10.1007/s13347-019-00391-6 · Cited by 274
Artificial intelligence education programs for health care professionals: Scoping review
Charow, R., Jeyakumar, T., Younus, S., Dolatabadi, E., Salhia, M., Al-Mouaswas, D., Anderson, M., Balakumar, S., Clare, M., Dhalla, A., et al. (2021) · JMIR Medical Education · doi:10.2196/31043 · Cited by 291
Presenting machine learning model information to clinical end users with model facts labels
Sendak, M. P., Gao, M., Brajer, N., & Balu, S. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0253-3 · Cited by 279
van de Sande, D., van Genderen, M. E., Huiskens, J., Gommers, D., & van Bommel, J. (2021) · Intensive Care Medicine · doi:10.1007/s00134-021-06446-7 · Cited by 279
Chakraborty, C., Bhattacharya, M., Pal, S., & Lee, S. (2023) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2023.100164 · Cited by 311
Illuminating the dark spaces of healthcare with ambient intelligence
Haque, A., Milstein, A., & Fei-Fei, L. (2020) · Nature · doi:10.1038/s41586-020-2669-y · Cited by 274
Shepherd, D. A., & Majchrzak, A. (2022) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2022.106227 · Cited by 329
Current status and applications of Artificial Intelligence (AI) in medical field: An overview
Haleem, A., Javaid, M., & Khan, I. H. (2019) · Current Medicine Research and Practice · doi:10.1016/j.cmrp.2019.11.005 · Cited by 285
Ullah, A., Anwar, S. M., Li, J., Nadeem, L., Mahmood, T., Rehman, A. U., & Saba, T. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01175-4 · Cited by 333
Wang, Q., Li, Y., & Li, R. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03520-5 · Cited by 280
Chen, M., Zhang, B., Cai, Z., Seery, S., Gonzalez, M. J., Ali, N. M., Ren, R., Qiao, Y., Xue, P., & Jiang, Y. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.990604 · Cited by 258
Artificial Intelligence in Dermatology: A Primer
Young, A. T., Xiong, M., Pfau, J., Keiser, M. J., & Wei, M. L. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.02.026 · Cited by 249
Machine Learning (ML) in Medicine: Review, Applications, and Challenges
Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Naqvi, R. A. (2021) · Mathematics · doi:10.3390/math9222970 · Cited by 289
Jacobs, M., Pradier, M. F., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01224-x · Cited by 247
Harnessing the Power of Clinical Decision Support Systems: Challenges and Opportunities
Chen, Z., Liang, N., Zhang, H., Li, H., Yang, Y., Zong, X., Chen, Y., Wang, Y., & Shi, N. (2023) · Open Heart · doi:10.1136/openhrt-2023-002432 · Cited by 244
Bloem, B. R., Henderson, E. J., Dorsey, E. R., Okun, M. S., Okubadejo, N. U., Chan, P., Andrejack, J., Darweesh, S. K. L., & Munneke, M. (2020) · The Lancet Neurology · doi:10.1016/s1474-4422(20)30064-8 · Cited by 260
Continuous Learning AI in Radiology: Implementation Principles and Early Applications
Pianykh, O. S., Langs, G., Dewey, M., Enzmann, D. R., Herold, C. J., Schoenberg, S. O., & Brink, J. A. (2020) · Radiology · doi:10.1148/radiol.2020200038 · Cited by 237
Teo, Z. L., Jin, L., Liu, N., Li, S., Miao, D., Zhang, X., Ng, W. Y., Tan, T. F., Lee, D. M., Chua, K. J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101419 · Cited by 255
Gama, F., Tyskbo, D., Nygren, J., Barlow, J., Reed, J., & Svedberg, P. (2022) · Journal of Medical Internet Research · doi:10.2196/32215 · Cited by 216
Artificial Intelligence in Medical Sensors for Clinical Decisions
Haick, H., & Tang, N. (2021) · ACS Nano · doi:10.1021/acsnano.1c00085 · Cited by 231
Digital health technologies: opportunities and challenges in rheumatology
Solomon, D. H., & Rudin, R. S. (2020) · Nature Reviews Rheumatology · doi:10.1038/s41584-020-0461-x · Cited by 228
Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, et al. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00350-5 · Cited by 235
Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology
Feeny, A. K., Chung, M. K., Madabhushi, A., Attia, Z. I., Čikeš, M., Firouznia, M., Friedman, P. A., Kalscheur, M. M., Kapa, S., Narayan, S. M., et al. (2020) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.119.007952 · Cited by 215
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
Bharati, S., Mondal, M. R. H., & Podder, P. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3266418 · Cited by 251
Marey, A., Arjmand, P., alerab, A. D. S., Eslami, M. J., Saad, A. M., Sanchez, N., & Umair, M. (2024) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-024-01356-2 · Cited by 243
Predictive analytics in health care: how can we know it works?
Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz130 · Cited by 223
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
Barrett, M., Boyne, J. J., Brandts, J., Rocca, H. B., Maesschalck, L. D., Wit, K. D., Dixon, L., Eurlings, C., Fitzsimons, D., Golubnitschaja, O., et al. (2019) · The EPMA Journal · doi:10.1007/s13167-019-00188-9 · Cited by 221
Guidelines and evaluation of clinical explainable AI in medical image analysis
Jin, W., Li, X., Fatehi, M., & Hamarneh, G. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102684 · Cited by 199
Artificial Intelligence Applications in Health Care Practice: Scoping Review
Sharma, M., Savage, C., Nair, M., Larsson, I., Svedberg, P., & Nygren, J. M. (2022) · Journal of Medical Internet Research · doi:10.2196/40238 · Cited by 198
"The human body is a black box": supporting clinical decision-making with deep learning
Sendak, M., Elish, M. C., Gao, M., Futoma, J., Ratliff, W., Nichols, M., Bedoya, A., Balu, S., & O'Brien, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372827 · Cited by 196
Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence
Bonkhoff, A. K., & Grefkes, C. (2021) · Brain · doi:10.1093/brain/awab439 · Cited by 216
Yang, P., Shi, Y., Li, S., Tao, X., Liu, Z., Wang, X., Wang, Z. L., & Chen, X. (2022) · ACS Nano · doi:10.1021/acsnano.1c11321 · Cited by 197
AlphaFold, Artificial Intelligence (AI), and Allostery
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190
Johnson, M., Jain, R., Brennan-Tonetta, P., Swartz, E. M., Silver, D. E., Paolini, J., Mamonov, S., & Hill, C. (2021) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-021-00272-y · Cited by 239
Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review
Huang, J., Galal, G., Etemadi, M., & Vaidyanathan, M. (2022) · JMIR Medical Informatics · doi:10.2196/36388 · Cited by 186
Cucchi, M., Gruener, C., Petrauskas, L. N., Steiner, P., Tseng, H., Fischer, A., Penkovsky, B., Matthus, C. D., Birkholz, P., Kleemann, H., et al. (2021) · Science Advances · doi:10.1126/sciadv.abh0693 · Cited by 195
Swed, S., Alibrahim, H., Elkalagi, N. K. H., Nasif, M. N., Rais, M. A., Nashwan, A. J., Aljabali, A., Elsayed, M., Sawaf, B., Albuni, M. K., et al. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.1011524 · Cited by 185
McIntosh, C., Conroy, L., Tjong, M. C., Craig, T., Bayley, A., Catton, C., Gospodarowicz, M. K., Helou, J. A., Isfahanian, N., Kong, V., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01359-w · Cited by 196
Pinaya, W. H. L., Vieira, S., Garcia‐Dias, R., & Mechelli, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00011-0 · Cited by 204
Nair, M., Svedberg, P., Larsson, I., & Nygren, J. M. (2024) · PLOS ONE · doi:10.1371/journal.pone.0305949 · Cited by 179
Emerging Applications of Machine Learning in Food Safety
Deng, X., Cao, S., & Horn, A. L. (2021) · Annual Review of Food Science and Technology · doi:10.1146/annurev-food-071720-024112 · Cited by 188
Jacobs, M., He, J., F. Pradier, M., Lam, B., Ahn, A. C., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445385 · Cited by 177
Self-citationArtificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation
Kumar, P., Sharma, S. K., & Dutot, V. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102598 · Cited by 176
Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application
Bellemo, V., Lim, G., Rim, T. H., Tan, G. S. W., Cheung, C. Y.-L., Sadda, S. R., He, M., Tufail, A., Lee, M. L., Hsu, W., et al. (2019) · Current Diabetes Reports · doi:10.1007/s11892-019-1189-3 · Cited by 184
Skin disease diagnosis with deep learning: a review
Li, H., Pan, Y., Zhao, J., & Zhang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.096 · Cited by 178
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
