Artificial Intelligence in Medicine
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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 literature often stops short of critical considerations of the limitations of AI in e-learning, especially regarding adaptability across varied educational contexts.
“Therefore, although the literature has identified the conceptual benefits accruable from AI in e-learning, it often stops short of critical considerations of its limitations, especially regarding the adaptability of AI systems across varied educational contexts [26,27].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [27]
Cites
Systematic Literature Reviews in Software Engineering—A Systematic Literature Review
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537 · see citing works
Computer-Based Medical Consultations: MYCIN.
(1976) · Annals of Internal Medicine · doi:10.7326/0003-4819-85-6-831_1 · Cited by 2,505 · see citing works
Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402 · see citing works
Artificial intelligence in medicine
Hamet, P., & Tremblay, J. (2017) · Metabolism · doi:10.1016/j.metabol.2017.01.011 · Cited by 2,334 · see citing works
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0040-6 · Cited by 1,638 · see citing works
Computing Machinery and Intelligence
Turing, A. M. (2007) · Springer eBooks · doi:10.1007/978-1-4020-6710-5_3 · Cited by 1,439 · see citing works
Elements of a theory of human problem solving.
Newell, A., Shaw, J. C., & Simon, H. A. (1958) · Psychological Review · doi:10.1037/h0048495 · Cited by 1,380 · see citing works
Artificial intelligence in medicine
Ramesh, A., Kambhampati, C., Monson, J. Q., & Drew, P. J. (2004) · Annals of The Royal College of Surgeons of England · doi:10.1308/147870804290 · Cited by 1,008 · see citing works
Han, S. S., Kim, M. S., Lim, W., Park, G., Park, I., & Chang, S. E. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.01.028 · Cited by 715 · see citing works
Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach
Lamy, J.-B., Sekar, B., Guezennec, G., Bouaud, J., & Séroussi, B. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.01.001 · Cited by 282 · see citing works
Ethical Design of Intelligent Assistive Technologies for Dementia: A Descriptive Review
Ienca, M., Wangmo, T., Jotterand, F., Kressig, R. W., & Elger, B. S. (2017) · Science and Engineering Ethics · doi:10.1007/s11948-017-9976-1 · Cited by 209 · see citing works
Automated Interpretation of Blood Culture Gram Stains by Use of a Deep Convolutional Neural Network
Smith, K. P., Kang, A. D., & Kirby, J. E. (2017) · Journal of Clinical Microbiology · doi:10.1128/jcm.01521-17 · Cited by 194 · see citing works
Artificial intelligence in medicine: what is it doing for us today?
Becker, A. (2019) · Health Policy and Technology · doi:10.1016/j.hlpt.2019.03.004 · Cited by 137 · see citing works
Artificial intelligence and deep learning – Radiology's next frontier?
Mayo, R. C., & Leung, J. W. T. (2017) · Clinical Imaging · doi:10.1016/j.clinimag.2017.11.007 · Cited by 128 · see citing works
Yamada, Y., & Kobayashi, M. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.06.005 · Cited by 112 · see citing works
Predicting scheduled hospital attendance with artificial intelligence
Nelson, A. P. K., Herron, D., Rees, G., & Nachev, P. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0103-3 · Cited by 106 · see citing works
Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound
Ko, S. Y., Lee, J. H., Yoon, J. H., Na, H., Hong, E., Han, K., Jung, I., Kim, E., Moon, H. J., Park, V. Y., et al. (2019) · Head & Neck · doi:10.1002/hed.25415 · Cited by 104 · see citing works
Care robots and the future of ICT-mediated elderly care: a response to doom scenarios
Coeckelbergh, M. (2015) · AI & Society · doi:10.1007/s00146-015-0626-3 · Cited by 58 · see citing works
Tariq, M. I., Ahmed, S., Memon, N. A., et al. (2020) · Sensors · doi:10.3390/s20051310 · Cited by 56 · see citing works
Self-citationArtificial Intelligence in Medicine: Weighing the Accomplishments, Hype, and Promise
Shortliffe, E. H. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677891 · Cited by 47 · see citing works
Radiology in 2018: Are You Working with AI or Being Replaced by AI?
Bluemke, D. A. (2018) · Radiology · doi:10.1148/radiol.2018184007 · Cited by 45 · see citing works
Predictive Variables for Agile Development Merging Cloud Computing Services
Butt, S. A., Tariq, M. I., Jamal, T., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2929169 · Cited by 45 · see citing works
Self-citationAgent Based Information Security Framework for Hybrid Cloud Computing
Tariq, M. I. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.01.023 · Cited by 42 · see citing works
Self-citationTariq, M. I., Tayyaba, S., Rasheed, H., et al. (2017) · International Conference on Communication, Computing and Digital Systems (C-CODE) · doi:10.1109/c-code.2017.7918925 · Cited by 40 · see citing works
Self-citationArtificial Intelligence in Medicine
Çakır, S. S., & Ötünçtemur, A. (2018) · European Archives of Medical Research · doi:10.5152/eamr.2018.43534 · Cited by 35 · see citing works
Importance of Big Data in Precision and Personalized Medicine
Naqvi, M. R., Jaffar, M. A., Aslam, M., Shahzad, S. K., Iqbal, M. W., & Farooq, A. (2020) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora49412.2020.9152842 · Cited by 31 · see citing works
Self-citationTowards Information Security Metrics Framework for Cloud Computing
Tariq, M. I. (2012) · International Journal of Cloud Computing and Services Science (IJ-CLOSER) · doi:10.11591/closer.v1i4.1442 · Cited by 27 · see citing works
Self-citationStudy of Block Chain and its Impact on Internet of Health Things (IoHT):Challenges and Opportunities
Naqvi, M. R., Aslam, M., Iqbal, M. W., Shahzad, S. K., Malik, M., & Tahir, M. U. (2020) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora49412.2020.9152846 · Cited by 26 · see citing works
Self-citationAnalysis of ISO 27001:2013 Controls Effectiveness for Cloud Computing
Tariq, M. I., & Santarcangelo, V. (2016) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0005648702010208 · Cited by 21 · see citing works
Self-citationThrough Patients' Eyes: Regulation, Technology, Privacy, and the Future
Petersen, C. C. (2018) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1641193 · Cited by 21 · see citing works
Deep learning techniques for optimizing medical big data
Tariq, M. I., Tayyaba, S., Ashraf, M. W., & Bălaş, V. E. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819061-6.00008-2 · Cited by 21 · see citing works
Self-citationEichhorn, K. W., Westphal, R., Rilk, M. E., et al. (2017) · Acta Oto-Laryngologica · doi:10.1080/00016489.2017.1336284 · Cited by 15 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328 · see citing works
- Authors
- Malik, M., Tariq, M. I., Kamran, M., & Naqvi, M. R.
- Affiliations
- Superior University (PK)
- Institut National Polytechnique de Toulouse (FR)
- Venue
- Smart Innovation, Systems and Technologies
- Publisher
- Springer Singapore
- Published
- 2021
- Pages
- 159-170
- ISSN
- 2190-3018 (ISSN-L)
- 2190-3026
- OpenAlex
- W3206527637
- Cited by
- 11
- References
- 32
- Citations by year
- 2024: 4 · 2023: 1 · 2022: 2 · 2021: 1
- FWCI
- 2.83
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Smart innovation, systems and technologies (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, diagnosis, treatment guidelines, medical data analysis, history of artificial intelligence, AI adoption barriers, human error reduction
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
