Artificial intelligence in medical education: Are we ready for it?
Open accessAbstract
doi: https://doi.org/10.12669/pjms.36.5.3042 How to cite this:Imran N, Jawaid M. Artificial intelligence in medical education: Are we ready for it? Pak J Med Sci. 2020;36(5):857-859. doi: https://doi.org/10.12669/pjms.36.5.3042 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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
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?
Limited space in already full curriculum was described as a challenge.
“limited space in already full curriculum [176–178]”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [177]
Medical educationists throughout the world are of the view that a reboot of medical education and curriculum shift from "Knowledge acquisition" to emphasis on "Knowledge management and communication" is needed to address the emerging challenges of 21st century.
“Medical educationists throughout the world are of the view that a reboot of medical education and curriculum shift from “Knowledge acquisition” to emphasis on “Knowledge management and communication” is needed to address the emerging challenges of 21st century.39,45”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [39]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Twelve Tips for programmatic assessment
Van Der Vleuten, C. P. M., Schuwirth, L. W. T., Driessen, E. W., Govaerts, M. J. B., & Heeneman, S. (2015) · Medical Teacher · doi:10.3109/0142159x.2014.973388 · Cited by 333 · see citing works
Reimagining Medical Education in the Age of AI
(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.146 · Cited by 263 · see citing works
Flagged first, then by citations
Research works printed but not linked: 16
- [1]Coiera E The fate of medicine in the time of AI Lancet (London, England) 2018 392 10162 2331 2332 30318263
- [2]Nilsson NJ Nilsson NJ Artificial intelligence:a new synthesis Morgan Kaufmann 1998 4 15
- [3]Obermeyer Z Emanuel EJ Predicting the future—big data, machine learning, and clinical medicine N Engl J Med 2016 375 13 1216 1219 27682033 PMC5070532
- [4]Betancur J Commandeur F Motlagh M Sharir T Einstein AJ Bokhari S Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT:a multicenter study JACC Cardiovasc Imaging 2018 11 11 1654 1663 doi:10.1016/j.jcmg.2018.01.020 29550305 PMC6135711 · DOI
- [5]De Fauw J Ledsam JR Romera-Paredes B Nikolov S Tomasev N Blackwell S Clinically applicable deep learning for diagnosis and referral in retinal disease Nature medicine 2018 24 9 1342 1350 doi:10.1038/s41591-018-0107-6 30104768 · DOI · Paper page
- [6]Haenssle HA Fink C Schneiderbauer R Toberer F Buhl T Blum A Man against machine:diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists Ann Oncol 2018 29 8 1836 1842 doi:10.1093/annonc/mdy166 29846502 · DOI · Paper page
- [7]Stravodimos K Katafigiotis I Pournaras C Dimitroulis D Kousournas G Fragkiadis E Combined robot assisted right partial nephrectomy and cholecystectomy with single docking J Robot Surg 2019 13 1 167 169 29600421
- [8]Masters K Artificial intelligence in medical education Med Teach 2019 41 9 976 980 31007106
- [10]Wartman SA Combs CD Medical education must move from the information age to the age of artificial intelligence Acad Med 2018 93 8 1107 1109 29095704
- [11]Caudell T Summers K Holten J Hakamata T Mowafi M Jacobs J Virtual patient simulator for distributed collaborative medical education Anat Rec B New Anat 2003 270 1 23 29 doi:10.1002/ar.b.10007 doi:10.1002/ar.b.10007 12526063 · DOI · Paper page
- [12]Weidenbach M Trochim S Kreutter S Richter C Berlage T Grunst G Intelligent training system integrated in an echocardiography simulator Comput Biol Med 2004 34 5 407 425 doi:10.1016/S0010-4825(03)00084-2 15145712 · DOI
- [13]Latifi S Gierl M Boulais A De Champlain Andre F Using Automated Scoring to Evaluate Written Responses in English and French on a High-Stakes Clinical Competency Examination Eval Health Prof 2016 39 1 100 113 doi:10.1177/016327∋605358 26377072 · DOI
- [14]Kintsch W The potential of latent semantic analysis for machine grading of clinical case summaries J Biomed Inform 2002 35 1 3 https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(02)00004-7 12415721
- [15]Alonso-Silverio G Perez-Escamirosa Fernando Bruno-Sanchez R Ortiz-Simon J Muñoz-Guerrero Roberto Minor-Martinez A Alarcón-Paredes Antonio Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills Surg Innov 2018 25 4 380 388 doi:10.1177/155335061≉045 29809097 · DOI
- [16]Ward J Gordon J Field M Lehmann H Communication and information technology in medical education Lancet 2001 357 9258 792 796 doi:10.1016/S0140-6736(00)04173-8 11253986 · DOI
- [21]Turing AM Computing machinery and intelligence In Parsing the Turing Test 2009 Springer, Dordrecht 23 65
Cited by
Sorted by citations
A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., et al. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314198 · Cited by 543 · see citing works
Artificial Intelligence in Undergraduate Medical Education: A Scoping Review
Lee, J., Wu, A. S., Li, D., & Kulasegaram, K. (. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004291 · Cited by 324 · see citing works
Wood, E. A., Ange, B. L., & Miller, D. D. (2021) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205211024078 · Cited by 261 · see citing works
Bisdas, S., Topriceanu, C.-C., Zakrzewska, Z., Irimia, A.-V., Shakallis, L., Subhash, J., Casapu, M.-M., Leon-Rojas, J., Pinto dos Santos, D., Andrews, D. M., et al. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.795284 · Cited by 232 · see citing works
Grunhut, J., Wyatt, A. T., & Marques, O. (2021) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205211036836 · Cited by 161 · see citing works
Ng, F. Y. C., Thirunavukarasu, A. J., Cheng, H., Tan, T. F., Gutierrez, L., Lan, Y., Ong, J. C. L., Chong, Y. S., Ngiam, K. Y., Ho, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101230 · Cited by 113 · see citing works
Tung, A. Y. Z., & Dong, L. W. (2023) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205231201164 · Cited by 84 · see citing works
Issa, W. B., Shorbagi, A., Al-Sharman, A., Rababa, M., Al-Majeed, K., Radwan, H., Refaat Ahmed, F., Al-Yateem, N., Mottershead, R., Abdelrahim, D. N., et al. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06076-9 · Cited by 78 · see citing works
“To integrate AI in HPE, we suggest that academic institutions embed AI gradually through a collaboration between experts from engineering and medical colleges [31].”
BackgroundJebreen, K., Radwan, E., Kammoun-Rebai, W., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05465-4 · Cited by 63 · see citing works
Artificial intelligence in undergraduate medical education: an updated scoping review
Simoni, J., Urtubia-Fernandez, J., Mengual, E., Simoni, D. A., Royo, M., Egaña-Yin, D., Hertog, O. L. A., López-Ortiz, L., Muñoz-Tomás, A., Santiago-Martínez, P., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08188-2 · Cited by 62 · see citing works
Sharma, V., Saini, U., Pareek, V., Sharma, L., & Kumar, S. (2023) · Scripta Medica · doi:10.5937/scriptamed54-46267 · Cited by 27 · see citing works
Adopting artificial intelligence driven technology in medical education
Abdekhoda, M., & Dehnad, A. (2024) · Interactive Technology and Smart Education · doi:10.1108/itse-12-2023-0240 · Cited by 15 · see citing works
- Authors
- Imran, N., & Jawaid, M.
- Affiliations
- King Edward Medical University (PK)
- Corresponding author
- Imran, Nazish
- Venue
- Pakistan Journal of Medical Sciences
- Publisher
- Pakistan Journal of Medical Sciences
- Published
- 2020-06-26
- Volume
- 36
- Issue
- 5
- Pages
- 857-859
- ISSN
- 1681-715X (ISSN-L)
- 1682-024X
- PMC
- PMC7372685
- OpenAlex
- W3035113595
- Cited by
- 56
- References
- 2
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 20 · 2023: 12 · 2022: 7 · 2021: 7 · 2020: 2
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Pakistan Journal of Medical Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- artificial intelligence, medical education, educational technology
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
