Putting undergraduate medical students in AI-CDSS designers’ shoes: An innovative teaching method to develop digital health critical thinking
Open accessAbstract
INTRODUCTION: Digital health programs are urgently needed to accelerate the adoption of Artificial Intelligence and Clinical Decision Support Systems (AI-CDSS) in clinical settings. However, such programs are still lacking for undergraduate medical students, and new approaches are required to prepare them for the arrival of new and unknown technologies. At University Paris Cité medical school, we designed an innovative program to develop the digital health critical thinking of undergraduate medical students that consisted of putting medical students in AI-CDSS designers' shoes. METHODS: We followed the six steps of Kern's approach for curriculum development: identification of needs, definition of objectives, design of an educational strategy, implementation, development of an assessment and design of program evaluation. RESULTS: A stand-alone and elective AI-CDSS program was implemented for fourth-year medical students. Each session was designed from an AI-CDSS designer viewpoint, with theoretical and practical teaching and brainstorming time on a project that consisted of designing an AI-CDSS in small groups. From 2021 to 2022, 15 students were enrolled: they rated the program 4.4/5, and 80% recommended it. Seventy-four percent considered that they had acquired new skills useful for clinical practice, and 66% felt more confident with technologies. The AI-CDSS program aroused great enthusiasm and strong engagement of students: 8 designed an AI-CDSS and wrote two scientific 5-page articles presented at the Medical Informatics Europe conference; 4 students were involved in a CDSS research project; 2 students asked for a hospital internship in digital health; and 1 decided to pursue PhD training. DISCUSSION: Putting students in AI-CDSS designers' shoes seemed to be a fruitful and innovative strategy to develop digital health skills and critical thinking toward AI technologies. We expect that such programs could help future doctors work in rapidly evolving digitalized environments and position themselves as key leaders in digital health.
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?
Educators at Université Paris Cité have developed an elective course to foster critical thinking in digital health.
“Educators at Université Paris Cité have developed an elective course to foster critical thinking in digital health [171].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [171]
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
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
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 · see citing works
An overview of clinical decision support systems: Benefits, risks, and strategies for success
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0221-y · Cited by 3,019 · see citing works
Types of Unintended Consequences Related to Computerized Provider Order Entry
Campbell, E. M., Sittig, D. F., Ash, J. S., Guappone, K. P., & Dykstra, R. H. (2006) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2042 · Cited by 823 · see citing works
Medical Education Must Move From the Information Age to the Age of Artificial Intelligence
Wartman, S. A., & Combs, C. D. (2018) · Academic Medicine · doi:10.1097/acm.0000000000002044 · Cited by 585 · see citing works
Kwan, J. L., Lo, L., Ferguson, J., Goldberg, H. R., Díaz-Martínez, J. P., TOMLINSON, G. A., Grimshaw, J., & Shojania, K. G. (2020) · BMJ · doi:10.1136/bmj.m3216 · Cited by 471 · see citing works
How the nursing profession should adapt for a digital future
Booth, R. G., Strudwick, G., McBride, S. G., O’Connor, S., & López, A. L. S. (2021) · BMJ · doi:10.1136/bmj.n1190 · Cited by 336 · see citing works
Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2021) · JMIR Nursing · doi:10.2196/23933 · Cited by 326 · 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
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 · see citing works
Perceptions of Digital Health Education Among European Medical Students: Mixed Methods Survey
Machleid, F., Kaczmarczyk, R., Johann, D., Balčiūnas, J., Atienza-Carbonell, B., von Maltzahn, F., & Mosch, L. (2020) · Journal of Medical Internet Research · doi:10.2196/19827 · Cited by 190 · see citing works
Digital health competencies for primary healthcare professionals: A scoping review
Jiménez, G., Spinazze, P., Matchar, D. B., Huat, G. K. C., van der Kleij, R., Chavannes, N. H., & Car, J. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104260 · Cited by 166 · see citing works
Clinical requirements of future patient monitoring in the intensive care unit: qualitative study
Poncette, A.-S., Spies, C., Mosch, L., Schieler, M., Weber-Carstens, S., Krampe, H., & Balzer, F. (2019) · JMIR Medical Informatics · doi:10.2196/13064 · Cited by 136 · see citing works
Aungst, T. D., & Patel, R. (2020) · Journal of Medical Education and Curricular Development · doi:10.1177/2382120519901275 · Cited by 135 · see citing works
Digital health competencies in medical school education: a scoping review and Delphi method study
Khurana, M. P., Raaschou-Pedersen, D. E., Kurtzhals, J., Bardram, J. E., Ostrowski, S. R., & Bundgaard, J. S. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03163-7 · Cited by 129 · see citing works
Tsopra, R., Fernández, X. M., Luchinat, C., Alberghina, L., Lehrach, H. R., Vanoni, M., Dreher, F., Sezerman, O. U., Marc Cuggia, A., de Tayrac, M., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01634-3 · Cited by 117 · see citing works
Self-citationPoncette, A.-S., Glauert, D. L., Mosch, L. K., Braune, K., Balzer, F., & Back, D. A. (2020) · Journal of Medical Internet Research · doi:10.2196/22161 · Cited by 107 · see citing works
Digital health training programs for medical students: scoping review
Tudor Car, L., Kyaw, B. M., Nannan Panday, R. S., van der Kleij, R., Chavannes, N., Majeed, A., & Car, J. (2021) · JMIR Medical Education · doi:10.2196/28275 · Cited by 106 · see citing works
Application of Kern's Six-step approach to curriculum development by global health residents
Sweet, L. R., & Palazzi, D. (2015) · Education for Health · doi:10.4103/1357-6283.170124 · Cited by 104 · see citing works
Ash, J. S., Sittig, D. F., Guappone, K. P., Dykstra, R. H., Richardson, J., Wright, A., Carpenter, J., McMullen, C., Shapiro, M., Bunce, A., et al. (2012) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-12-6 · Cited by 103 · see citing works
Simulated Electronic Health Record (Sim-EHR) Curriculum
Milano, C. E., Hardman, J. A., Plesiu, A., Rdesinski, R. E., & Biagioli, F. E. (2014) · Academic Medicine · doi:10.1097/acm.0000000000000149 · Cited by 83 · see citing works
AntibioGame®: A serious game for teaching medical students about antibiotic use
Tsopra, R., Courtine, M., Sedki, K., Eap, D., Cabal, M., Cohen, S., Bouchaud, O., Mechai, F., & Lamy, J.-B. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104074 · Cited by 77 · see citing works
Self-citationRajaram, A., Hickey, Z., Patel, N. R., Newbigging, J., & Wolfrom, B. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz178 · Cited by 67 · see citing works
Health informatics: a required skill for 21st century clinicians
Fridsma, D. B. (2018) · BMJ · doi:10.1136/bmj.k3043 · Cited by 59 · see citing works
Digital competence – A Key Competence for Todays and Future Physicians
Foadi, N., & Varghese, J. (2022) · Journal of European CME · doi:10.1080/21614083.2021.2015200 · Cited by 54 · see citing works
Tsopra, R., Jaïs, J., Alain Venot, A., & Duclos, C. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002042 · Cited by 53 · see citing works
Self-citationMedical student training in eHealth: scoping review
Echelard, J.-F., Méthot, F., Nguyen, H.-A., & Pomey, M.-P. (2020) · JMIR Medical Education · doi:10.2196/20027 · Cited by 51 · see citing works
Why We Needn’t Fear the Machines: Opportunities for Medicine in a Machine Learning World
Li, D., Kulasegaram, K., & Hodges, B. D. (2019) · Academic Medicine · doi:10.1097/acm.0000000000002661 · Cited by 50 · see citing works
Health informatics in UK Medical Education: an online survey of current practice
Walpole, S. C., Taylor, P., & Banerjee, A. (2016) · JRSM Open · doi:10.1177/2054270416682674 · Cited by 40 · see citing works
Gibson, K. E., & Silverberg, M. (2000) · PubMed · Cited by 36 · see citing works
Lee, W. W., Alkureishi, M. A., Wroblewski, K., Farnan, J. M., & Arora, V. M. (2017) · Medical Education Online · doi:10.1080/10872981.2017.1396171 · Cited by 34 · see citing works
Medical informatics: reasoning methods
Long, W. J. (2001) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(01)00076-8 · Cited by 33 · see citing works
Computing for Medicine: Can We Prepare Medical Students for the Future?
Law, M., Veinot, P. L., Campbell, J., Craig, M., & Mylopoulos, M. (2018) · Academic Medicine · doi:10.1097/acm.0000000000002521 · Cited by 33 · see citing works
The UCLA Health Resident Informaticist Program – A Novel Clinical Informatics Training Program
Singer, J. S., Cheng, E. M., Baldwin, K., & Pfeffer, M. A. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw174 · Cited by 31 · see citing works
Wang, J. J., Singh, R. K., Miselis, H. H., & Stapleton, S. (2022) · JMIR Medical Education · doi:10.2196/32183 · Cited by 28 · see citing works
Tsopra, R., Peckham, D. G., Beirne, P., Rodger, K., Callister, M. E. J., White, H., Jaïs, J., Ghosh, D., Whitaker, P., Clifton, I., et al. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.03.015 · Cited by 26 · see citing works
Self-citationAgher, D., Sedki, K., Desprès, S., Albinet, J.-P., Jaulent, M., & Tsopra, R. (2022) · Journal of Medical Internet Research · doi:10.2196/25384 · Cited by 26 · see citing works
Self-citationTsopra, R., Lamy, J.-B., & Sedki, K. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.04.013 · Cited by 25 · see citing works
Self-citationEvaluating the AMIA-OHSU 10x10 program to train healthcare professionals in medical informatics.
Feldman, S. S., & Hersh, W. (2008) · PubMed Central · Cited by 22 · see citing works
Use of Diagnostic Decision Support Systems in Medical Education
McGowan, J. J., & Berner, E. S. (2010) · Methods of Information in Medicine · doi:10.3414/me9309 · Cited by 22 · see citing works
Foadi, N., Koop, C., Mikuteit, M., Paulmann, V., Steffens, S., & Behrends, M. (2021) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205211028347 · Cited by 22 · see citing works
Behrends, M., Paulmann, V., Koop, C., Foadi, N., Mikuteit, M., & Steffens, S. (2021) · Studies in health technology and informatics · doi:10.3233/shti210307 · Cited by 21 · see citing works
Lessons learned from piloting mHealth informatics practice curriculum into a medical elective
Fernando, J., & Lindley, J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx076 · Cited by 20 · see citing works
Sapci, A. H., & Sapci, H. A. (2017) · Applied Clinical Informatics · doi:10.4338/aci-2017-08-ra-0136 · Cited by 20 · see citing works
Masters, K. (2014) · Medical Teacher · doi:10.3109/0142159x.2014.916783 · Cited by 17 · see citing works
Tsopra, R., Sedki, K., Courtine, M., Hector Falcoff, C., Beco, A. D., Madar, R., Mechai, F., & Lamy, J.-B. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz057 · Cited by 17 · see citing works
Self-citationBurgun, A., Darmoni, S. J., Duff, F. L., & Weber, J. (2005) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2005.07.014 · Cited by 13 · see citing works
Self-citationVitiello, E. M., Kane, M., Hutto, A., & Hall, A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa160 · Cited by 13 · see citing works
Using standardized patients to evaluate medical students’ evidence‐based medicine skills
Amini, R., Hernandez, N. C., Keim, S. M., & Gordon, P. (2015) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.12183 · Cited by 10 · see citing works
Madar, R., Ugon, A., Ivanković, D., & Tsopra, R. (2021) · Journal of Medical Internet Research · doi:10.2196/25741 · Cited by 10 · see citing works
Self-citationA COVID-19 Decision Support System for Phone Call Triage, Designed by and for Medical Students
Vrel, J.-P., Oulmane, S., Boukobza, A., Burgun, A., & Tsopra, R. (2021) · Studies in health technology and informatics · doi:10.3233/shti210226 · Cited by 4 · see citing works
Self-citationTien, W. H., Yang, H. K., & Yunn, S. S. (2016) · Educational Research and Reviews · doi:10.5897/err2016.2853 · Cited by 3 · see citing works
Towards a Clinical Decision Support System for Helping Medical Students in Emergency Call Centers
Michot, E., Woo, J., Mouline, L., Sinnappan, C., Boukobza, A., Campeotto, F., Dupic, L., Burgun, A., Vivien, B., & Tsopra, R. (2022) · Studies in health technology and informatics · doi:10.3233/shti220494 · Cited by 3 · see citing works
Self-citationAn Interactive Interface for Displaying Recommendations on Emergency Phone Triage in Pediatrics
Durchon, C., Vanderlan, S., Jegard, A., Saram, H., Falchi, M., Campeotto, F., Dupic, L., Burgun, A., Vivien, B., & Tsopra, R. (2022) · Studies in health technology and informatics · doi:10.3233/shti220495 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
3 of the 57 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- [20][20] [A.G. Pereira, M. Kim, M. Seywerd, B. Nesbitt, M.B. Pitt, Minnesota Epic101 Collaborative. Collaborating for Competency-A Model for Single Electronic Health Record Onboarding for Medical Students Rotating among Separate Health Systems. Appl Clin, Inform. 9 (1) (2018 Jan) 199 -204.](http://refhub.elsevier.com/S1386-5056(22)00294-5/h0100) -
- [26][26] [P.A. Thomas, D.E. Kern, M.T. Hughes, B.Y. Chen, Curriculum development for medical education: A six-step approach [Internet], Johns Hopkins University Press, 2015 [cited 2022 Feb 11]. Available from:.](http://refhub.elsevier.com/S1386-5056(22)00294-5/h0130) -
- [29][29] Berner ES. Clinical Decision Support Systems: Theory and Practice. 3rd ed. 2016 ´ edition. New York, NY: Springer International Publishing AG; 2016. 313 p. -
- [45][45] [L.G. McCoy, S. Nagaraj, F. Morgado, V. Harish, S. Das, L.A. Celi, What do medical students actually need to know about artificial intelligence? npj Digit Med. 3 (1) (2020 Jun 19) 1 -3.](http://refhub.elsevier.com/S1386-5056(22)00294-5/h0225) -
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
A scoping review of artificial intelligence within pharmacy education
Aziz, M. H. A., Rowe, C., Southwood, R., Nogid, A., Berman, S., & Gustafson, K. A. (2023) · American Journal of Pharmaceutical Education · doi:10.1016/j.ajpe.2023.100615 · Cited by 114 · see citing works
Physicians’ required competencies in AI-assisted clinical settings: a systematic review
Schuitmaker, L., Drogt, J., Benders, M., & Jongsma, K. (2025) · British Medical Bulletin · doi:10.1093/bmb/ldae025 · Cited by 54 · see citing works
“Aside from the importance of training physicians in the use of technology in practice and in critical human skills, they must also become conscious users of AI in medical practice [19,23,25,26,37,57].”
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
“…a learner-centered education [166], and combining “experience-based medical education” with AI literacy education [170], or putting them in the role of designers through theoretical and practical teaching with case studies, asking them to design an AI Clinical Decision Support System [171].”
Salido, A., Syarif, I., Sitepu, M. S., Suparjan, Wana, P. R., Taufika, R., & Melisa, R. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101924 · Cited by 48 · see citing works
Applications of Artificial Intelligence in Medical Education: A Systematic Review
Hallquist, E., Gupta, I., Montalbano, M. J., & Loukas, M. (2025) · Cureus · doi:10.7759/cureus.79878 · Cited by 47 · see citing works
Determining Factors for the Development of Critical Thinking in Higher Education
Jaramillo Gómez, D. L., Álvarez Maestre, A. J., Parada Trujillo, A. E., Pérez Fuentes, C. A., Bedoya Ortiz, D. H., & Sanabria Alarcón, R. K. (2025) · Journal of Intelligence · doi:10.3390/jintelligence13060059 · Cited by 50 · see citing works
A multinational study on artificial intelligence adoption: Clinical implementers' perspectives
Marco-Ruiz, L., Hernández, M. Á. T., Ngo, P. D., Makhlysheva, A., Svenning, T. O., Dyb, K., Chomutare, T., Fernández-Llatas, C., Muñoz-Gama, J., & Tayefi, M. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105377 · Cited by 41 · see citing works
Simou, E. (2025) · International Medical Education · doi:10.3390/ime4040050 · Cited by 30 · see citing works
“Empirical studies indicate that involving students in the co-design and critique of AI-enabled decision support systems (AI-CDSS) enhances their analytical rigor, metacognitive awareness, and disposition towards evidence-based inquiry [163].”
Optimization of diagnosis and treatment of hematological diseases via artificial intelligence
Wang, S., Huang, Z., Li, J., Wu, Y., Du, J., & Li, T. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1487234 · Cited by 19 · see citing works
Johns, E., Godet, J., Alkanj, A., Beck, M., Mas, L. D., Gourieux, B., Sauleau, E., & Michel, B. (2023) · European Journal of Hospital Pharmacy · doi:10.1136/ejhpharm-2023-003857 · Cited by 18 · see citing works
“36 Exploring the promising future of AI in clinical pharmacy will require formulating recommendations on this subject to establish a global framework to guide researchers and clinical pharmacists.”
BackgroundMontagna, M., Chiabrando, F., LORENZO, R. D., & Rovere, P. Q. (2024) · JMIR Medical Education · doi:10.2196/55709 · Cited by 12 · see citing works
AI-CDSS Design Guidelines and Practice Verification
He, X., Xi, Z., Ding, H., Liu, Y., & Zhu, H. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2235882 · Cited by 9 · see citing works
“G10 Build up physician-centered partnerships between AI and humans (Amann et al., 2023; Benrimoh et al., 2021; Bezemer et al., 2019; Cartolovni et al., 2022; Chen et al., 2022; Kudina & de Boer, 2021; Matthiesen et al., 2021; Reverberi et al., 2022; Romero-Brufau et al., 2020; Tsopra et al., 2023).”
InfluentialArtificial intelligence, extended reality, and emerging AI–XR integrations in medical education
Tene, T., López, D. F. V., Veloz, M. J. G., Oviedo, B. S. R., & Tene-Fernandez, R. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1740557 · Cited by 9 · see citing works
Budi, S. C., Putri, D. G. P., Puspitasari, K., & Sena, A. R. (2024) · Healthcare Informatics Research · doi:10.4258/hir.2024.30.4.387 · Cited by 8 · see citing works
“They also showed enthusiasm for AI-CDSS programs and increased confidence in using technology [21].”
Woods, L. S., Lyons, K., van der Vegt, A. H., Olsen, Q., Huang, W., Khor, J. S., Xu, N., & Sullivan, C. M. (2026) · BMC Medical Education · doi:10.1186/s12909-026-08969-3 · Cited by 19 · see citing works
“…[62] Time constraints ( n = 7) Scheduling, sequence and time of tasks [37, 46, 50, 52, 61] Not enough time to complete requirements [37, 48, 60] High dropout rate [46] Lack of processes for AI implementation ( n = 4) Limited existing knowledge of AI [42, 48] Unprepared for shift to AI [44,…”
Artificial intelligence in medicine: a position paper by the Italian Society of Internal Medicine
Balsano, C., Cabitza, F., Cicco, S., Gori, M., Malerba, D., Montagna, M., Tarquini, R., Vacca, A., & The Working Group on Artificial Intelligence, D. T. O. T. I. S. O. I. M. (. (2025) · Internal and Emergency Medicine · doi:10.1007/s11739-025-04146-4 · Cited by 6 · see citing works
Sitepu, M. S., Prasojo, L. D., Hermanto, H., Salido, A., Nurhakim, L. I., Setyorini, E., Disnawati, H., & Wiratsongko, B. (2025) · European Journal of Educational Research · doi:10.12973/eu-jer.15.1.305 · Cited by 17 · see citing works
Framework for AI integration into the undergraduate medical curricula: a scoping review
Cheng, D., Chan, E. Y. S., Song, Y., Cui, A., Chang, A., & Wang, W. (2026) · BMC Medical Education · doi:10.1186/s12909-026-08620-1 · Cited by 4 · see citing works
“Research and innovation with AI were reported in 6 articles [26, 27, 32, 35, 38, 46].”
InfluentialFindayani, N. K., Suparta, I. N., & Sariyasa, S. (2023) · Jurnal Pendidikan MIPA · doi:10.23960/jpmipa/v24i3.pp516-527 · Cited by 3 · see citing works
Enhancing security in patient medical information exchange: A qualitative study
Manns, A., Pezziardi, T., Kadlub, N., Burgun, A., Destrez, A., & Tsopra, R. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105841 · Cited by 5 · see citing works
Self-citationТЕХНОЛОГІЇ ШТУЧНОГО ІНТЕЛЕКТУ ЯК СКЛАДОВА ЦИФРОВОЇ КОМПЕТЕНТНОСТІ МАЙБУТНІХ ЛІКАРІВ
Матвієнко, М. М. (2024) · Medicina ta farmacìâ: osvìtnì diskursi. · doi:10.32782/eddiscourses/2024-1-4 · Cited by 3 · see citing works
Shariq, S., Hameed, S., & Butt, A. (2025) · Library Hi Tech · doi:10.1108/lht-12-2023-0637 · Cited by 2 · see citing works
Wang, S., & Yu, L. (2025) · Langenbeck s Archives of Surgery · doi:10.1007/s00423-025-03843-0 · Cited by 2 · see citing works
Increase Reading Comprehension and Critical Thinking through Reading Stories
Marwan, Firdaus, F. M., & Handayani, I. W. (2024) · Jurnal Ilmiah Sekolah Dasar · doi:10.23887/jisd.v8i2.66783 · Cited by 4 · see citing works
“Traditional strategies usually focus on increasing students' knowledge through the latest technology to encourage development of critical thinking, as observed in research (Hubers et al., 2022; O’Reilly et al., 2018; Tseng et al., 2022; Tsopra et al., 2023).”
BackgroundMansour, N., Khoury, F. M.-E., Zaatari, G. S., & Harb, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70779-7_15 · Cited by 1 · see citing works
Clinical Informatics Education to Advance Learning Health Systems: A Scoping Review
Zingg, A., Tovar, L. I., Witte, L., Koym, K., & Godwin, K. M. (2025) · Learning Health Systems · doi:10.1002/lrh2.70050 · Cited by 1 · see citing works
Nyagoga, L. M., Malik, L., Lukoko, H. O., & Liu, M. (2026) · Artificial Intelligence in Education · doi:10.1108/aiie-02-2025-0029 · Cited by 1 · see citing works
Mapping AI Literacy in Medical Education: A Review of Concepts and Teaching Practices
Ma, Y., & Song, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29773-0_21 · Cited by 1 · see citing works
AlSheikh, M. H., Zaini, R. G., Almulhem, M. A., & Ahmad, S. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1871468 · Cited by 1 · see citing works
Odunlami, W. O., Greenfield, G., Li, E., El-Osta, A. A. R., Tsopra, R., Lingner, H., Memarian, E., Hoffman, R., Nessler, K., Jiménez, G., et al. (2023) · doi:10.2196/preprints.50646
Self-citationEnhancing security in data sharing between practitioners: a qualitative study (Preprint)Preprint
Manns, A., Pezziardi, T., Kadlub, N., Burgun, A., Destrez, A., & Tsopra, R. (2023) · doi:10.2196/preprints.54239
Self-citationІННОВАЦІЙНІ МЕТОДИ ВИКЛАДАННЯ В МЕДИЧНІЙ ОСВІТІ УКРАЇНИ: УПРОВАДЖЕННЯ ТА РЕЗУЛЬТАТИ
Герасименко, О., Совірда, О. С., & Тюпа, В. (2024) · Перспективи та інновації науки · doi:10.52058/2786-4952-2024-3(37)-141-154
ЕФЕКТИВНІСТЬ СИМУЛЯЦІЙНОГО НАВЧАННЯ У ФОРМУВАННІ ПРОФЕСІЙНИХ НАВИЧОК МАЙБУТНІХ ХІРУРГІВ В УКРАЇНІ
Повч, О., & Сидоренко, В. (2024) · Перспективи та інновації науки · doi:10.52058/2786-4952-2024-6(40)-347-359
عسيري, م. ب. م., & العيبان, ع. ا. ب. س. (2025) · مجلة كلية التربية بتفهنا الأشراف · doi:10.21608/jfst.2025.437657
Yanagita, Y., Ishizuka, K., Yokokawa, D., & Shikino, K. (2025) · PLoS ONE · doi:10.1371/journal.pone.0332918
Aplicación de la inteligencia artificial en el Internet de las Cosas: un estudio documental
Mieles, G. M., & Navia, M. (2025) · MASKAY · doi:10.24133/maskay.161.4161
Design and Implementation of an AI Tool for Fostering Critical Thinking in Secondary Schools
Kassenkhan, A. M., Abshukirova, A. M., Kalpeyeva, Z. B., & Sholpanbayev, A. T. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.04.129
Yi, L., Xu, Y., & Fang, X. (2026) · International Conference on Intelligent Systems and Computational Networks (ICISCN) · doi:10.1109/iciscn67954.2026.11566283
“Hence, students are required to establish the utilization of critical thinking and problem-solving skills in the provision of patient care in compound medical environments [4].”
La relation humaine doit-elle être au cœur de l’enseignement de la médecine ?
Sarnacki, S. (2026) · Bulletin de l Académie Nationale de Médecine · doi:10.1016/j.banm.2026.06.009
Plett, C., Galuppo, I., Koutsouleris, N., Schulze, T. G., Falkai, P., Horrer, A., & Wiegand, A. (2025) · JMIR Medical Education · doi:10.2196/88317
Application of Artificial Intelligence in Pharmacy Education: A Scoping Review
Watema-Lord, R., & Sanyal, C. (2026) · Journal of Pharmacy Practice · doi:10.1177/08971900261491254
- Authors
- Tsopra, R., Peiffer-Smadja, N., Charlier, C., Campeotto, F., Lemogne, C., Ruszniewski, P., Vivien, B., & Burgun, A.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Inserm (FR)
- Hôpital Necker-Enfants Malades (FR)
- Corresponding author
- Tsopra, Rosy
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 171
- Pages
- 104980
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W4313410647
- Cited by
- 42
- References
- 57
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 10 · 2023: 6
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Medical Informatics (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Engineering › Biomedical Engineering › Biomedical and Engineering Education
- Keywords
- artificial intelligence, clinical decision support systems, digital health, undergraduate medical education, critical thinking, curriculum development, student engagement, medical informatics education
- MeSH terms
- Artificial Intelligence, Curriculum, Education, Medical, Undergraduate, Humans, Shoes, Students, Medical, Thinking
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
