This paper is in the following e-collection/theme issue:

Natural Language Processing (706) JMIR Theme Issue: COVID-19 Special Issue (2461) Training Tools for Medical Students (132) Artificial Intelligence (1421) Simulation (105) Virtual Patients (52)

Published on in Vol 9, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24073, first published .
A Natural Language Processing–Based Virtual Patient Simulator and Intelligent Tutoring System for the Clinical Diagnostic Process: Simulator Development and Case Study

A Natural Language Processing–Based Virtual Patient Simulator and Intelligent Tutoring System for the Clinical Diagnostic Process: Simulator Development and Case Study

A Natural Language Processing–Based Virtual Patient Simulator and Intelligent Tutoring System for the Clinical Diagnostic Process: Simulator Development and Case Study

Authors of this article:

Raffaello Furlan1, 2 Author Orcid Image ;   Mauro Gatti3 Author Orcid Image ;   Roberto Menè1, 3 Author Orcid Image ;   Dana Shiffer1 Author Orcid Image ;   Chiara Marchiori4 Author Orcid Image ;   Alessandro Giaj Levra1 Author Orcid Image ;   Vincenzo Saturnino3 Author Orcid Image ;   Enrico Brunetta1, 2 Author Orcid Image ;   Franca Dipaola1, 2 Author Orcid Image
Article Authors Cited by (23) Tweetations (3) Metrics

Journals

  1. Rangel-de Lázaro G, Duart J. You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education. Sustainability 2023;15(4):3507 View
  2. Ji T, Yin X, Cheng P, Zhou L, Liu S, Bao W, Lyu C. IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant. International Journal of Environmental Research and Public Health 2022;19(23):15493 View
  3. Liu T, Xiao X. A Framework of AI-Based Approaches to Improving eHealth Literacy and Combating Infodemic. Frontiers in Public Health 2021;9 View
  4. Maicher K, Stiff A, Scholl M, White M, Fosler-Lussier E, Schuler W, Serai P, Sunder V, Forrestal H, Mendella L, Adib M, Bratton C, Lee K, Danforth D. Artificial intelligence in virtual standardized patients: Combining natural language understanding and rule based dialogue management to improve conversational fidelity. Medical Teacher 2023;45(3):279 View
  5. Furlan R, Gatti M, Mene R, Shiffer D, Marchiori C, Giaj Levra A, Saturnino V, Brunetta E, Dipaola F. Learning Analytics Applied to Clinical Diagnostic Reasoning Using a Natural Language Processing–Based Virtual Patient Simulator: Case Study. JMIR Medical Education 2022;8(1):e24372 View
  6. Hassan A, El-Agroudy A, Shehata M, Almoawda M, Atwa H. Adaptations of Clinical Teaching During the COVID-19 Pandemic: Perspectives of Medical Students and Faculty Members. Advances in Medical Education and Practice 2022;Volume 13:883 View
  7. Park H, Shim S, Lee Y. A scoping review on adaptations of clinical education for medical students during COVID-19. Primary Care Diabetes 2021;15(6):958 View
  8. Stamer T, Steinhäuser J, Flägel K. Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review. Journal of Medical Internet Research 2023;25:e43311 View
  9. Zhang W, Cai M, Lee H, Evans R, Zhu C, Ming C. AI in Medical Education: Global situation, effects and challenges. Education and Information Technologies 2024;29(4):4611 View
  10. Stoehr F, Kämpgen B, Müller L, Zufiría L, Junquero V, Merino C, Mildenberger P, Kloeckner R. Natural language processing for automatic evaluation of free-text answers — a feasibility study based on the European Diploma in Radiology examination. Insights into Imaging 2023;14(1) View
  11. Voss M, Geniets A, Winters N. Strategies for Digital Clinical Teaching During the COVID Pandemic: A Scoping Review. Medical Science Educator 2023;34(1):219 View
  12. Lippitsch A, Steglich J, Ludwig C, Kellner J, Hempel L, Stoevesandt D, Thews O. Development and evaluation of a software system for medical students to teach and practice anamnestic interviews with virtual patient avatars. Computer Methods and Programs in Biomedicine 2024;244:107964 View
  13. Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu N, Bartlett R, Hanson J, Haas M, Spadafore M, Grafton-Clarke C, Gasiea R, Michie C, Corral J, Kwan B, Dolmans D, Thammasitboon S. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. Medical Teacher 2024;46(4):446 View
  14. Dipaola F, Gebska M, Gatti M, Levra A, Parker W, Menè R, Lee S, Costantino G, Barsotti E, Shiffer D, Johnston S, Sutton R, Olshansky B, Furlan R. Will Artificial Intelligence Be “Better” Than Humans in the Management of Syncope?. JACC: Advances 2024;3(9):101072 View
  15. Gilligan C, Bujnowska-Fedak M, Essers G, Frerichs W, Brinke D, Junod Perron N, Kiessling C, Pype P, Tsimtsiou Z, Van Nuland M, Wilkinson T, Rosenbaum M. Assessment of communication skills in health professions education; Ottawa 2024 consensus statement. Medical Teacher 2024;46(12):1593 View
  16. du Plooy E, Casteleijn D, Franzsen D. Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon 2024;10(21):e39630 View
  17. Kidwai S, Rojas-Velazquez D, Lopez-Rincon A, Kraneveld A, Oberski D, Meijerman I. Keeping pace in the age of innovation: The perspective of Dutch pharmaceutical science students on the position of machine learning training in an undergraduate curriculum. Currents in Pharmacy Teaching and Learning 2025;17(2):102231 View

Books/Policy Documents

  1. Padilla-Cáceres T, Caballero-Apaza L, Mamani-Cori V, Cervantes-Alagón S. Proceedings of the 7th Brazilian Technology Symposium (BTSym’21). View
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