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Currently submitted to: JMIR Medical Education

Date Submitted: Nov 12, 2023
Date Accepted: Mar 23, 2024

The final, peer-reviewed published version of this preprint can be found here:

Evidence-Based Learning Strategies in Medicine Using AI

Arango-Ibanez JP, Posso-Nunez JA, Díaz-Solórzano JP, Cruz-Suárez GA

Evidence-Based Learning Strategies in Medicine Using AI

JMIR Med Educ 2024;10:e54507

DOI: 10.2196/54507

PMID: 38801706

PMCID: 11144835

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Evidence-based Learning Strategies in Medicine Using Artificial Intelligence

  • Juan Pablo Arango-Ibanez; 
  • José Alejandro Posso-Nunez; 
  • Juan Pablo Díaz-Solórzano; 
  • Gustavo Adolfo Cruz-Suárez

ABSTRACT

Large language models (LLMs) like ChatGPT, are transforming the landscape of medical education. They offer a vast range of applications, such as tutoring (personalized learning), patient simulation, generation of exam questions, and streamlined access to information. The rapid advancement of medical knowledge and the need for personalized learning underscore the relevance and timeliness of exploring innovative strategies for integrating artificial intelligence (AI) into medical education. In this viewpoint article, we propose coupling evidence-based learning strategies such as active recall and memory cues with AI to optimize learning. These strategies include the generation of tests, mnemonics, and visual cues.


 Citation

Please cite as:

Arango-Ibanez JP, Posso-Nunez JA, Díaz-Solórzano JP, Cruz-Suárez GA

Evidence-Based Learning Strategies in Medicine Using AI

JMIR Med Educ 2024;10:e54507

DOI: 10.2196/54507

PMID: 38801706

PMCID: 11144835

Per the author's request the PDF is not available.