Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review

Overview

Journal JMIR Med Educ

Publisher JMIR Publications

Specialties Biotechnology
General Medicine
Medical Education

Date 2020 Jul 1

PMID 32602844

Citations 67

Authors

A Hasan Sapci

H Aylin Sapci

Affiliations

Soon will be listed here.

Abstract

Background: The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice.

Objective: The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience.

Methods: A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments.

Results: This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula.

Conclusions: To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.

Citing Articles

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Generative Artificial Intelligence in Medical Education-Policies and Training at US Osteopathic Medical Schools: Descriptive Cross-Sectional Survey.

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Savardi M, Signoroni A, Benini S, Vaccher F, Alberti M, Ciolli P Insights Imaging. 2025; 16(1):23.

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Knowledge, Attitude, and Practice (KAP) Regarding the Use of Artificial Intelligence in Hospital Settings in Mardan, Khyber Pakhtunkhwa, Pakistan.

Sadiq F, Sadiq F, Gul R, Zuhra F, Khan M, Shah S Cureus. 2025; 16(12):e75355.

PMID: 39781175 PMC: 11707555. DOI: 10.7759/cureus.75355.

References

Conde J, De S, Hall R, Johansen E, Meglan D, Peng G . Telehealth innovations in health education and training. Telemed J E Health. 2010; 16(1):103-6. PMC: 2937346. DOI: 10.1089/tmj.2009.0152. View

Frize M, Frasson C . Decision-support and intelligent tutoring systems in medical education. Clin Invest Med. 2000; 23(4):266-9. View

Pinto Dos Santos D, Giese D, Brodehl S, Chon S, Staab W, Kleinert R . Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2018; 29(4):1640-1646. DOI: 10.1007/s00330-018-5601-1. View

Alonso-Silverio G, Perez-Escamirosa F, Bruno-Sanchez R, Ortiz-Simon J, Munoz-Guerrero R, Minor-Martinez A . 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: 10.1177/1553350618777045. View

. Artificial intelligence and medical imaging 2018: French Radiology Community white paper. Diagn Interv Imaging. 2018; 99(11):727-742. DOI: 10.1016/j.diii.2018.10.003. View

Chin-Yee B, Upshur R . Clinical judgement in the era of big data and predictive analytics. J Eval Clin Pract. 2017; 24(3):638-645. DOI: 10.1111/jep.12852. View

Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M . Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015; 4:1. PMC: 4320440. DOI: 10.1186/2046-4053-4-1. View

Cabitza F, Rasoini R, Gensini G . Unintended Consequences of Machine Learning in Medicine. JAMA. 2017; 318(6):517-518. DOI: 10.1001/jama.2017.7797. View

Caudell T, Summers K, Holten 4th J, Hakamata T, Mowafi M, Jacobs J . Virtual patient simulator for distributed collaborative medical education. Anat Rec B New Anat. 2003; 270(1):23-9. DOI: 10.1002/ar.b.10007. View

10.

Rimmer A . Technology will improve doctors' relationships with patients, says Topol review. BMJ. 2019; 364:l661. DOI: 10.1136/bmj.l661. View

11.

Kolachalama V, Garg P . Machine learning and medical education. NPJ Digit Med. 2019; 1:54. PMC: 6550167. DOI: 10.1038/s41746-018-0061-1. View

12.

Yang Y, Shulruf B . Expert-led and artificial intelligence (AI) system-assisted tutoring course increase confidence of Chinese medical interns on suturing and ligature skills: prospective pilot study. J Educ Eval Health Prof. 2019; 16:7. PMC: 6517322. DOI: 10.3352/jeehp.2019.16.7. View

13.

Popenici S, Kerr S . Exploring the impact of artificial intelligence on teaching and learning in higher education. Res Pract Technol Enhanc Learn. 2019; 12(1):22. PMC: 6294271. DOI: 10.1186/s41039-017-0062-8. View

14.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

15.

Tang A, Tam R, Cadrin-Chenevert A, Guest W, Chong J, Barfett J . Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. Can Assoc Radiol J. 2018; 69(2):120-135. DOI: 10.1016/j.carj.2018.02.002. View

16.

Wartman S, Combs C . Reimagining Medical Education in the Age of AI. AMA J Ethics. 2019; 21(2):E146-152. DOI: 10.1001/amajethics.2019.146. View

17.

Char D, Shah N, Magnus D . Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018; 378(11):981-983. PMC: 5962261. DOI: 10.1056/NEJMp1714229. View

18.

Petit dit Dariel O, Raby T, Ravaut F, Rothan-Tondeur M . Developing the Serious Games potential in nursing education. Nurse Educ Today. 2013; 33(12):1569-75. DOI: 10.1016/j.nedt.2012.12.014. View

19.

Chary M, Parikh S, Manini A, Boyer E, Radeos M . A Review of Natural Language Processing in Medical Education. West J Emerg Med. 2019; 20(1):78-86. PMC: 6324711. DOI: 10.5811/westjem.2018.11.39725. View

20.

Lynn L . Artificial intelligence systems for complex decision-making in acute care medicine: a review. Patient Saf Surg. 2019; 13:6. PMC: 6357484. DOI: 10.1186/s13037-019-0188-2. View