Title: Integrating artificial intelligence in medical assistant programs: a systematic review

Authors: Manish Das; Dola Sarkar; Purnendu Mandal

Addresses: Department of Nursing, Usha Martin University, Ranchi, Jharkand, India ' Texas Healthtech Institute, Houston, Texas, USA ' Lamar University, College of Business, Beaumont, Texas, USA; Texas Healthtech Institute, Houston, Texas, USA

Abstract: Rapid integration of artificial intelligence (AI) into healthcare settings has created an urgent need for medical assistant training programs to evolve beyond traditional competency frameworks. This systematic review synthesises evidence from 20 peer-reviewed studies published between 2020 and 2026 to evaluate the range, quality, and effectiveness of AI-integrated training tools and educational interventions for medical assistants. A comprehensive search of major academic databases was conducted, covering publications from January 2015 to March 2026. Studies from several countries were selected and appraised using PRISMA 2020 framework and ROBINS-I risk-of-bias tool. Key instruments identified include AI competency self-assessment scale, comprehensive AI competency questionnaire, and AI training scale, all demonstrating satisfactory reliability and validity in their respective contexts. Findings from recent 2024-2026 studies further confirm that faculty unpreparedness, inadequate standardised curricula, and infrastructure inequities remain primary barriers to AI integration. Ethical AI education and longitudinal assessment frameworks remain critically underdeveloped.

Keywords: artificial intelligence in healthcare education; medical assistant training; AI competency assessment; clinical simulation; AI-integrated curriculum; healthcare workforce readiness; PRISMA systematic review.

DOI: 10.1504/IJGAIB.2026.157514

International Journal of Generative Artificial Intelligence in Business, 2026 Vol.1 No.4, pp.436 - 457

Received: 11 May 2026
Accepted: 04 Jun 2026
Published online: 06 Nov 2026 *

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