AI-Assisted Medical Education: Implications for Postgraduate Aesthetic Training

Commentary

Dr. Ahmed Haq¹

Journal: HSI J Aesthet Med AI

DOI: 10.XXXXX/hsij.2025.04

Volume / Issue: 1 / 1

Pages: 42–51

Received: 2025-09-01

Accepted: 2025-10-18

Published: 2025-12-01

Licence: CC BY 4.0

Abstract

Background.: Large language models and adaptive learning systems are reshaping postgraduate medical education, including in aesthetic medicine.
Methods.: We review current evidence on AI in postgraduate education and reflect on their integration within an accredited aesthetic training pathway.
Results.: AI-assisted formative assessment, simulation and case-based learning show promise but require human curation to ensure clinical accuracy.
Conclusion.: AI should augment, not replace, supervised clinical learning. Disclosure and pedagogical oversight remain essential.

Keywords: medical education, artificial intelligence, postgraduate training, aesthetic medicine, adaptive learning

The proliferation of generative AI tools has prompted urgent reflection on how postgraduate aesthetic education should evolve.

AI is being deployed for formative quizzing, case-based reasoning, simulation feedback and standardised photo assessment.

Hallucination, opaque reasoning and the potential erosion of clinical reasoning skills are credible concerns requiring active mitigation.

Used with disclosure and curation, AI is a powerful educational adjunct in aesthetic training.

AI Disclosure

Large language model assistance was used during manuscript drafting. The named human author retains full responsibility.

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