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Background <p>Artificial intelligence [AI]–driven image recognition is transforming how visual perceptional and interpretational skills are taught and assessed across the continuum of medical education. As AI tools achieve expert-level performance in fields like radiology and pathology, their integration into teaching, assessment, and simulation environments is accelerating.</p> Objective <p>This narrative review aims to synthesize current applications, educational outcomes, challenges, and future perspectives surrounding AI-based image recognition tools in undergraduate, postgraduate, and continuing medical education.</p> Methods <p>A comprehensive literature search was conducted in PubMed, Scopus, and Google Scholar [2010–2025] using terms related to AI, image recognition, and medical education. Studies were thematically analyzed and categorized by educational level, specialty domain, and pedagogical function. A representative subset of key studies was summarized in a supplemental table.</p> Results <p>AI image recognition supports medical learning through tools such as content-based image retrieval systems, annotated slide viewers, surgical video segmentation, and dermatologic lesion classifiers. These applications enhance the delivery of instructions, facilitate real-time feedback, and offer bias-free objective assessment frameworks. Educational outcomes include improved diagnostic accuracy, shortened learning curves, and increased learner confidence. Real-world implementations and pilot programs further illustrate the feasibility and scalability of AI-enhanced instruction. However, challenges persist, including algorithmic bias, hallucinated outputs, over-reliance by learners, ethical concerns about data privacy, and the need for faculty training.</p> Conclusions <p>AI image recognition holds substantial promise as a catalyst for more personalized, adaptive, and competency-based medical education. However, its implementation must be deliberate, reflexive, and ethically grounded. Future educators will increasingly serve as facilitators of AI-augmented learning environments, ensuring that technological innovation complements, rather than displaces, the humanistic and critical dimensions of medical training.</p>

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Artificial intelligence driven image recognition in medical education: current applications, educational impact, challenges, and future directions

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Sarantopoulos Andreas,
  • Zoe Dorothea Pana,
  • Andreas Larentzakis,
  • Sophia Kondylis,
  • Anna Maina,
  • Nikolaos Ziogas,
  • Dimitrios Ntourakis

摘要

Background

Artificial intelligence [AI]–driven image recognition is transforming how visual perceptional and interpretational skills are taught and assessed across the continuum of medical education. As AI tools achieve expert-level performance in fields like radiology and pathology, their integration into teaching, assessment, and simulation environments is accelerating.

Objective

This narrative review aims to synthesize current applications, educational outcomes, challenges, and future perspectives surrounding AI-based image recognition tools in undergraduate, postgraduate, and continuing medical education.

Methods

A comprehensive literature search was conducted in PubMed, Scopus, and Google Scholar [2010–2025] using terms related to AI, image recognition, and medical education. Studies were thematically analyzed and categorized by educational level, specialty domain, and pedagogical function. A representative subset of key studies was summarized in a supplemental table.

Results

AI image recognition supports medical learning through tools such as content-based image retrieval systems, annotated slide viewers, surgical video segmentation, and dermatologic lesion classifiers. These applications enhance the delivery of instructions, facilitate real-time feedback, and offer bias-free objective assessment frameworks. Educational outcomes include improved diagnostic accuracy, shortened learning curves, and increased learner confidence. Real-world implementations and pilot programs further illustrate the feasibility and scalability of AI-enhanced instruction. However, challenges persist, including algorithmic bias, hallucinated outputs, over-reliance by learners, ethical concerns about data privacy, and the need for faculty training.

Conclusions

AI image recognition holds substantial promise as a catalyst for more personalized, adaptive, and competency-based medical education. However, its implementation must be deliberate, reflexive, and ethically grounded. Future educators will increasingly serve as facilitators of AI-augmented learning environments, ensuring that technological innovation complements, rather than displaces, the humanistic and critical dimensions of medical training.