Evidence map›Paper›PMID 39732679›Full record

ReviewBMC medical education2024

Exploring prospects, hurdles, and road ahead for generative artificial intelligence in orthopedic education and training.

Nikhil Gupta, Kavin Khatri, Yogender Malik, Amit Lakhani, Abhinav Kanwal, Sameer Aggarwal, Anshul Dahuja

Abstract readReview
In one paragraph

Review in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  17. Innovative models for Orthopaedic collaboration: Training and education.Journal of clinical orthopaedics and trauma · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Nikhil GuptaDepartment of Pharmacology, All India Institute of Medical Sciences, Bathinda, Punjab, 151001, India.
Kavin KhatriDepartment of Orthopedics, Postgraduate Institute of Medical Education and Research (PGIMER) Satellite Centre, Sangrur, Punjab, 148001, India. kavinkhatri84@gmail.com.
Yogender MalikDepartment of Forensic Medicine and Toxicology, Bhagat Phool Singh Govt Medical College for Women, Khanpur Kalan, Sonepat, Haryana, 131305, India.
Amit LakhaniDepartment of Orthopedics, Dr B.R. Ambedkar State Institute of Medical Sciences, Mohali, Punjab, 160055, India.
Abhinav KanwalDepartment of Pharmacology, All India Institute of Medical Sciences, Bathinda, Punjab, 151001, India. abhinavkanwal@gmail.com.
Sameer AggarwalDepartment of Orthopedics, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, 160012, India.
Anshul DahujaDepartment of Orthopedics, Guru Gobind Singh Medical College and Hospital, Faridkot, Punjab, 151203, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory. A review of recent literature was conducted to evaluate the current applications, identify potential benefits, and outline limitations of integrating generative AI in orthopedic education. Key findings indicate that generative AI holds substantial promise in enhancing orthopedic training through its various applications such as providing real-time explanations, adaptive learning materials tailored to individual student's specific needs, and immersive virtual simulations. However, despite its potential, the integration of generative AI into orthopedic education faces significant issues such as accuracy, bias, inconsistent outputs, ethical and regulatory concerns and the critical need for human oversight. Although generative AI models such as ChatGPT and others have shown impressive capabilities, their current performance on orthopedic exams remains suboptimal, highlighting the need for further development to match the complexity of clinical reasoning and knowledge application. Future research should focus on addressing these challenges through ongoing research, optimizing generative AI models for medical content, exploring best practices for ethical AI usage, curriculum integration and evaluating the long-term impact of these technologies on learning outcomes. By expanding AI's knowledge base, refining its ability to interpret clinical images, and ensuring reliable, unbiased outputs, generative AI holds the potential to revolutionize orthopedic education. This work aims to provides a framework for incorporating generative AI into orthopedic curricula to create a more effective, engaging, and adaptive learning environment for future orthopedic practitioners.

Indexed as

Artificial IntelligenceEducation, MedicalOrthopedicsCurriculumHumansMachine LearningArtificial intelligenceMachine learningMedical educationOrthopedics

Identifiers

PMID39732679
PMCPMC11681633

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.