Abstract :

The Educational Method Of Traditional Medical Training Depends Highly Upon The Use Of Physical Mannequins Which Replicate Human Physiology And Diagnostic Cases. The Training Benefits From These Models Exist But They Present Major Obstacles Including The Expense Combined With Restricted Availability And Fixed Non-machine Learning Simulation Capabilities That Do Not Display Live Biological Response Changes. Digital Twins Have Emerged Into Practice Through The Need For Training Solutions Which Offer Scalable Benefits Alongside Lower Costs Alongside Interactive Features. The Integration Of Digital Twin Technology Into Healthcare Training Presents Itself As A Modern Practical Approach Instead Of Using Conventional Mannequins For Education. Student Interaction With Realistic Clinical Scenarios Spanning Multiple Medical Problems Becomes Possible In Digital Twin Systems Because They Operate Through Ongoing Real-time Data Updates. Through Remote Access Surgeon Trainees Can Overcome Distance Limitations As Well As Financial Restrictions To Obtain Their Medical Education. The Initial Investment In AI, VR, And AR May Seem High, But Digital Twins Offer Huge Long-term Benefits. They Need Less Maintenance, Can Be Easily Updated, And Provide More Diverse Training Options Without Constant Replacements. Plus, They Boost Engagement And Retention By Offering Immersive, Real-time Learning Experiences. This Paper Concludes How Digital Twins Could Complement, Not Replace, Traditional Medical Training Methods.

Published:

21-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

2800 - 2803

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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