Balancing Innovation and Living Experience: The Role and Limits of Modern Technological Approaches to Anatomy Education across Disciplines
Bibliographic record
Abstract
Human anatomy, the study of the human body’s structure, is the foundation of health and medical sciences. Over time, its exploration has expanded beyond medicine, finding applications in diverse fields such as therapy, creative and performing arts, kinesiology, and engineering. Despite the widespread use of anatomical knowledge across diverse disciplines, there is a significant knowledge gap in that these various disciplines do not learn educational methods from one another; there is thus a great (and as-yet unexplored) opportunity for interdisciplinary learning and innovation. This narrative review explores the trending methods of instruction in anatomy education across disciplines, focusing on the application, successes, and limitations of modern technological tools – including 3D visualization systems, virtual and augmented reality (VR & AR), advanced imaging modalities, and 3D printing. This review also critically examines how state-of-the-art technology commonly overshadows living anatomy, where our own bodies are the original instructional tool of anatomical education. By integrating insights from multiple disciplines, this narrative review offers insight on how living anatomy can complement the innovative technological advancements, and why it would manifest as a balanced, holistic approach to teaching and learning human anatomy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".