Potential of 3D printing in development of foods for special medical purpose: A review
Bibliographic record
Abstract
Nutritional management has emerged as an effective strategy to mitigate the risks of malnutrition and disease-related mortality among patients. The emergence of novel food types, particularly foods for special medical purposes (FSMPs), has garnered increasing attention from researchers and businesses. 3D printing (3DP) technology, alternatively known as food additive manufacturing, has gained popularity among novel food developers due to its distinct capabilities in tailoring nutrition, appearance, texture, and enhancing overall edible quality. This review examines current market trends, product forms, and unique characteristics of FSMPs, highlighting the progress made in applying 3DP to the development of functional foods and drugs. Despite its potential medical benefits, there are limited instances of direct utilization of 3DP in the production of such specialized food type. Currently, the FSMP market faces several challenges, including limited product diversity, inadequate formula design, and a lack of product appeal. 3DP offers significant advantages in catering to the unique needs of special patients, encompassing both physiological medical benefits and enhanced sensory as well as psychological eating experiences. It holds great promise in promoting precision medicine and personalized home-based FSMPs preparations. This review will delve into the development strategies and feasibility of 3DP in creating specialized medical food for patients with unique conditions and across different age groups. Additionally, it explores the potential challenges of applying 3DP to the FSMP sector, such as regulatory frameworks, patient acceptance, cost of 3D-printed FSMPs, and the improvement of 3DP.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".