Evidence map›Paper›PMID 41822841›Full record

ReviewFood science of animal resources2025

A Comprehensive Review of Artificial Intelligence (AI)-Driven Approaches to Meat Quality and Safety.

Young-Hwa Hwang, Abdul Samad, Ayesha Muazzam, Amm Nurul Alam, Seon-Tea Joo

Abstract readReview
In one paragraph

Review in Food science of animal resources, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
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

5 authors.

Young-Hwa HwangInstitute of Agriculture & Life Science, Gyeongsang National University, Jinju 52828, Korea.ORCID https://orcid.org/0000-0003-3687-3535
Abdul SamadDivision of Applied Life Science (BK 21 Four), Gyeongsang National University, Jinju 52828, Korea.ORCID https://orcid.org/0000-0002-4724-3363
Ayesha MuazzamDivision of Applied Life Science (BK 21 Four), Gyeongsang National University, Jinju 52828, Korea.ORCID https://orcid.org/0000-0002-5155-6629
Amm Nurul AlamDivision of Applied Life Science (BK 21 Four), Gyeongsang National University, Jinju 52828, Korea.ORCID https://orcid.org/0000-0003-3153-3718
Seon-Tea JooInstitute of Agriculture & Life Science, Gyeongsang National University, Jinju 52828, Korea.ORCID https://orcid.org/0000-0002-5483-2828

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessment of meat quality is a fundamental aspect as it is the backbone of the meat industry. The quality of meat influences consumer satisfaction and safety, and is also necessary for competitiveness in the market. Nowadays, consumers know much more about food quality and safety. Moreover, quality and safety are major concerns for consumers. The meat industry is looking for alternatives to evaluate meat quality rather than traditional methods, as conventional methods are less efficient and time-consuming for evaluating the quality. The development of artificial intelligence (AI) technologies provides promising solutions to transform current techniques in quality evaluation. Currently, several sophisticated AI technologies are being developed for quality analysis, improving the precision and efficiency of meat quality examination. The AI systems are being used to examine color attributes as well as textures and microbial load to generate precise information that will assist producers in achieving ideal freshness and safety standards. AI-based technologies support predictive models that help stakeholders recognize supply chain issues in meat science while they remain easier to manage. This review conducts a comprehensive examination of AI systems used for meat quality evaluation. Furthermore, this review investigates the essential contribution of AI toward food safety improvements while explaining multiple techniques that can be utilized to determine expiration time. Multiple real-world scenarios demonstrate field implementations, and the advantages and disadvantages of AI-driven approaches in the meat science sector are discussed in this paper. Furthermore, this review also incorporates future predictions.

Indexed as

artificial intelligencefood safetyfuture predictionsmeat quality assessmentpredictive models

Identifiers

PMID41822841
PMCPMC12965230

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.