Evidence map›Paper›PMID 42226986›Full record

ReviewArchives of Razi Institute2025

The Use and Importance ofArtificial Intelligence in Vaccine Research, Development, and Production.

Naeem Muhammad Yasir, Selamoglu Batuhan, Danebekovna Koshanova Gulnazira, Selamoglu Mesut, Selamoglu Zeliha

Abstract readReview
In one paragraph

Review in Archives of Razi Institute, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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.

Naeem Muhammad YasirDepartment of Agronomy, Animals, Food, Natural Resources and the Environment (DAFNAE), University of Padova, Padova, Italy.
Selamoglu BatuhanInstitute of Science, Electrical and Electronic Engineering, Mersin University, Mersin, Turkey.
Danebekovna Koshanova GulnaziraDepartment of Mathematics, Faculty of Sciences, Central Campus, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkestan, Kazakhstan.
Selamoglu MesutBahce Vocational School, Osmaniye Korkut Ata University, Management and Organization, Osmaniye, Turkey.
Selamoglu ZelihaDepartment of Medical Biology, Faculty of Medicine, Nigde Omer Halisdemir University, Nigde, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) refers to a variety of computing approaches, including machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision. AI has transformed healthcare, with applications ranging from diagnostics to personalized medicine, drug development, and clinical trial optimization. The advancement of vaccine creation, research, and manufacturing is being significantly impacted by AI. The integration of AI into vaccine research, development, and production has the potential to revolutionize traditional methodologies, significantly accelerating the process of bringing vaccines to market. This review aims to evaluate the role of AI technologies-such as ML, DL, and NLP-in identifying vaccine targets, optimizing formulations, and streamlining manufacturing processes. AI facilitates the analysis of extensive datasets, enabling predictive analytics that enhance the selection of promising vaccine candidates and improve trial outcomes. Furthermore, AI-driven optimization of supply chains enhances vaccine distribution, particularly in low-resource settings, addressing global disparities in access to immunizations. Despite these advancements, challenges remain, including ethical concerns related to data privacy, algorithmic bias, and the integration of AI into existing frameworks. Future directions point toward advancements in AI technologies, including quantum computing, which could further enhance vaccine development efficiency. Collaboration between AI experts and vaccine researchers is crucial for maximizing the potential of AI and ensuring equitable access to vaccines globally. The vaccination distribution may be optimized by AI-powered logistics systems, guaranteeing that doses are given to the appropriate places at the appropriate times. AI can identify the most effective ways to deliver vaccines, minimizing delays and reducing waste, by analyzing data on transportation routes, storage capacity, and cold chain needs. This review highlights the transformative impact of AI on the vaccine development landscape and underscores its importance in responding to emerging infectious diseases and public health crises.

Indexed as

Artificial IntelligenceVaccine DevelopmentVaccinesData AnalyticsHumansMachine LearningVaccinesBiopharmaceuticalsComputational biologyImmune-informaticsMachine learning (ML)Predictive analytics

Identifiers

PMID42226986
PMCPMC13222437

What OpenQuestion holds

Textmetadata
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.