Evidence map›Paper›PMID 40553332›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

AI-Assisted Cell Culture System.

Semim Akhtar Ahmed, Abhipsha Saikia, Shalini G Devi, Jagat C Borah

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 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

4 authors.

Semim Akhtar AhmedChemical Biology Lab-I, Institute of Advanced Study in Science and Technology (IASST), Paschim Boragaon, Guwahati, Assam, India.
Abhipsha SaikiaChemical Biology Lab-I, Institute of Advanced Study in Science and Technology (IASST), Paschim Boragaon, Guwahati, Assam, India.
Shalini G DeviChemical Biology Lab-I, Institute of Advanced Study in Science and Technology (IASST), Paschim Boragaon, Guwahati, Assam, India.
Jagat C BorahChemical Biology Lab-I, Institute of Advanced Study in Science and Technology (IASST), Paschim Boragaon, Guwahati, Assam, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) proves to be a great tool that makes it possible for machines to learn from experience, regulate to new inputs, and execute human-like activities. AI is already the prime driver of emerging technologies and there are further possibilities in different fields. Certainly, the technology has not yet reached its full effectiveness in many fields but there is one area where AI, integrated with automation, can greatly improve laboratory research known as cell culture. Cell culture is an essential component of present-day biomedical research, allowing researchers to study cellular activities and underlying mechanisms of different diseases in a controlled laboratory setup and environment. Conventional cell culture procedures are hands-on procedures that are time engrossing, susceptible to error and variability, making it difficult to conduct large-scale investigations or screen large numbers of probable drug candidates. Application of AI and automated cell culture procedures can improve the reproducibility of the cell culture protocols that leads to less variability, better conclusions, and finally improving in-depth investigation and drug development. AI algorithms examine data from sensors and cameras to detect alterations in cell behavior, such as changes in appearance, morphology, or growth rate, and make adaptations to the culture conditions in real-time to optimize the growth and health of the cells. Robotic systems automate cell culture tasks, while AI algorithms monitor cell behavior and make adjustments to the culture conditions in real time. AI is considered a significant game changer in 3D cell culture as researchers can grow spheroids or organoids from different cell sources simultaneously, which will ensure consistency and reproducibility. Further, researchers and scientists can create customized workflows and protocols that follow the natural flow of the cell culture process without the need for modularization and human intervention through the use of AI. Though the AI application to cell culture system offers significant change to the scientists, the laboratories, and the companies, there are different aspects of AI applications that need to be considered through vast exploration.

Indexed as

Artificial IntelligenceCell Culture TechniquesAdaptation, PhysiologicalAutomation, LaboratoryBiomedical ResearchCell Growth ProcessesCell ShapeReproducibility of ResultsTime FactorsAlgorithmsArtificial intelligenceBiomedical researchCell culture

Identifiers

PMID40553332

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.