Evidence map›Paper›PMID 39613923›Full record

ReviewDaru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences2024

Advances in artificial intelligence-based technologies for increasing the quality of medical products.

Nidhi Srivastava, Sneha Verma, Anupama Singh, Pranki Shukla, Yashvardhan Singh, Ankit D Oza, Tanvir Kaur, Sohini Chowdhury, Monit Kapoor, Ajar Nath Yadav

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. 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

10 authors.

Nidhi SrivastavaMaharishi School of Pharmaceutical Sciences, Maharishi University of Information and Technology, Lucknow, Uttar Pradesh, India. nidhimpharm@gmail.com.
Sneha VermaMaharishi School of Science and Humanities, Maharishi University of Information and Technology, Lucknow, Uttar Pradesh, India.
Anupama SinghMaharishi School of Pharmaceutical Sciences, Maharishi University of Information and Technology, Lucknow, Uttar Pradesh, India.
Pranki ShuklaMaharishi School of Pharmaceutical Sciences, Maharishi University of Information and Technology, Lucknow, Uttar Pradesh, India.
Yashvardhan SinghMaharishi School of Pharmaceutical Sciences, Maharishi University of Information and Technology, Lucknow, Uttar Pradesh, India.
Ankit D OzaDepartment of Mechanical Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujarat, India.
Tanvir KaurDepartment of Biotechnology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
Sohini ChowdhuryChitkara Centre for Research and Development, Chitkara University, Baddi, Himachal Pradesh, India.
Monit KapoorCentre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
Ajar Nath YadavDepartment of Genetics, Plant Breeding and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, Sirmour, Himachal Pradesh, India. ajarbiotech@gmail.com.ORCID http://orcid.org/0000-0002-6911-7050

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is a technology that combines machine learning (ML) and deep learning. It has numerous usages in the domains of medicine and other sciences. Artificial intelligence can forecast the behavior of a drug's target protein and predict its desired physicochemical qualities. AI's potential to enhance healthcare services offerings formerly unheard-of opportunities for cost reserves, enhanced overall clinical and patient outcomes. The recent development of research in the biomedical field, encompassing fields such as genomics, computational medicine, AI, and algorithms for learning, has led to the demand for novel technology, a fresh workforce, and new standards of practice set the stage for the revolution in healthcare. By connecting these health statistics with cutting-edge AI technologies, precise insights into patient treatment can be obtained. Moreover, AI can aid in the search for new drugs by foretelling the target protein's two-dimensional structure. In the current review, an overview of the latest AI-based technologies and how they may be employed to reduce product development time to market and snowballing product quality, cost-effectiveness, as well as security throughout the manufacturing process is detailed.

Indexed as

Artificial IntelligenceHumansMachine LearningPharmaceutical PreparationsPharmaceutical PreparationsArtificial intelligenceComputational medicinesDrug developmentHealthcare industryMachine learning

Identifiers

PMID39613923
PMCPMC11607247

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.