Evidence map›Paper›PMID 37872929›Full record

ReviewCureus2023

Evolution of Diagnostic and Forensic Microbiology in the Era of Artificial Intelligence.

Anwita Mishra, Salman Khan, Arghya Das, Bharat C Das

Open access · diamondAbstract readReview
In one paragraph

Review in Cureus, 2023. 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
1.6field-weighted citation impact, top 14% of its field
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, 13 citations in OpenAlex.

  1. Review
  2. Article
  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 at 4 institutions in 2 countries.

Anwita MishraDepartment of Microbiology, Mahamana Pandit Madan Mohan Malviya Cancer Centre and Homi Bhabha Cancer Hospital, Varanasi, IND.
Salman KhanDepartment of Microbiology, National Cancer Institute, Jhajjar, IND.
Arghya DasDepartment of Microbiology, All India Institute of Medical Sciences, Madurai, IND.
Bharat C DasDepartment of Microbiology, All India Institute of Medical Sciences, New Delhi, IND.
All India Institute of Medical Sciences · INCancer Hospital and Research Institute · INMadurai Medical College · INNational Cancer Institute · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diagnostic microbiology plays a vital role in managing infectious diseases, combating antimicrobial resistance, and containment of outbreaks. During the fourth industrial revolution, when artificial intelligence (AI) became an essential part of our day-to-day lives, its integration into healthcare would further revolutionize our knowledge and potential. Although in the budding stage, AI with machine learning is being increasingly utilized in various aspects of diagnostic microbiology. It can handle large datasets that are difficult to analyze manually. Researchers have developed and demonstrated several machine-learning algorithms for interpreting bacterial cultures, conducting image analysis for microbial detection, and predicting antimicrobial susceptibility patterns. Thus, AI may most likely be the ultimate solution to the ever-increasing demand for improved results with shorter turnaround times. AI can also assist forensic microbiologists in crime scene investigations, as it can guide individual identification, cause and time since death, and manner of death. This review summarizes the application of AI in diagnostic microbiology for performing diverse sets of microbial investigations and is an essential aid in forensic microbiology.

Indexed as

antimicrobial susceptibilityartificial intelligencediagnostic microbiologyimage analysismachine learningmicrobial forensicspathogen identification

Identifiers

PMID37872929
PMCPMC10590455
OpenAlexW4386966220

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.