Evidence map›Paper›PMID 38146561›Full record

ReviewCureus2023

Application of Artificial Intelligence in the Management of Drinking Water: A Narrative Review.

Revathi G Maroju, Sonali G Choudhari, Mohammed Kamran Shaikh, Sonali K Borkar, Harshal Mendhe

Abstract 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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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.

Revathi G MarojuDepartment of Community Medicine, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research (DU), Nagpur, IND.
Sonali G ChoudhariDepartment of Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (DU), Wardha, IND.
Mohammed Kamran ShaikhDepartment of Community Medicine, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research (DU), Nagpur, IND.
Sonali K BorkarDepartment of Community Medicine, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research (DU), Nagpur, IND.
Harshal MendheDepartment of Community Medicine, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research (DU), Nagpur, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Waterborne illnesses are a significant concern worldwide. The management of water resources can be facilitated by artificial intelligence (AI) with the help of data analytics, regression models, and algorithms. Achieving the Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development of the United Nations depends on understanding, communicating, and measuring the value of water and incorporating it into decision-making. Various barriers are used from the source to the consumer to prevent microbiological contamination of drinking water sources or reduce contamination to levels safe for human health. Infrastructure development and capacity-building policies should be integrated with guidelines on applying AI to problems relating to water to ensure good development outcomes. Communities can live healthily with such technology if they can provide clean, economical, and sustainable water to the ecosystem as a whole. Quick and accurate identification of waterborne pathogens in drinking and recreational water sources is essential for treating and controlling the spread of water-related diseases, especially in resource-constrained situations. To ensure successful development outcomes, policies on infrastructure development and capacity building should be combined with those on applying AI to water-related problems. The primary focus of this study is the use of AI in managing drinking water and preventing waterborne illness.

Indexed as

artificial intelligencemachine learningsafe waterwaterborne diseaseswater management

Identifiers

PMID38146561
PMCPMC10749683

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.