Evidence map›Paper›PMID 38161831›Full record

ArticleCureus2023

Empowering Women's Health: A Global Perspective on Artificial Intelligence and Robotics.

Munaza Afaq, Divya E Abraham, Saloni H Patel, Areen D Al-Dhoon, Zara Arshad

Abstract readEditorial
In one paragraph

Article in Cureus, 2023. 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. Article
  2. 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.

Munaza AfaqInternal Medicine, Global Remote Research Scholars Program, Saint Paul, USA.
Divya E AbrahamInternal Medicine, Global Remote Research Scholars Program, Saint Paul, USA.
Saloni H PatelInternal Medicine, Global Remote Research Scholars Program, Saint Paul, USA.
Areen D Al-DhoonNeurology, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, USA.
Zara ArshadInternal Medicine, Global Remote Research Scholars Program, Saint Paul, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In our rapidly evolving world, technology stands at the forefront, driving remarkable advancements across various sectors. One of the most notable changes is the use of Artificial Intelligence (AI) and robotics in healthcare, starting a revolution that has the power to change women's health all over the world. Developed nations are already witnessing the benefits. However, a significant portion of the global population in underdeveloped regions is lagging behind, resulting in a noticeable disparity. This is particularly evident in women's healthcare, an area already facing global inequities. As we witness a digital revolution, we examine the progressive steps taken in women's healthcare. AI and robotics are key to this transformation. The services range from using data to predict cancer trends to tailor-made medicine and technologies in reproduction. This editorial addresses the existing gaps and the digital divide, exploring the necessity for an inclusive approach in technology design and implementation to ensure equitable healthcare access.  Furthermore, it highlights the imperative role of multi-sectoral collaborations to foster innovation while mitigating risks. The clear goal is to build a future where all women, no matter where they live, can get good healthcare, helped by AI and robotics, bringing in a time of healthcare for all. It's crucial for everyone involved to come together to make a healthcare system that everyone can use, helping women everywhere with the help of new technology.

Indexed as

artificial intelligence and robotics in healthcarechallenges to health innovationglobal health and artificial intelligenceglobal health policyhealth-care equityinternational collaboration and developmentmaternal mortalitywomen’s health

Identifiers

PMID38161831
PMCPMC10755639

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.