Evidence map›Paper›PMID 39553101›Full record

ReviewCureus2024

The Potential of Artificial Intelligence in Unveiling Healthcare's Future.

Mousumi Khanam, Sume Akther, Iffath Mizan, Fakhrul Islam, Samsul Chowdhury, Nayla Mehereen Ahsan, Deepa Barua, Sk K Hasan

Abstract readReview
In one paragraph

Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
  7. 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

8 authors.

Mousumi KhanamInternal Medicine, Dhaka Medical College, Dhaka, BGD.
Sume AktherInternal Medicine, Institute of Applied Health Sciences, Chattogram, BGD.
Iffath MizanMedicine, Shaheed Suhrawardy Medical College, Dhaka, BGD.
Fakhrul IslamInternal Medicine, Sylhet Mohammad Ataul Gani Osmani Medical College, Sylhet, BGD.
Samsul ChowdhuryInternal Medicine, Icahn School of Medicine at Mount Sinai (Queens), New York City, USA.
Nayla Mehereen AhsanInternal Medicine, Bangladesh Medical College, Dhaka, BGD.
Deepa BaruaInternal Medicine, Khulna Medical College, Khulna, BGD.
Sk K HasanMechanical and Manufacturing Engineering, Miami University, Oxford, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article examines the transformative potential of artificial intelligence (AI) in shaping the future of healthcare. It highlights AI's capacity to revolutionize various medical fields, including diagnostics, personalized treatment, drug discovery, telemedicine, and patient care management. Key areas explored include AI's roles in cancer screening, reproductive health, cardiology, outpatient care, laboratory diagnosis, language translation, neuroscience, robotic surgery, radiology, personal healthcare, patient engagement, AI-assisted rehabilitation with exoskeleton robots, and administrative efficiency. The article also addresses challenges to AI adoption, such as privacy concerns, ethical issues, cost barriers, and decision-making authority in patient care. By overcoming these challenges and building trust, AI is positioned to become a critical driver in advancing healthcare, improving outcomes, and meeting the future needs of patients and providers.

Indexed as

ai-assisted diagnosticsartificial intelligence (ai) in healthcareethical challenges in ai healthcare adoptionpredictive analytics in medicinerehabilitation robotics

Identifiers

PMID39553101
PMCPMC11566355

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.