Evidence map›Paper›PMID 39238711›Full record

ReviewCureus2024

A Comprehensive Review of Artificial Intelligence in Prostate Cancer Care: State-of-the-Art Diagnostic Tools and Future Outlook.

Somya Agrawal, Sunita Vagha

Abstract readReview
PubMed Full text at PMC Publisher
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 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
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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

2 authors.

Somya AgrawalPathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND.
Sunita VaghaPathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prostate cancer remains a significant global health challenge, characterized by high incidence and substantial morbidity and mortality rates. Early detection is critical for improving patient outcomes, yet current diagnostic methods have limitations in accuracy and reliability. Artificial intelligence (AI) has emerged as a promising tool to address these challenges in prostate cancer care. AI technologies, including machine learning algorithms and advanced imaging techniques, offer potential solutions to enhance diagnostic accuracy, optimize treatment strategies, and personalize patient care. This review explores the current landscape of AI applications in prostate cancer diagnostics, highlighting state-of-the-art tools and their clinical implications. By synthesizing recent advancements and discussing future directions, the review underscores the transformative potential of AI in revolutionizing prostate cancer diagnosis and management. Ultimately, integrating AI into clinical practice can potentially improve outcomes and quality of life for patients affected by prostate cancer.

Indexed as

artificial intelligencediagnostic toolshealthcare innovationmachine learningprecision medicineprostate cancer

Identifiers

PMID39238711
PMCPMC11374581

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.