Evidence map›Paper›PMID 41496967›Full record

ArticleAnnals of medicine and surgery (2012)2026

Artificial intelligence in cancer genomics: refining diagnosis of hereditary breast and ovarian cancer syndromes.

Muhammad Khizar, Muhammad Zaib, Meerab Babar, Hasiba Karimi, Hasibullah Aminpoor

Abstract readLetter
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of medicine and surgery (2012), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Muhammad KhizarFaculty of Medicine, Georgian American University, Manifest Medical Research Co., Tbilisi, Georgia.ORCID https://orcid.org/0009-0005-5229-5052
Muhammad ZaibFaculty of Medicine, Georgian American University, Manifest Medical Research Co., Tbilisi, Georgia.
Meerab BabarFaculty of Medicine, Rawalpindi Medical University, Rawalpindi, Pakistan.ORCID https://orcid.org/0009-0002-4741-7631
Hasiba KarimiFaculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey.ORCID https://orcid.org/0000-0002-7618-3849
Hasibullah AminpoorFaculty of Medicine, Kabul University of Medical Sciences "Abu Ali Ibn Sina", Kabul, Afghanistan.ORCID https://orcid.org/0000-0003-1817-7800

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence (AI) into soft tissue sarcoma (STS) diagnostics represents a significant leap toward precision oncology. AI-driven genomic tools, especially in the detection of fusion genes, enhance diagnostic sensitivity, speed, and accuracy, facilitating improved patient stratification and personalized therapy. The convergence of AI with multi-omic data - including transcriptomic, epigenomic, and imaging datasets - offers a comprehensive view of sarcoma biology and supports earlier detection and optimized treatment selection. However, effective implementation requires international collaboration, standardized reporting frameworks, and continuous clinician education. AI holds transformative potential to advance the precision, efficiency, and clinical impact of STS management.

Indexed as

artificial intelligencegenomicsprecision oncologysoft tissue sarcoma

Identifiers

PMID41496967
PMCPMC12768212

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.