Evidence map›Paper›PMID 41497157›Full record

ArticleAnnals of medicine and surgery (2012)2026

Artificial intelligence in genomics: improving detection of fusion genes in soft tissue sarcomas.

Muhammad Khizar, Muhammad Zaib, Savera Ejaz, Hasibullah Aminpoor, Hasiba Karimi

Abstract readLetter
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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.ORCID https://orcid.org/0009-0008-7675-7161
Savera EjazMedicine Department, Shaheed Mohtarma Benazir Bhutto Medical College Karachi, Karachi, Pakistan.ORCID https://orcid.org/0009-0009-4789-7154
Hasibullah AminpoorFaculty of Medicine, Kabul University of Medical Sciences "Abu Ali Ibn Sina", Kabul, Afghanistan.ORCID https://orcid.org/0000-0003-1817-7800
Hasiba KarimiFaculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey.ORCID https://orcid.org/0000-0002-7618-3849

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence (AI) into clinical workflows for soft tissue sarcomas (STS) marks a major step forward in precision oncology. As diagnostic complexity grows, AI-driven genomics - particularly in detecting fusion genes - enhances sensitivity, speed, and accuracy, improving patient stratification and guiding targeted therapies. The convergence of AI with multi-omic datasets, including transcriptomic, epigenomic, and imaging data, offers a holistic view of sarcoma biology, enabling earlier detection and more personalized treatment. Achieving these benefits, however, requires global collaboration, standardized protocols, and continuous clinician education. Ultimately, AI's transformative potential in STS management depends not only on technological innovation but also on the establishment of robust frameworks for its clinical adoption.

Indexed as

artificial intelligencefusion genesgenomicssoft tissue sarcomas

Identifiers

PMID41497157
PMCPMC12767966

What OpenQuestion holds

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Registered trials

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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.